346 results on '"Xiao, Xiaokui"'
Search Results
302. LF-GDPR: A Framework for Estimating Graph Metrics With Local Differential Privacy.
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Ye, Qingqing, Hu, Haibo, Au, Man Ho, Meng, Xiaofeng, and Xiao, Xiaokui
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PRIVACY , *TASK analysis , *TRUST , *ACQUISITION of data , *TANNER graphs - Abstract
Local differential privacy (LDP) is an emerging technique for privacy-preserving data collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be easily applied to real-world graph analysis tasks such as community detection and centrality analysis due to its high implementation complexity and low data utility. In this paper, we address these two issues by presenting LF-GDPR, the first LDP-enabled graph metric estimation framework for graph analysis. It collects two atomic graph metrics—the adjacency bit vector and node degree—from each node locally. LF-GDPR simplifies the job of implementing LDP-related steps (e.g., local perturbation, aggregation and calibration) for a graph metric estimation task by providing either a complete or a parameterized algorithm for each step. To address low data utility of LDP, it optimally allocates privacy budget between the two atomic metrics during data collection. To demonstrate the usage of LF-GDPR, we show use cases on two common graph analysis tasks, namely, clustering coefficient estimation and community detection. The privacy and utility achieved by LF-GDPR are verified through theoretical analysis and extensive experimental results. [ABSTRACT FROM AUTHOR]
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- 2022
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303. ERGCN: Data enhancement-based robust graph convolutional network against adversarial attacks.
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Wu, Tao, Yang, Nan, Chen, Long, Xiao, Xiaokui, Xian, Xingping, Liu, Jun, Qiao, Shaojie, and Cui, Canyixing
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STRUCTURAL reliability , *MACHINE learning , *TRUST - Abstract
With recent advancements, graph neural networks (GNNs) have shown considerable potential for various graph-related tasks, and their applications have gained considerable attention. However, adversarial attacks can significantly degrade the performance of GNNs, hindering their deployment in critical real-world tasks. GNNs must be robust against adversarial attacks, in which imperceptible adversarial perturbations are introduced to induce serious security issues. To achieve this goal, we propose a robust graph convolutional network, ERGCN, for node classification via data enhancement. ERGCN simultaneously utilizes properties from the "data domain" and "model space" as guidance. Based on the feature smoothness assumption, a graph structure enhancement (GSE) mechanism is proposed to improve the structural reliability of input graphs. Moreover, inspired by self-training methods that assign pseudo-labels to unlabeled training samples and use them to optimize the target model iteratively, a reliable node selection metric, model boundary distance (MBD), is defined based on the distance from training samples to model decision boundary. Finally, a self-training-based robust graph convolutional network is proposed for node classification. Extensive experiments on three public datasets demonstrate the superiority of our model over existing state-of-the-art methods. Our study provides a solution for trustworthy graph machine learning systems in adversarial environments. The code is available at https://github.com/star4455/ERGCN. [ABSTRACT FROM AUTHOR]
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- 2022
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304. Differentially private multivariate time series forecasting of aggregated human mobility with deep learning: Input or gradient perturbation?
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Arcolezi, Héber Hwang, Couchot, Jean-François, Renaud, Denis, Al Bouna, Bechara, and Xiao, Xiaokui
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DEEP learning , *TIME series analysis , *RECURRENT neural networks , *FORECASTING - Abstract
This paper investigates the problem of forecasting multivariate aggregated human mobility while preserving the privacy of the individuals concerned. Differential privacy, a state-of-the-art formal notion, has been used as the privacy guarantee in two different and independent steps when training deep learning models. On one hand, we considered gradient perturbation, which uses the differentially private stochastic gradient descent algorithm to guarantee the privacy of each time series sample in the learning stage. On the other hand, we considered input perturbation, which adds differential privacy guarantees in each sample of the series before applying any learning. We compared four state-of-the-art recurrent neural networks: Long Short-Term Memory, Gated Recurrent Unit, and their Bidirectional architectures, i.e., Bidirectional-LSTM and Bidirectional-GRU. Extensive experiments were conducted with a real-world multivariate mobility dataset, which we published openly along with this paper. As shown in the results, differentially private deep learning models trained under gradient or input perturbation achieve nearly the same performance as non-private deep learning models, with loss in performance varying between 0.57 and 2.8 % . The contribution of this paper is significant for those involved in urban planning and decision-making, providing a solution to the human mobility multivariate forecast problem through differentially private deep learning models. [ABSTRACT FROM AUTHOR]
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- 2022
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305. Real-Time Popularity Prediction on Instagram
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Chu, Deming, Shen, Zhitao, Zhang, Yu, Yang, Shiyu, Lin, Xuemin, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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306. A Deep Approach for Multi-modal User Attribute Modeling
