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Knowledge Graph (KG) has attracted more and more companies' attention for its ability to connect different types of data in meaningful ways and support rich data services. However, due to privacy concerns, different companies cannot share their own KGs with each other. Such data isolation problem limits the performance of KG and prevents its further development. Therefore, how to let multiple parties conduct KG-related tasks collaboratively on the basis of privacy protection becomes an important research question to answer. In this paper, to fill this gap, we summarize the open problems for privacy-preserving KG in the data isolation setting and propose possible solutions for them. Specifically, we summarize the open problems in privacy-preserving KG from four aspects, i.e., merging, query, representation, and completion. We present these problems in detail and propose possible technical solutions for them, along with the datasets, evaluation methods, and future research directions. We also provide three privacy-preserving KG application scenarios. [ABSTRACT FROM AUTHOR]

Graph Convolution Network (GCN) has been widely applied in recommender systems for its representation learning capability on user and item embeddings. However, GCN is vulnerable to noisy and incomplete graphs, which are common in real world, due to its recursive message propagation mechanism. In the literature, some work proposes to remove the feature transformation during message propagation, but making it unable to effectively capture the graph structural features. Moreover, they model users and items in the Euclidean space, which has been demonstrated to have high distortion when modeling complex graphs, further degrading the capability to capture the graph structural features and leading to sub-optimal performance. To this end, in this paper, we propose a simple yet effective Quaternion-based Graph Convolution Network (QGCN) recommendation model. In the proposed model, we utilize the hyper-complex Quaternion space to learn user and item representations and feature transformation to improve both performance and robustness. Specifically, we first embed all users and items into the Quaternion space. Then, we introduce the quaternion embedding propagation layers with quaternion feature transformation to perform message propagation. Finally, we combine the embeddings generated at each layer with the mean pooling strategy to obtain the final embeddings for recommendation. Extensive experiments on three public benchmark datasets demonstrate that our proposed QGCN model outperforms baseline methods by a large margin. [ABSTRACT FROM AUTHOR]

Recently, researches on dynamic decision-making based on streaming data are in full swing. As an indispensable technology for data management and analysis, indexing methods are also evolving. The indexing paradigm named learned index has been proposed to replace the traditional B-tree family in some scenarios. It has been proved that learned indexes can provide higher lookup efficiency and lower storage cost overhead than traditional indexes. Usually, learned indexes assume that the data is static or at least the data distribution is unchanged. However, the streaming scenarios break the strong assumption. This paper presents a learned index scheme for streaming scenarios (LIFOSS for short), where the workloads insert, delete, and query data arbitrarily. Precisely, LIFOSS consists of three parts: a) an adaptive packed-memory array which stores data and handles updates with lower bound of performance guaranteed; b) a middle-layer model group, used to fit the cumulative distribution function of data; c) a feedback mechanism designed to update parameters of the model group above in real-time locally. Extensive experiments on two public datasets show that LIFOSS performs better than the state-of-the-art dynamic learned index method. LIFOSS reduces the lookup latency by at least 6 % , and its dynamic performance is more stable, requiring only a tiny amount of extra space. [ABSTRACT FROM AUTHOR]

