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Online information has increased tremendously in today’s age of the Internet. As a result, the need has arisen to extract relevant content from the plethora of available information. Researchers are widely using automatic text summarization techniques for extracting useful and relevant information from voluminous available information. The summary obtained from the automatic text summarization often faces the issues of diversity and information coverage. Earlier researchers have used graph-based approaches for ranking and optimization. This research work introduces a probabilistic approach named as ClusRank for summary extraction, comprising of a two-stage sentence selection model involving clustering and then ranking of sentences. The initial stage involves clustering of sentences using a proposed overlapping clustering algorithm on the weighted network, and later selection of salient sentences using the introduced probabilistic approach. In the analysis of real-world networks, community structure development is essential because it provides strategic insights that help decision-makers make well-informed choices. Furthermore, methodologically strict community detection algorithms are required due to the occurrence of discontinuous, overlapping, and nested community patterns in such networks.This research work, an algorithm is presented for detecting overlapping communities based on the concept of rough set and granular information on links. The sentence selection algorithm based on budget maximum coverage approach supports the assumption that larger sub-topics in a document are of more importance than smaller subtopics. The performance of the proposed probabilistic ClusRank is validated on DUC2001, DUC 2002, DUC2004, and DUC 2006 data sets.

Text Summarization models facilitate biomedical clinicians and researchers in acquiring informative data from enormous domain-specific literature within less time and effort. Evaluating and selecting the most informative sentences from biomedical articles is always challenging. This study aims to develop a dual-mode biomedical text summarization model to achieve enhanced coverage and information. The research also includes checking the fitment of appropriate graph ranking techniques for improved performance of the summarization model. The input biomedical text is mapped as a graph where meaningful sentences are evaluated as the central node and the critical associations between them. The proposed framework utilizes the top k similarity technique in a combination of UMLS and a sampled probability-based clustering method which aids in unearthing relevant meanings of the biomedical domain-specific word vectors and finding the best possible associations between crucial sentences. The quality of the framework is assessed via different parameters like information retention, coverage, readability, cohesion, and ROUGE scores in clustering and non-clustering modes. The significant benefits of the suggested technique are capturing crucial biomedical information with increased coverage and reasonable memory consumption. The configurable settings of combined parameters reduce execution time, enhance memory utilization, and extract relevant information outperforming other biomedical baseline models. An improvement of 17% is achieved when the proposed model is checked against similar biomedical text summarizers. [ABSTRACT FROM AUTHOR]

Graph mining is an essential component of recommender systems and search engines. Outputs of graph mining models typically provide a ranked list sorted by each item's relevance or utility. However, recent research has identified issues of algorithmic bias in such models, and new graph mining algorithms have been proposed to correct for bias. As such, algorithm developers need tools that can help them uncover potential biases in their models while also exploring the impacts of correcting for biases when employing fairness-aware algorithms. In this paper, we present FairRankVis, a visual analytics framework designed to enable the exploration of multi-class bias in graph mining algorithms. We support both group and individual fairness levels of comparison. Our framework is designed to enable model developers to compare multi-class fairness between algorithms (for example, comparing PageRank with a debiased PageRank algorithm) to assess the impacts of algorithmic debiasing with respect to group and individual fairness. We demonstrate our framework through two usage scenarios inspecting algorithmic fairness. [ABSTRACT FROM AUTHOR]

Recommender systems have become an indispensable engine for online applications, which can help users locate the items they need among numerous other candidate items. Despite the success of general models, sequential recommender systems based on sequential patterns have garnered attention, especially in e-commerce, because customers do not necessarily purchase the same items over time. Markov chains and neural networks are the most adopted models in previous studies, but few integrated user sequential order information and user numeric rating feedback. To simultaneously capture the time-aware demands and time-invariant interests of e-commerce customers, this study proposes a unique heterogeneous graph structure to model sequential buying order and rating feedback and develops a query-based recommendation approach to predict future purchase behavior. Experiments on four different datasets show that the approach demonstrates improved performance for different metrics. [ABSTRACT FROM AUTHOR]

A 2-ranking of a graph G is an ordered partition of the vertices of G into independent sets V1,...,Vt such that for i

Abstract Bottom-up saliency detection has been widely studied in many applications, such as image retrieval, object recognition, image compression and so on. Saliency detection via manifold ranking (MR) can identify the most salient and important area from an image efficiently. One limitation of the MR model is that it fails to consider the prior information in its ranking process. To overcome this limitation, we propose a prior regularized multi-layer graph ranking model (RegMR), which uses the prior calculating by boundary connectivity. We employ the foreground possibility in the first stage and background possibility in the second stage based on a multi-layer graph. We compare our model with fifteen state-of-the-art methods. Experiments show that our model performs well than all other methods on four public databases on PR-curves, F-measure and so on. [ABSTRACT FROM AUTHOR]

Recommender systems aim to identify relevant items for particular users in large-scale online applications. The historical rating data of users is a valuable input resource for many recommendation models such as collaborative filtering (CF), but these models are known to suffer from the rating sparsity problem when the users or items under consideration have insufficient rating records. With the continued growth of online social networks, the increased user-to-user relationships are reported to be helpful and can alleviate the CF rating sparsity problem. Although researchers have developed a range of social network-based recommender systems, there is no unified model to handle multirelational social networks. To address this challenge, this paper represents different user relationships in a multigraph and develops a multigraph ranking model to identify and recommend the nearest neighbors of particular users in high-order environments. We conduct empirical experiments on two real-world datasets: 1) Epinions and 2) Last.fm, and the comprehensive comparison with other approaches demonstrates that our model improves recommendation performance in terms of both recommendation coverage and accuracy, especially when the rating data are sparse. [ABSTRACT FROM PUBLISHER]