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Huang, Xiu, Yang, Zihao, Yang, Yang, Shen, Fumin, Xie, Ning, Shen, Heng Tao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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307. Jointly Learning Attentions with Semantic Cross-Modal Correlation for Visual Question Answering
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Cao, Liangfu, Gao, Lianli, Song, Jingkuan, Xu, Xing, Shen, Heng Tao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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308. Provenance-Based Rumor Detection
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Duong, Chi Thang, Nguyen, Quoc Viet Hung, Wang, Sen, Stantic, Bela, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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309. Learning Robust Graph Hashing for Efficient Similarity Search
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Liu, Luyao, Zhu, Lei, Li, Zhihui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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310. Training Deep Ranking Model with Weak Relevance Labels
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Luo, Cheng, Zheng, Yukun, Mao, Jiaxin, Liu, Yiqun, Zhang, Min, Ma, Shaoping, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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311. An Embedded Feature Selection Framework for Hybrid Data
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Boroujeni, Forough Rezaei, Stantic, Bela, Wang, Sen, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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312. DSKQ: A System for Efficient Processing of Diversified Spatial-Keyword Query
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Jiang, Shanqing, Zhang, Chengyuan, Zhang, Ying, Zhang, Wenjie, Lin, Xuemin, Cheema, Muhammad Aamir, Wang, Xiaoyang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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313. Deep Semantic Indexing Using Convolutional Localization Network with Region-Based Visual Attention for Image Database
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Zhang, Mingxing, Yang, Yang, Zhang, Hanwang, Ji, Yanli, Xie, Ning, Shen, Heng Tao, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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314. Searching k-Nearest Neighbor Trajectories on Road Networks
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Yuan, Pengcheng, Zhao, Qinpei, Rao, Weixiong, Yuan, Mingxuan, Zeng, Jia, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
- Published
- 2017
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315. Decomposition-Based Approximation of Time Series Data with Max-Error Guarantees
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Ruan, Boyu, Hua, Wen, Zhang, Ruiyuan, Zhou, Xiaofang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
- Published
- 2017
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316. Locality-Constrained Transfer Coding for Heterogeneous Domain Adaptation
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Li, Jingjing, Lu, Ke, Zhu, Lei, Li, Zhihui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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317. Reversible Fragile Watermarking for Fine-Grained Tamper Localization in Spatial Data
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Lan, Hai, Peng, Yuwei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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318. Mining High-Dimensional CyTOF Data: Concurrent Gating, Outlier Removal, and Dimension Reduction
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Lee, Sharon X., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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319. Efficient Supervised Hashing via Exploring Local and Inner Data Structure
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He, Shiyuan, Ye, Guo, Hu, Mengqiu, Yang, Yang, Shen, Fumin, Shen, Heng Tao, Li, Xuelong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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320. Extracting Keyphrases Using Heterogeneous Word Relations
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Shi, Wei, Liu, Zheng, Zheng, Weiguo, Yu, Jeffrey Xu, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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321. A New Data Mining Scheme for Analysis of Big Brain Signal Data
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Siuly, Siuly, Zarei, Roozbeh, Wang, Hua, Zhang, Yanchun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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322. A Multi-way Semi-stream Join for a Near-Real-Time Data Warehouse
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Naeem, M. Asif, Nguyen, Kim Tung, Weber, Gerald, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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323. Query Refinement for Correlation-Based Time Series Exploration
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Albarrak, Abdullah M., Sharaf, Mohamed A., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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324. Boosting Point-Based Trajectory Search with Quad-Tree
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Yadamjav, Munkh-Erdene, Wang, Sheng, Bao, Zhifeng, Zhang, Bang, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
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- 2017
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325. Surrounding Join Query Processing in Spatial Databases
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Li, Lingxiao, Taniar, David, Indrawan-Santiago, Maria, Shao, Zhou, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
- Published
- 2017
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326. The Flexible Group Spatial Keyword Query
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Ahmad, Sabbir, Kamal, Rafi, Ali, Mohammed Eunus, Qi, Jianzhong, Scheuermann, Peter, Tanin, Egemen, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Huang, Zi, editor, Xiao, Xiaokui, editor, and Cao, Xin, editor
- Published
- 2017
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327. Best Bang for the Buck: Cost-Effective Seed Selection for Online Social Networks.