Integrating social networks as auxiliary information shows effectiveness in improving the performance for a recommendation task. Typical models usually characterize the user trust relationship as a binary adjacent matrix derived from a social graph, which basically only incorporates neighborhood interactions, and then encodes the trust values of different individuals with the same value. Such methods fail to capture the implicit high-order relations hidden under a graph structure, and thereby ignore the impact of indirect influencers. To address the aforementioned problems, we present an I mplicit T rust R elation-A ware model (ITRA) based on Variational Auto-Encoder (VAE). ITRA adopts an attention module to feed the weighted trust embedding information into an inherited non-linear VAE structure. In this sense, ITRA could provide recommendations by reconstructing a non-binary adjacency social matrix with implicit high-order interactions from both indirect key opinion leaders and explicit connections from neighbors. The extensive experiments conducted on three datasets illustrate that ITRA could achieve a better performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Purpose: Unlike the normal organ segmentation task, automatic tumor segmentation is a more challenging task because of the existence of similar visual characteristics between tumors and their surroundings, especially on computed tomography (CT) images with severe low contrast resolution, as well as the diversity and individual characteristics of data acquisition procedures and devices. Consequently, most of the recently proposed methods have become increasingly difficult to be applied on a different tumor dataset with good results, and moreover, some tumor segmentors usually fail to generalize beyond those datasets and modalities used in their original evaluation experiments. Methods: In order to alleviate some of the problems with the recently proposed methods, we propose a novel unified and end-to-end adversarial learning framework for automatic segmentation of any kinds of tumors from CT scans, called CTumorGAN, consisting of a Generator network and a Discriminator network. Specifically, the Generator attempts to generate segmentation results that are close to their corresponding golden standards, while the Discriminator aims to distinguish between generated samples and real tumor ground truths. More importantly, we deliberately design different modules to take into account the well-known obstacles, e.g., severe class imbalance, small tumor localization, and the label noise problem with poor expert annotation quality, and then use these modules to guide the CTumorGAN training process by utilizing multi-level supervision more effectively. Results: We conduct a comprehensive evaluation on diverse loss functions for tumor segmentation and find that mean square error is more suitable for the CT tumor segmentation task. Furthermore, extensive experiments with multiple evaluation criteria on three well-established datasets, including lung tumor, kidney tumor, and liver tumor databases, also demonstrate that our CTumorGAN achieves stable and competitive performance compared with the state-of-the-art approaches for CT tumor segmentation. Conclusion: In order to overcome those key challenges arising from CT datasets and solve some of the main problems existing in the current deep learning-based methods, we propose a novel unified CTumorGAN framework, which can be effectively generalized to address any kinds of tumor datasets with superior performance. [ABSTRACT FROM AUTHOR]

Address matching is a crucial task in various location-based businesses like take-out services and express delivery, which aims at identifying addresses referring to the same location in address databases. It is a challenging one due to various possible ways to express the address of a location, especially in Chinese. Traditional address matching approaches relying on string similarities and learning matching rules to identify addresses referring to the same location, could hardly solve the cases with redundant, incomplete or unusual expression of addresses. In this paper, to learn the geographical semantic representations for address strings, we novelly propose to get rich contexts for addresses from the Web through Web search engines, which could strongly enrich the semantic meaning of addresses that could be learned. Apart from that, we propose a two-stage geographical address representation learning model for address matching. In the first stage, we propose to use an encode-decoder architecture to learn the semantic vector representation for each address string where an up-sampling and sub-sampling strategy is applied to solve the problem of address redundancy and incompleteness. The attention mechanism is also applied to the model to highlight important features of addresses in their semantic representations. And in the second stage, we construct a single large graph from the corpus, which contains address elements and addresses as nodes, and the edges between nodes are built by word co-occurrence information to learn embedding representations for all the nodes on the graph. Our empirical study conducted on two real-world address datasets demonstrates that our approach greatly improves both precision (up to 8%) and recall (up to 12%) of the state-of-the-art existing methods. [ABSTRACT FROM AUTHOR]

With the ubiquity of mobile devices and wireless networks, Spatial Crowdsourcing (SC) has earned considerable importance and attention as a new strategy of problem-solving. Tasks in SC have location constraints and workers need to move to certain locations to perform them. Current studies mainly focus on maximizing the benefits of the SC platform. However, user average waiting time, which is an important indicator of user experience, has been overlooked. To enhance user experience, the SC platform needs to collect lots of data from both workers and users. During this process, the private information may be compromised if the platform is not trustworthy. In this paper, we first define user experience-driven secure task assignment problem and propose two privacy-preserving online task assignment strategies to minimize the average waiting time. We securely construct an encrypted bipartite graph to protect private data. Based on this encrypted graph, we propose a secure Kuhn-Munkres algorithm to realize task assignment without privacy disclosure. Theoretical analysis shows the security of our approach and experimental results demonstrates its efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Collaborative Web services QoS prediction (CQoSP) has been proved to be an effective tool to predict unknown QoS values of services. Recently a number of efforts have been made in this area, focusing on improving the accuracy of prediction. In this paper, we consider a novel kind of CQoSP, shared CQoSP, where multiple parties share their data with each other in order to provide more accurate prediction than a single party could do. To encourage data sharing, we propose a privacy-preserving framework which enables shared collaborative QoS prediction without leaking the private information of the involved party. Our framework is based on differential privacy, a rigorous and provable privacy model. We conduct extensive experiments on a real Web services QoS dataset. Experimental results show the proposed framework increases prediction accuracy while ensuring the privacy of data owners. [ABSTRACT FROM AUTHOR]