Graph ranking is one popular and successful technique for image retrieval, but its effectiveness is often limited by the well-known semantic gap. To bridge this gap, one of the current trends is to leverage the click-through data associated with images to facilitate the graph-based image ranking. However, the sparse and noisy properties of the image click-through data make the exploration of such resource challenging. Towards this end this paper propose a novel click-boosted graph ranking framework for image retrieval, which consists of two coupled components. Concretely, the first one is a click predictor based on matrix factorization with visual regularization, in order to alleviate the sparseness of the click-through data. The second component is a soft-label graph ranker that conducts the image ranking by using the enriched click-through data noise-tolerantly. Extensive experiments for the tasks of click predicting and image ranking validate the effectiveness of the proposed methods in comparison to several existing approaches. [ABSTRACT FROM AUTHOR]

Image saliency detection is an important issue in computer vision and has been widely used in many applications. In this paper, we propose a new global and local consistent ranking (GLR) model for image saliency computation. Firstly, we propose to use an absorbed Markov chain model to obtain a kind of global ranking for image superpixels, in which the absorbing nodes represent the virtual boundary superpixels and the transient nodes denote the general superpixels of image. Then, the absorbed time from each transient node to boundary absorbing nodes are computed. This absorbing time of transient node measures its global similarity with all absorbing nodes and thus provides a kind of global ranking for each transient node w.r.t. absorbing nodes. At last, we further exploit the local manifold structure and incorporate the local manifold smooth information into ranking process and thus propose a general global and local consistent ranking for saliency detection. Experimental results on several large benchmark databases show the effectiveness of the proposed GLR method. [ABSTRACT FROM AUTHOR]

The tree-depth problem can be seen as finding an elimination tree of minimum height for a given input graph G. We introduce a bicriteria generalization in which additionally the width of the elimination tree needs to be bounded by some input integer b. We are interested in the case when G is the line graph of a tree, proving that the problem is NP -hard and obtaining a polynomial-time additive 2 b -approximation algorithm. This particular class of graphs received significant attention in the past, mainly due to a number of potential applications, e.g. in parallel assembly of modular products, or parallel query processing in relational databases, as well as purely combinatorial applications including searching in tree-like partial orders (which in turn generalizes binary search on sorted data). [ABSTRACT FROM AUTHOR]

An accurate cultural insect detection and recognition relies mainly on a proper automatic segmentation. This paper deals with butterfly segmentation in ecological images characterized by several artifacts like the complexity of environmental decors and cluttered backgrounds. The distractors contained in the rich ecological environment and the huge difference between butterfly species complicate severely the segmentation and make it a challenging task. As butterflies appears to be well contrasted from their surrounding, we suggest to explore the saliency property to delineate accurately the butterfly boundaries. In this vein, we perform a graph ranking process with high level guidance according to foreground and background cues to improve the quality of segmentation. The ranking accuracy is improved through a weighting scheme that combines accurately color, texture and spatial information. The contribution of each used feature is controlled according to its relevance in highlighting butterfly regions. After that, we initialize foreground seeds from most salient pixels and background seeds from less salient pixels as an input for a Graph-cut algorithm to extract the butterfly from the background. Comparative evaluation has shown that our segmentation scheme outperforms some existing segmentation methods that provide high segmentation scores. [Display omitted] • Background and foreground cues guidance for graph ranking based butterfly segmentation. • Butterfly kernel determination based on regional connectivity to background cues. • Feature discriminative power for graph weighting scheme. • Background / butterfly discrimination based on regional saliency. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we deal with the arc ranking problem of directed graphs. We give some classes of graphs for which the arc ranking problem is polynomially solvable. We prove that deciding whether , where G is an acyclic orientation of a 3-partite graph is an NP-complete problem. In this way we answer an open question stated by Kratochvil and Tuza in 1999. [Copyright &y& Elsevier]

Text summarization tools can help biomedical researchers and clinicians reduce the time and effort needed for acquiring important information from numerous documents. It has been shown that the input text can be modeled as a graph, and important sentences can be selected by identifying central nodes within the graph. However, the effective representation of documents, quantifying the relatedness of sentences, and selecting the most informative sentences are main challenges that need to be addressed in graph-based summarization. In this paper, we address these challenges in the context of biomedical text summarization. We evaluate the efficacy of a graph-based summarizer using different types of context-free and contextualized embeddings. The word representations are produced by pre-training neural language models on large corpora of biomedical texts. The summarizer models the input text as a graph in which the strength of relations between sentences is measured using the domain specific vector representations. We also assess the usefulness of different graph ranking techniques in the sentence selection step of our summarization method. Using the common Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics, we evaluate the performance of our summarizer against various comparison methods. The results show that when the summarizer utilizes proper combinations of context-free and contextualized embeddings, along with an effective ranking method, it can outperform the other methods. We demonstrate that the best settings of our graph-based summarizer can efficiently improve the informative content of summaries and decrease the redundancy. [ABSTRACT FROM AUTHOR]