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Han, Kai, He, Yuntian, Huang, Keke, Xiao, Xiaokui, Tang, Shaojie, Xu, Jingxin, and Huang, Liusheng
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ONLINE social networks , *SELECTION (Plant breeding) , *POLYNOMIAL time algorithms , *VIRAL marketing , *SOCIAL networks , *APPROXIMATION algorithms - Abstract
We study the min-cost seed selection problem in online social networks for viral marketing, where the goal is to select a set of seed nodes with the minimum total cost such that the expected number of influenced nodes in the network exceeds a predefined threshold. We propose several algorithms that outperform the previous studies both on the theoretical approximation ratio and on the experimental performance. In the case where the nodes have heterogeneous costs, our algorithms are the first bi-criteria approximation algorithms with polynomial running time and provable approximation ratio. In the case where the users have uniform costs, our algorithms achieve logarithmic approximation ratio and provable time complexity which is smaller than that of the existing algorithms in orders of magnitude. We conduct extensive experiments using real social networks. The experimental results show that, our algorithms significantly outperform the existing algorithms both on the total cost and on the running time, and also scale well to billion-scale networks. [ABSTRACT FROM AUTHOR]
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- 2020
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328. ROAM: A Fundamental Routing Query on Road Networks with Efficiency.
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Luo, Siqiang, Cheng, Reynold, Kao, Ben, Xiao, Xiaokui, Zhou, Shuigeng, and Hu, Jiafeng
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ALGORITHMS , *TRAVEL costs , *COST shifting , *PUBLIC transit , *GRAPH algorithms - Abstract
Novel road-network applications often recommend a moving object (e.g., a vehicle) about interesting services or tasks on its way to a destination. A taxi-sharing system, for instance, suggests a new passenger to a taxi while it is serving another one. The traveling cost is then shared among these passengers. A fundamental query is: given two nodes $s$ s and $t$ t , and an area $\mathcal {A}$ A on road network graph $\mathcal {G}$ G , is there a “good” route (e.g., short enough path) $P$ P from $s$ s to $t$ t that crosses $\mathcal {A}$ A in $\mathcal {G}$ G ? In a taxi-sharing system, $s$ s and $t$ t can be a taxi's current and destined locations, and $\mathcal {A}$ A contains all the places to which a person waiting for a taxi is willing to walk. Answering this Route and Area Matching (ROAM) Query allows the application involved to recommend appropriate services to users efficiently. In this paper, we examine efficient ROAM query algorithms. Particularly, we develop solutions for finding a $\rho$ ρ -route, which is an $s$ s - $t$ t path that passes $\mathcal {A}$ A , with a length of at most $(1+\rho)$ (1 + ρ) times the shortest distance between $s$ s and $t$ t . The existence of a $\rho$ ρ -route implies that a service or task located at $\mathcal {A}$ A can be found for a given moving object $m$ m , and that $m$ m only deviates slightly from its current route. We present comprehensive studies on index-free and index-based algorithms for answering ROAM queries. Comprehensive experiments show that our algorithm runs up to 30 times faster than baseline algorithms. [ABSTRACT FROM AUTHOR]
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- 2020
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329. The Disruptions of 5G on Data-Driven Technologies and Applications.
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Loghin, Dumitrel, Cai, Shaofeng, Chen, Gang, Dinh, Tien Tuan Anh, Fan, Feiyi, Lin, Qian, Ng, Janice, Ooi, Beng Chin, Sun, Xutao, Ta, Quang-Trung, Wang, Wei, Xiao, Xiaokui, Yang, Yang, Zhang, Meihui, and Zhang, Zhonghua
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SMART cities , *TECHNOLOGICAL innovations , *TECHNOLOGY , *DATA management , *DATABASES , *LANDSCAPE assessment , *LANDSCAPE design - Abstract
With 5G on the verge of being adopted as the next mobile network, there is a need to analyze its impact on the landscape of computing and data management. In this paper, we analyze the impact of 5G on both traditional and emerging technologies and project our view on future research challenges and opportunities. With a predicted increase of 10-100x in bandwidth and 5-10x decrease in latency, 5G is expected to be the main enabler for smart cities, smart IoT and efficient healthcare, where machine learning is conducted at the edge. In this context, we investigate how 5G can help the development of federated learning. Network slicing, another key feature of 5G, allows running multiple isolated networks on the same physical infrastructure. However, security remains the main concern in the context of virtualization, multi-tenancy and high device density. Formal verification of 5G networks can be applied to detect security issues in massive virtualized environments. In summary, 5G will make the world even more densely and closely connected. What we have experienced in 4G connectivity will pale in comparison to the vast amounts of possibilities engendered by 5G. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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330. A Novel Recommendation Algorithm Based on Heterogeneous Information Network Similarity and Preference Diffusion
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Zhang, Bangzuo, Tang, Shulin, Ying, Zongming, Cai, Yongjian, Xu, Guiping, Xu, Kun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xiao, Xiaokui, editor, and Zhang, Zhenjie, editor