Temporal path discovery in dynamic graphs is significant in many applications, such as the trip planning in transportation networks, and the disease progression tracking in gene networks. Attributed Dynamic Graph (ADG) contains multiple value-changed attributes on edges, such as the speed of a bus between two stations, and the price of the bus ticket in different time periods. The traditional methods for temporal path discovery in ADGs assume users only consider a single constraint on the attributes in their models, such as finding the fast route to reach the destination under the constraint of a given budget, which makes the path discovery problem simple though it still suffers expensive time cost. However, such an assumption is too strict in real applications, where users can specify multiple constraints, such as finding the fast route under the constraints of a given budget and the total number of stopovers. In such a situation, the temporal path discovery becomes more complicated as it subsumes the classical NP-Complete Multi-Constraint Path (MCP) problem, and thus the traditional methods cannot work for finding a new type of Temporal Path with Multiple Constraints (TPMC). In this paper, we propose a set of new Two-Pass approximation algorithms to bi-directionally search ADGs to find TPMC results. To the best of our knowledge, our Two-Pass algorithms are the first algorithms to support the discovery of the temporal paths with multiple constraints in ADGs. The experimental results on 12 real-world dynamic graphs demonstrate that our algorithms outperform the state-of-the-art methods in both efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Publication recommendation is an interesting but challenging research problem. Most existing studies only use partial information of papers' contents, reference network or co-author relationship, which leads to an unsatisfied recommendation result. In this study, we propose a novel hybrid publication recommendation approach using compound information which retrieves top-K most relevant papers from a publication depository for a set of user input keywords. Our advantages comparing to the existing methods include: (1) Reaching a better recommendation results by taking the advantages of both content-based recommendation and citation-based recommendation and exploring much richer information of papers in one method; (2) Effectively solving the cold-start problem for new published papers by considering the vitality of papers and the impact factor of venues into the citation network; (3) Saving a large overhead in calculating the content-based similarity between papers and user input keywords by doing paper clustering based on the citation network. Extensive experiments on DBLP and Microsoft Academic datasets demonstrate that PubTeller improves the state-of-the-art methods with 4% in Precision and 4.5% in Recall. [ABSTRACT FROM AUTHOR]

Collaborative Web services QoS prediction has proved to be an important tool to estimate accurately personalized QoS experienced by individual users, which is beneficial for a variety of operations in the service ecosystem, such as service selection, composition and recommendation. While a number of achievements have been attained on the study of improving the accuracy of collaborative QoS prediction, little work has been done for protecting user privacy in this process. In this paper, we propose a privacy-preserving collaborative QoS prediction framework which can protect the private data of users while retaining the ability of generating accurate QoS prediction. We introduce differential privacy, a rigorous and provable privacy model, into the process of collaborative QoS prediction. We first present DPS, a method that disguises a user's observed QoS values by applying differential privacy to the user's QoS data directly. We show how to integrate DPS with two representative collaborative QoS prediction approaches. To improve the utility of the disguised QoS data, we present DPA, another QoS disguising method which first aggregates a user's QoS data before adding noise to achieve differential privacy. We evaluate the proposed methods by conducting extensive experiments on a real world Web services QoS dataset. Experimental results show our approach is feasible in practice. [ABSTRACT FROM AUTHOR]

The ubiquity of mobile device and wireless networks flourishes the market of spatial crowdsourcing, in which location constrained tasks are sent to workers and expected to be performed in some designated locations. To obtain a global optimal task assignment scheme, the platform usually needs to collect location information of all workers. During this process, there is a significant security concern, that is, the platform may not be trustworthy, so it brings about a threat to workers location privacy. In this paper, to tackle the privacy-preserving task assignment problem, we propose a privacy-preserving reverse auction based assignment model which consists of two key parts. In the first part, we generalize private location to travel cost and protect it by an anonymity based data aggregation protocol. In the second part, we propose a reverse auction task assignment algorithm, which is a truthful incentive mechanism, to encourage workers to offer authentic data. We theoretically show that the proposed model is secure against semi-honest adversaries. Experimental results show that our model is efficient and can scale to real SC applications. [ABSTRACT FROM AUTHOR]

Online Social Network (OSN) has been used to enhance service provision and service selection, where trust is one of the most important factors for the decision making of service consumers. Thus, a significant and challenging problem is how to effectively and efficiently find those social trust paths that can yield trustworthy trust evaluation results based on the requirements of a service consumer particularly in contextual OSNs which contains social contexts, like social relationships and social trust between participants, and social positions of participants. In this paper, we propose a new concept called Strong Social Graph (SSG), consisting of participants with strong social connections. We also propose an approach to identify SSGs, and propose a novel index method and a graph compression method for SSG. Then based on the compressed SSG and indices, we propose a new efficient and effective approximation algorithm, called SSG-MCBA by adopting the Monte Carlo method and our optimization search strategies. The experiments conducted onto two real social network datasets illustrate that SSG-MCBA greatly outperforms the state-of-the-art method in both efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Graph Pattern Matching (GPM) is to find those subgraphs that match a given pattern graph. In many applications, users are interested in the top-k nodes that matches the label of a specific node, (named as the designated node vd) included in a given pattern graph, rather than the entire set of matching. This is called Graph Pattern based Node Matching (GPNM) problem. However, the existing GPM methods for matching the designated node vd in social graphs do not consider the social contexts like the social relationships, the social trust and the social positions which commonly exist in real applications, like the experts recommendation in social graphs, leading to deliver low quality designated nodes. In this paper, we first propose the conText-Aware Graph pattern based Top-K designed nodes finding problem (TAG-K), which involves the NP-Complete Multiple Constrained GPM problem, and thus it is NP-Complete. To address the efficiency and effectiveness issues of TAG-K in large-scale social graphs, we propose two indices, MA-Tree and SSC-Index, which can help efficiently find the Top-K matching. Furthermore, we propose a probabilistic algorithm based on the Monte Carlo Method, called MC-TAG-K. Based on the experimental results on five real social graphs, we have demonstrated that MC-TAG-K outperforms the existing methods in both efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

Graph Pattern Matching (GPM) plays a significant role in many real applications, where many applications often need to find Top-K matches of a specific node, (named as the designated node vd) based on a pattern graph, rather than the entire set of matching. However, the existing GPM methods for matching the designated node vd in social graphs do not consider the social contexts like the social relationships, the social trust and the social positions which commonly exist in real applications, like the experts recommendation in social graphs, leading to deliver low quality designated nodes. In this paper, we first propose the conText-Aware Graph pattern based Top-K designed nodes finding problem (TAG-K), which involves the NP-Complete Multiple Constrained GPM problem, and thus it is NP-Complete. To address the efficiency and effectiveness issues of TAG-K in large-scale social graphs, we propose two indices, MA-Tree and SSC-Index, which can help efficiently find the Top-K matching. Furthermore, we propose an approximation algorithm, A-TAG-K. Using real social network datasets, we experimentally verify that A-TAG-K outperforms the existing methods in both efficiency and effectiveness for solving the TAG-K problem. [ABSTRACT FROM AUTHOR]

The goal of this special issue is to solicit high quality, original research contributions on all aspects of trust, privacy and security on crowdsourcing computing, thereby capturing the state of the art and stimulating further developments in the related areas. Paper "A Low Cost and Un-Cancelled Laplace Noise Based Differential Privacy Algorithm for Spatial Decompositions" by Xiaocui Li, Yangtao Wang, Jingkuan Song, Yu Liu, Xinyu Zhang, Ke Zhou, and Chunhua Li studies the security problem of applying differential privacy into spatial docomposition. Paper "Distributed Time-respecting Flow Graph Pattern Matching on Temporal Graphs" by Tianming Zhang, Yunjun Gao, Linshan Qiu, Lu Chen, Qingyuan Linghu, and Shiling Pu explores the problem of distributed graph pattern matching in temporal graphs. [Extracted from the article]