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- 2015
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331. Social Media Usage as a Service: A Service-Dominant Logic Perspective
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Hu, Tao, Wang, Hengjie, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xiao, Xiaokui, editor, and Zhang, Zhenjie, editor
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- 2015
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332. Information Diffusion in Online Social Networks: Models, Methods and Applications
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Hu, Changjun, Xu, Wenwen, Shi, Peng, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xiao, Xiaokui, editor, and Zhang, Zhenjie, editor
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- 2015
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333. Information Revelation for Better or Worse Recommendation: Understanding Chinese Users’ Privacy Attitudes and Practices
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Tang, Tiffany Y., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xiao, Xiaokui, editor, and Zhang, Zhenjie, editor
- Published
- 2015
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334. Mining Personal Interests of Microbloggers Based on Free Tags in SINA Weibo
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Wang, Xiang, Yu, Xiang, Zhou, Bin, Jia, Yan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xiao, Xiaokui, editor, and Zhang, Zhenjie, editor
- Published
- 2015
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335. Personalized Mention Probabilistic Ranking – Recommendation on Mention Behavior of Heterogeneous Social Network
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Li, Quanle, Song, Dandan, Liao, Lejian, Liu, Li, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xiao, Xiaokui, editor, and Zhang, Zhenjie, editor
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- 2015
- Full Text
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336. A New Link Prediction Algorithm Based on Local Links
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Yang, Juan, Yang, Lixin, Zhang, Pengye, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xiao, Xiaokui, editor, and Zhang, Zhenjie, editor
- Published
- 2015
- Full Text
- View/download PDF
337. Spammer Detection on Online Social Networks Based on Logistic Regression
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Zhu, Xiang, Nie, Yuanping, Jin, Songchang, Li, Aiping, Jia, Yan, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xiao, Xiaokui, editor, and Zhang, Zhenjie, editor
- Published
- 2015
- Full Text
- View/download PDF
338. A Distributed RDF Storage and Query Model Based on HBase
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Li, Keran, Wu, Bin, Wang, Bai, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xiao, Xiaokui, editor, and Zhang, Zhenjie, editor
- Published
- 2015
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339. Efficient index structures for reachability and shortest path queries
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Sibo Wang, Xiao Xiaokui, School of Computer Engineering, and Centre for Advanced Information Systems
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Theoretical computer science ,Index (economics) ,Computer science ,Reachability ,Shortest path problem ,Engineering::Computer science and engineering::Information systems::Database management [DRNTU] - Abstract
Graphs are a fundamental data structure to represent objects and their relations in various domains, e.g., social science, citation analysis, web link analysis, and navigation systems. Reachability and shortest path queries are two types of primitive and well-studied graph queries. In this thesis, we investigate on how to build efficient index structures to answer the reachability queries and two variants of the shortest path queries. The first variant of the shortest path query considers the shortest path in timetable graphs, where it requires the consideration of spatiotemporal constraints between different edges. The second variant aims to provide the shortest path under an additional cost constraint. These problems find many real world applications, and are challenging to achieve high efficiency on sizable graphs. First, we study the reachability queries on directed graphs. Given a directed graph $G$, a source $s$, and a target $t$, a reachability query asks whether there exists a path from $s$ to $t$ in $G$. The state-of-the-art solutions for answering reachability queries are a class of labelling schemes referred to as {\em Hierarchical Hub Labelings (HHL)}. One distinct characteristic of {\em HHL} is that any {\em HHL} index implicitly or explicitly defines a total order of vertices in the graph, and the performance of the index is solely decided by the ordering. Several heuristic approaches have been proposed for vertex ordering in {\em HHL}, but none of them provides any worst-case guarantee. We present a novel study on the vertex ordering in {\em HHL}, and devise a polynomial-time algorithm for vertex ordering that yields an {\em HHL} index whose size is at most $O(\sqrt{n})$ times the optimal one. Motivated by our theoretical study, we design a new heuristic for vertex ordering, and show that it leads to an {\em HHL} index with superior practical performance for reachability queries. Besides, identifying fast routes in public transportation networks is an important problem with applications in map services and navigation systems. In