In recent years, social networking sites have been used as a means for a rich variety of activities, such as movie recommendations and product recommendations. In order to evaluate the trust between a truster (i.e., the source) and a trustee (i.e., the target) who have no direct interaction in Online Social Networks (OSNs), the trust network between them that contains important intermediate participants, the trust relations between the participants, and the social context, has an important influence on trust evaluation. Thus, to deliver a reasonable trust evaluation result, before performing any trust evaluation (i.e., trust transitivity), the contextual trust network from a given source to a given target needs to be first extracted from the social network, where constraints on social context should also be considered to guarantee the quality of the extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this challenging problem, we first present a contextual trust-oriented social network structure which takes social contextual impact factors, including trust, social intimacy degree, community impact factor, preference similarity and residential location distance into account. These factors have significant influences on both social interactions between participants and trust evaluation. Then, we present a new concept QoTN (Quality of Trust Network) and propose a social context-aware trust network extraction model. Finally, we propose a Heuristic Social Context-Aware trust Network extraction algorithm (H-SCAN-K) by extending the K-Best-First Search (KBFS) method with several proposed optimization strategies. The experiments conducted on two real datasets illustrate that our proposed model and algorithm outperform the existing methods in both algorithm efficiency and the quality of the extracted trust networks. [ABSTRACT FROM AUTHOR]

Crowdsourcing applications like Amazon Mechanical Turk (AMT) make it possible to address many difficult tasks (e.g., image tagging and sentiment analysis) on the internet and make full use of the wisdom of crowd, where worker quality is one of the most crucial issues for the task owners. Thus, a challenging problem is how to effectively and efficiently select the high quality workers, so that the tasks online can be accomplished successfully under a certain budget. The existing methods on the crowd worker selection problem mainly based on the quality measurement of the crowd workers, those who have to register on the crowdsourcing platforms. With the connect of the OSNs and the crowdsourcing applications, the social contexts like social relationships and social trust between participants and social positions of participants can assist requestors to select one or a group of trustworthy crowdsourcing workers. In this paper, we first present a contextual social network structure and a concept of Strong Social Component (SSC), which emblems a group of workers who have high social contexts values. Then, we propose a novel index for SSC, and a new efficient and effective algorithm C-AWSA to find trustworthy workers, who can complete the tasks with high quality. The results of our experiments conducted on four real OSN datasets illustrate that the superiority of our method in trustworthy worker selection. [ABSTRACT FROM AUTHOR]

A location-aware publish/subscribe (pub/sub) system is gaining more and more interest in both industry and academia with the rapid progress of mobile Internet and the rising popularity of smart-phones. Nowadays, with the booming of E-commerce, OTO (online-to-offline) services are gaining more and more popularity, which results in millions of products with different structured descriptions and locations. To meet this requirement, a pub/sub system should handle subscriptions with location-aware boolean expressions to present users' interests. In this paper, we propose an efficient location-aware pub/sub index for boolean expressions, called RP-trees. RP-trees integrates an R-tree index and a boolean expression index together, can efficiently and simultaneously prune boolean expressions and spatial dimensions. RP-trees is also extensible to support complex environment such as prefix-matching and subscriptions in format of CNF and DNF. Our experimental results show that RP-trees achieves good performance on a synthetic dataset and two real-world datasets (58 city and ebay). [ABSTRACT FROM AUTHOR]

The proliferation of GPS-enabled smart mobile devices enables us to collect a large-scale trajectories of moving objects with GPS tags. While the raw trajectories that only consists of positional information have been studied extensively, many recent works have been focusing on enriching the raw trajectories with semantic knowledge. The resulting data, called activity trajectories, embed the information about behaviors of the moving objects and support a variety of applications for better quality of services. In this paper, we propose a Top-k Spatial Keyword (T kSK) query for activity trajectories, with the objective to find a set of trajectories that are not only close geographically but also meet the requirements of the query semantically. Such kind of query can deliver more informative results than existing spatial keyword queries for static objects, since activity trajectories are able to reflect the popularity of user activities and reveal preferable combinations of facilities. However, it is a challenging task to answer this query efficiently due to the inherent difficulties in indexing trajectories as well as the new complexity introduced by the textual dimension. In this work, we provide a comprehensive solution, including the novel similarity function, hybrid indexing structure, efficient search algorithm and further optimizations. Extensive empirical studies on real trajectory set have demonstrated the scalability of our proposed solution. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of GPS-equipped devices, a myriad of trajectory data representing the mobility of various moving objects in two-dimensional space have been generated. This paper aims to detect the anomalous trajectories with the help of the historical trajectory dataset and the popular routes. In this paper, both of spatial and temporal abnormalities are taken into consideration simultaneously to improve the accuracy of the detection. Previous work has developed a novel time-dependent popular routes based algorithm named TPRO. TPRO focuses on finding out all outliers in the historical trajectory dataset. But in most cases, people do not care about which trajectory in the dataset is abnormal. They only yearn for the detection result of a new trajectory that is not included in the dataset. So this paper develops the the upgrade version of TPRO, named TPRRO. TPRRO is a real-time outlier detection algorithm and it contains the off-line preprocess step and the on-line detection step. In the off-line preprocess step, TTI (short for time-dependent transfer index) and hot TTG (short for time-dependent transfer graph) cache are constructed according to the historical trajectory dataset. Then in the on-line detection step, TTI and hot TTG cache are used to speed up the detection progress. The experiment result shows that TPRRO has a better efficiency than TPRO in detecting outliers. [ABSTRACT FROM AUTHOR]