public transportation networks, any traversal between different network locations relies on transportation services (e.g., buses, subways, and ferries.) that run on fixed routes with pre-defined schedules. A public transportation network can often be modeled as a timetable graph where (i) each node represents a station; and (ii) each directed edge $\langle u, v\rangle $ is associated with a timetable that records the departure (resp. arrival) time of each vehicle at station $u$ (resp. $v$). There are several types of shortest path queries on timetable graphs. We study three types of shortest path queries for route planning that have been extensively studied on timetable graphs: {\em earliest arrival path (EAP)}, {\em latest departure path (LDP)}, and {\em shortest duration path (SDP)} queries. These three types of queries aim to find a path that arrives at a place as early as possible, departs as late as possible but arrives on time, and has the shortest travel time, respectively. We present {\em Timetable Labelling (TTL)}, an efficient indexing technique for shortest path queries on timetable graphs. The basic idea of {\em TTL} is to associate each node $u$ with a set of labels, each of which records the shortest travel time from $u$ to some other node $v$ given a certain departure time from $u$; such labels would then be used during query processing to improve efficiency. In addition, we propose query algorithms that enable {\em TTL} to support EAP, LDP, and SDP queries, and investigate how we reduce the space consumption of {\em TTL} with advanced preprocessing and label compression methods. By conducting an extensive set of experiments on real world datasets, we demonstrate that {\em TTL} significantly outperforms the states of the art in terms of query efficiency, while incurring moderate preprocessing and space overheads. Finally, {\em constrained shortest path (CSP)} is a variant of the shortest path problem with an additional total cost constraint. Specifically, in a CSP query, each edge in the road network is associated with both a length and a cost, e.g., travel distance and time. Given an origin $s$, a destination $t$, and a cost constraint $\theta$, the goal is to find the path from $s$ to $t$ that minimizes its total length, while satisfying that its total cost does not exceed $\theta$. Deriving the exact answer for a CSP query has been proven to be NP-hard, and the majority of previous work focuses on approximate solutions. We propose {\em COLA}, the first practical solution for approximate CSP processing on large road networks. {\em COLA} exploits the facts that a road network can be effectively partitioned, and that there exists a relatively small set of landmark vertices that commonly appear in CSP results. Meanwhile, {\em COLA} includes both an effective indexing scheme for partition boundary vertices, and an efficient on-the-fly algorithm called $\alpha$-Dijk for path computation within a partition. Extensive experiments demonstrate that on continent-sized networks with tens of millions of vertices, {\em COLA} answers an approximate CSP query in sub-second time, whereas existing methods take hours. Interestingly, even without an index, the $\alpha$-Dijk algorithm in {\em COLA} still outperforms previous solutions by more than an order of magnitude. DOCTOR OF PHILOSOPHY (SCE)
- Published
- 2019
340. Efficient query processing on large graphs
- Author
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Diwen Zhu, Xiao Xiaokui, School of Computer Engineering, and Centre for Advanced Information Systems
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Query expansion ,Information engineering ,Theoretical computer science ,Computer science ,Query optimization ,Query language ,Engineering::Computer science and engineering::Information systems::Database management [DRNTU] - Abstract
Graphs provide a natural way to represent real-world objects and their relationships, and hence they are utilized to model concepts and data in various domains, such as social networks, the Web, biological informatics and spatial databases. In the big data era, most graphs are extremely large and growing rapidly in size, which brings challenges to processing graph queries e ciently. In this thesis, we investigate three important problems in graph query processing, namely, the point-to-point shortest path and distance queries on road networks, the single source shortest path and distance queries on diskresident graphs, and the point-to-point reachability queries on dynamic graphs. These problems are not only interesting in themselves when the graph size becomes large, but also fundamental building blocks of numerous real-world applications. In particular, given two locations s and t in a road network, a point-to-point distance query returns the minimum network distance from s to t, while a point-to-point shortest path query computes the actual route that achieves the minimum distance. To solve this problem, we propose Arterial Hierarchy (AH), an index structure that narrows the gap between theory and practice. On the theoretical side, we show that, based on a realistic characteristic of real-world road networks, AH answers any distance query in constant time. In addition, any shortest path query can be answered with k more steps, where k is the number of vertices on the shortest path. On the practical side, AH outperforms the state-of-the-art methods in terms of query time and its space and pre-computation overheads are moderate. Then, given a vertex s in a graph G, a single source shortest distance query from s asks for the distance from s to every other vertex in G, while a single source shortest path query retrieves the shortest path from s to any other vertex. To handle these two types of queries on disk-resident graphs, we propose Highways-on-disk (HoD), a disk-based index that supports both shortest path and distance queries on directed and weighted graphs. Di erent from existing methods, HoD does not assume the graph ts into memory, does not limit the graph type as undirected graph, and does not restrict that the edges have unit weights or even integer weights. Although being more general and exible, HoD is still able to outperform the state-of-the-art methods in terms of query processing time by up to two orders of magnitude. In addition, HoD has a smaller space consumption and pre-computation time in most cases. Finally, given two vertices s and t in G, a reachability query from s to t asks whether there exists a path from s to t in G. To solve the problem on dynamic graphs, we rsti propose the Total Order Labeling (TOL) framework, which summarizes three most advanced methods [22,51,94] for reachability queries on static graphs. Then we investigate novel algorithms to handle updates of TOL, regarding both insertions and deletions on dynamic graphs. To our best knowledge, this is the rst practically e cient and scalable approach that handles the point-to-point reachability queries on dynamic graphs. Finally we propose a new reachability index Butter y, which o ers reduced preprocessing, space, and query costs than any existing indices under TOL [22,51,94]. We experimentally evaluate TOL using a large variety of benchmark datasets with up to twenty million vertices, and we demonstrate the superiority of TOL against alternative solutions for static and dynamic graphs. DOCTOR OF PHILOSOPHY (SCE)
- Published
- 2019
341. The Meaning of Dissimilar: An Evaluation of Various Similarity Quantification Approaches Used to Evaluate Community Detection Solutions
- Author
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Luca Rossi, Obaida Hanteer, Spezzano, Francesca, Chen, Wei, and Xiao, Xiaokui
- Subjects
Ground truth ,Computer science ,business.industry ,Pattern recognition ,Context (language use) ,02 engineering and technology ,Disjoint sets ,Function (mathematics) ,Similarity (network science) ,020204 information systems ,Taxonomy (general) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Meaning (existential) ,Artificial intelligence ,business ,Cluster analysis - Abstract
Evaluating a community detection method involvesmeasuring the extent to which the resulted solution, i.e clustering,is similar to an optimal solution, a ground truth. Differentnormalized similarity indices have been proposed in the literatureto quantify the extent to which two clusterings are similar where1 refers to a perfect agreement between them (i.e the two clusterings are identical) and 0 refers to a perfect disagreement. Whileinterpreting the similarity score 1 seems to be intuitive, it does notseem to be so when the similarity score is otherwise suggestinga level of disagreement between the compared clusterings. Thatis because there is no universal definition of dissimilarity whenit comes to comparing two clusterings. In this paper, we addressthis issue by first providing a taxonomy of similarity indicescommonly used for evaluating community detection solutions.We then elaborate on the meaning of clusterings dissimilarityand the types of possible dissimilarities that can exist among twoclusterings in the context of community detection. We perform anextensive evaluation to study the behaviour of different similarityindices as a function of the dissimilarity type with both disjointand non-disjoint clusterings. We finally provide practitioners withsome insights on which similarity indices to use for the task athand and how to interpret their values
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- 2019
342. GEMSEC: Graph Embedding with Self Clustering
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Charles Sutton, Benedek Rozemberczki, Ryan Davies, Rik Sarkar, Spezzano, Francesca, Chen, Wei, and Xiao, Xiaokui
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Social and Information Networks (cs.SI) ,FOS: Computer and information sciences ,Hyperparameter ,Theoretical computer science ,Linear programming ,Graph embedding ,Computer science ,feature extraction ,Feature vector ,network embedding ,Feature extraction ,Computer Science - Social and Information Networks ,node embedding ,02 engineering and technology ,Regularization (mathematics) ,020204 information systems ,community detection ,0202 electrical engineering, electronic engineering, information engineering ,Embedding ,020201 artificial intelligence & image processing ,Cluster analysis ,clustering - Abstract
Modern graph embedding procedures can efficiently process graphs with millions of nodes. In this paper, we propose GEMSEC – a graph embedding algorithm which learns a clustering of the nodes simultaneously with computing their embedding. GEMSEC is a general extension of earlier work in the domain of sequence-based graph embedding. GEMSEC places nodes in an abstract feature space where the vertex features minimize the negative log-likelihood of preserving sampled vertex neighborhoods, and it incorporates known social network properties through a machine learning regularization. We present two new social network datasets and show that by simultaneously considering the embedding and clustering problems with respect to social properties, GEMSEC extracts high-quality clusters competitive with or superior to other community detection algorithms. In experiments, the method is found to be computationally efficient and robust to the choice of hyperparameters.