Time synchronization is a critical issue for Underwater Wireless Sensor Networks (UWSN). Although numerous time synchronization algorithms have been proposed for terrestrial wireless sensor networks, none of them could be applied to UWSN directly. This is because underwater time synchronization offers two fundamental challenges compared with terrestrial wireless sensor networks, i.e., (1) long propagation delay and (2) sensor node mobility. We propose a novel time synchronization approach called Coll-Sync to address the above challenging problems, which is a pair-wise collision-based time synchronization scheme for mobile underwater acoustic sensor networks. Coll-Sync adopts collision and packet meetings in a long propagation process to eliminate the effect of node mobility. In this paper, we indicate that collision and packet meetings contain highly useful information that can be leveraged to improve time synchronization. Through comprehensive experiments, we show that our proposed Coll-Sync greatly outperforms existing time synchronization schemes in both precision and energy saving. [ABSTRACT FROM AUTHOR]

Gang scheduling combines time-sharing with space-sharing to ensure a short response time for interactive tasks and high overall system throughput. It has been widely studied in different areas including the Grid. Gang scheduling tries to assign the task belonging to one job to different Grid nodes. During the tasks assignment, there are three targets as follows: (1) to keep the Grid in higher resource utilization, (2) to keep the jobs in a low average waiting time and executing time, and, (3) to keep the system in fairness between jobs. In order to meet these targets, we propose a new model according to the waiting time of the jobs. Then we propose a new scheduling method ZERO-ONE scheduling with multiple targets (ZEROONEMT) to solve the Gang scheduling in the Grid. We have conducted extensive evaluations to compare our method with the existing methods based on a simulation environment and a real log from a Grid. In the experiments, in order to justify our method, different metrics, including adapted first come first served and largest job first served, are selected to test the performance of our methods. Experimental results illustrate that our proposed ZEROONEMT reduces the values in the average waiting time, the average response time, and the standard deviation of waiting time of all the jobs. [ABSTRACT FROM AUTHOR]

In Service-Oriented Computing environments, there is a large number of service providers providing a variety of services to service customers. Conventional recommender systems, which adopt the information filtering techniques, can be used to automatically generate recommendations of service providers to service customers who are also the system users. However, data sparsity and trust enhancement are the traditional problems in recommender systems. Targeting the data sparsity problem, recent studies on recommender systems have started to leverage information from online social networks to collect recommendations from more participants and derive the final recommendation. However, this requires the methods to infer the trust between participants without any direct interactions in online social networks, which should take into account both the social context of participants and the context of the target services to be recommended, for trust enhanced recommendations. In this paper, we first present a contextual social network model that takes into account both participants' personal characteristics (referred to as the independent social context, including preference and expertise in domains) and mutual relations (referred to as the dependent social context, including the trust, social intimacy, and interaction context between two participants). In addition, we propose a new probabilistic approach, SocialTrust, as the first solution in the literature, to social context-aware trust inference in social networks. The result delivered by this approach is particularly important in evaluating the trust from a source participant to an end recommender who recommends a target service or service provider, via the sub-network consisting of intermediate participants/recommenders between them and relevant contextual information. Moreover, we propose algorithms that consider cycles and information updates in social networks. Experiments demonstrate that our approach is effective and superior to existing trust inference methods, and can deliver more reasonable and trustworthy results. The proposed algorithms considering cycles and information updates in social networks are efficient and applicable to real social networks. [ABSTRACT FROM AUTHOR]