- Published
- 2018
343. Mobile app marketplace mining : methods and applications
- Author
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Ning Chen, Hoi Chu Hong, Steven, Xiao Xiaokui, School of Computer Engineering, and Centre for Advanced Information Systems
- Subjects
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence [DRNTU] ,Engineering::Computer science and engineering::Software::Software engineering [DRNTU] - Abstract
With the rocketing development of mobile applications, app marketplace has drawn much more attention among researchers in multiple important research areas, ranging from data mining, machine learning, software engineering to security. App marketplace is a new form of software repository which contains a wealth of multi-modal heterogeneous data associated with apps, e.g., description text, screenshot images, user reviews, and so on. Such app markets data is (i) large in volume; (ii) growing and changing rapidly; (iii) complex in its variety; and (iv) potentially valuable for various stakeholders in the mobile app ecosystem, e.g., developers, users, app platform providers, and etc. However, until now, there still lacks of mining approaches that can help app ecosystem stakeholders exploit such valuable data in an effective and efficient way. In this thesis, we present three novel mobile app marketplace data mining/machine learning schemes and apply them to address three crucial applications for app ecosystem stakeholders by exploring a specific and increasingly important data source, i.e., app markets data. First of all, in order to assist app developers find the most ``informative" user reviews from a large and rapidly increasing pool of user reviews in app markets, we present a novel framework named ``AR-Miner" (App Review Miner) which consists of four main steps: (i) AR-Miner first filters noisy and irrelevant reviews, (ii) then groups the remaining informative reviews by applying topic modeling, (iii) further prioritizes the informative reviews by using our proposed novel ranking model, (iv) and finally presents an intuitive visualized summarization to app developers. We conduct an extensive set of empirical studies on four popular Android apps (with hundred thousands of user reviews) to evaluate the performance of AR-Miner, from which the encouraging results show that AR-Miner is effective, efficient and promising. Second, in order to model the high-level app similarity, we present ``SimApp" -- a novel framework which consists of two stages: (i) we define a set of kernel (similarity) functions to measure app similarity for each modality of data; (ii) we assume the target app similarity function is a linear combination of the multiple kernels, and develop a new online kernel learning algorithm to learn the optimal combination weights of these kernels from training data streams. We conduct extensive experiments on a real-world dataset crawled from Google Play to evaluate SimApp, from which the encouraging results validate its efficacy in app similarity modeling. Finally, we address the issue of automatic app annotation, which could be potentially useful for different app ecosystem stakeholders. Most mainstream app markets, e.g., Google Play, Apple App Store, etc., currently do not explicitly support automatic annotation for apps. To address this problem, we propose a novel retrieval-based app annotation framework for automatically annotating apps. Given a query app (without any tags), our proposed framework (i) first retrieves a set of N apps which are most semantically similar to the query app from a large app database; and (ii) then mines the ``Description" and ``Update" text of both the query app and its top-N similar apps to discover relevant tags for the query app. To evaluate the efficacy of our proposed framework, we conduct a series of qualitative and quantitative experiments. The encouraging results demonstrate that our technique is both effective and promising. DOCTOR OF PHILOSOPHY (SCE)
- Published
- 2015
344. Efficient techniques for subgraph mining and query processing
- Author
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Wenqing Lin, Xiao Xiaokui, School of Computer Engineering, and Centre for Advanced Information Systems
- Subjects
Information engineering ,Computer science ,Data mining ,Query language ,computer.software_genre ,computer ,Engineering::Computer science and engineering::Information systems::Database management [DRNTU] - Abstract
Graph data has been so prevalent that efficiently obtaining useful information from them is highly demanded. Given massive amounts of graph data, people are often interested in a small portion, namely their subgraphs, by the processes of mining and querying. Due to the enormous number of subgraphs in the massive graph data, these processes are highly costly. In this thesis, we study three important problems on subgraph mining and query processing, i.e., frequent subgraph mining, network motif discovery, and generalized subgraph query processing. These problems find numerous applications in real world, whereas they are extremely challenging.% since subgraph isomorphism testing is NP-hard. First, mining frequent subgraphs from a large collection of graph objects is an important problem in several application domains such as bio-informatics, social networks, computer vision, etc. The main challenge in subgraph mining is efficiency, as (i) testing for graph isomorphisms is computationally intensive, and (ii) the cardinality of the graph collection to be mined may be very large. We propose a two-step {\em filter-and-refinement} approach that is suitable to massive parallelization within the scalable MapReduce computing model. We partition the collection of graphs among worker nodes, and each worker applies the filter step to determine a set of candidate subgraphs that are {\em locally frequent} in its partition. The union of all such graphs is the input to the refinement step, where each candidate is checked against all partitions and only the {\em globally frequent} graphs are retained. We devise a statistical threshold mechanism that allows us to predict which subgraphs have a high chance to become globally frequent, and thus reduce the computational overhead in the refinement step. We also propose effective strategies to avoid redundant computation in each round when searching for candidate graphs, as well as a lightweight graph compression mechanism to reduce the communication cost between machines. Extensive experimental evaluation results on several real-world large graph datasets show that the proposed approach clearly outperforms the existing state-of-the-art and provides a practical solution to the problem of frequent subgraph mining for massive collections of graphs. Second, the identification of network motifs has essential applications in numerous domains, such as pattern detection in biological networks and graph analysis in digital circuits. However, mining network motifs is computationally challenging, as it requires to enumerate subgraphs from a real-life graph, and compute the frequency of each subgraph in a large number of random graphs. In particular, existing solutions often require days to derive network motifs from biological networks with only a few thousand vertices. To address this problem, this thesis presents a novel study on network motif discovery using Graphical Processing Units (GPUs). The basic idea is to employ GPUs to parallelize a large number of subgraph matching tasks in computing subgraph frequencies from random graphs, so as to reduce the overall computation time of network motif discovery. We explore the design space of GPU-based subgraph matching algorithms, with careful analysis of several crucial factors that affect the performance of GPU programs. Based on our analysis, we develop a GPU-based solution that (i) drastically differs from existing CPU-based methods, and (ii) exploits the strengths of GPUs in terms of parallelism while mitigating their limitations in terms of the computation power per GPU core. With extensive experiments on a variety of biological networks, we show that our solution is up to two orders of magnitude faster than the best CPU-based approach, and is around $20$ times more cost-effective than the latter, when taking into account the monetary costs of the CPU and GPUs used. Finally, we study a new type of graph queries, which injectively maps its edges to paths of the graphs in a given database, where the length of each path is constrained by a given threshold specified by the weight of the corresponding matching edge. We give important applications of the new graph query and identify new challenges of processing such a query. In particular, the new type of graph queries further explodes the already exponential search space compared with conventional graph queries. Besides, existing pruning techniques cannot be directly applied or they are simply not adequate, since our new type of query graphs is different from their indexed features. To address these issues, we devise the cost model of the branch-and-bound algorithm framework for processing the graph query, and propose an efficient algorithm to minimize the cost overhead. We also develop three indexing techniques to efficiently answer the queries online, each of which can be applied into different scenarios. Finally, we verify the efficiency of our proposed indexes with extensive experiments on large real and synthetic datasets. DOCTOR OF PHILOSOPHY (SCE)
- Published
- 2015
345. Deep learning with application to hashing
- Author
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Boshen Zhang, School of Computer Engineering, Xiao Xiaokui, and Steven C.H. Hoi
- Subjects
Artificial architecture ,Computer science ,business.industry ,Deep learning ,Informatics engineering ,Hash function ,Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence [DRNTU] ,Artificial intelligence ,business - Abstract
Deep Learning and Learning to Hash are two important research areas in machine learning, which have rapid improvements in recent years. What I mainly researched on is an inter-discipline field: deep learning for cross view hashing. Multiple layers of representation in deep learning has the property of abstracting representation from input data, while, in the cross view similarity search, the biggest difficulty is to represent items from one domain to another. Here, I want to take advantage of the latest deep learning technology to solve the cross view similarity search problem. Hashing is used to accelerate this process. This thesis mainly contains three parts. Chapter 2 is a literature survey. It contains a deep learning survey and a learning to hash survey. The deep learning survey briefly introduces fundamental technology of deep learning and its recent development including the latest technology. The Learning to Hash survey brief introduces some widely used learning to hash algorithms. Chapter 3 is an experiment about comparison of some state of the arts learning to hash algorithms. Chapter 4 is cross view hashing based on deep learning. I present a cross view feature hashing technique using deep learning and show some results. These three chapters are main chapters. Chapter 1 and Chapter 5 are introduction and conclusion. MASTER OF ENGINEERING (SCE)
- Published
- 2014
346. Disease Gene Classification with Metagraph Representations.
- Author
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Ata SK, Fang Y, Wu M, Li XL, and Xiao X
- Subjects
- Data Mining, Humans, Protein Interaction Mapping, Publications, Algorithms, Computational Biology methods, Disease genetics
- Abstract
This chapter is based on exploiting the network-based representations of proteins, metagraphs, in protein-protein interaction network to identify candidate disease-causing proteins. Protein-protein interaction (PPI) networks are effective tools in studying the functional roles of proteins in the development of various diseases. However, they are insufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To enhance PPI networks, we utilize biological properties of individual proteins as well. More specifically, we integrate keywords from UniProt database describing protein properties into the PPI network and construct a novel heterogeneous PPI-Keyword (PPIK) network consisting of both proteins and keywords. As proteins with similar functional duties or involving in the same metabolic pathway tend to have similar topological characteristics, we propose to represent them with metagraphs. Compared to the traditional network motif or subgraph, a metagraph can capture the topological arrangements through not only the protein-protein interactions but also protein-keyword associations. We feed those novel metagraph representations into classifiers for disease protein prediction and conduct our experiments on three different PPI databases. They show that the proposed method consistently increases disease protein prediction performance across various classifiers, by 15.3% in AUC on average. It outperforms the diffusion-based (e.g., RWR) and the module-based baselines by 13.8-32.9% in overall disease protein prediction. Breast cancer protein prediction outperforms RWR, PRINCE, and the module-based baselines by 6.6-14.2%. Finally, our predictions also exhibit better correlations with literature findings from PubMed database.
- Published
- 2018
- Full Text
- View/download PDF
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