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Social networks are a vital mechanism to disseminate information to friends and colleagues. In this work, we investigate an important problem—the personalized influential topic search, or PIT-Search in a social network: Given a keyword query $q$ issued by a user $u$ in a social network, a PIT-Search is to find the top-$k$ $q$-related topics that are most influential for the query user $u$. The influence of a topic to a query user depends on the social connection between the query user and the social users containing the topic in the social network. To measure the topics’ influence at the similar granularity scale, we need to extract the social summarization of the social network regarding topics. To make effective topic-aware social summarization, we propose two random-walk based approaches: random clustering and an L-length random walk. Based on the proposed approaches, we can find a small set of representative users with assigned influential scores to simulate the influence of the large number of topic users in the social network with regards to the topic. The selected representative users are denoted as the social summarization of topic-aware influence spread over the social network. And then, we verify the usefulness of the social summarization by applying it to the problem of personalized influential topic search. Finally, we evaluate the performance of our algorithms using real-world datasets, and show the approach is efficient and effective in practice. [ABSTRACT FROM PUBLISHER]

Graphs have been widely used to represent objects and object connections in applications such as the web, social networks, and citation networks. Mining influence relationships from graphs has gained increasing interests in recent years because providing information on how graph objects influence each other can facilitate graph exploration, graph search, and connection recommendations. In this paper, we study the problem of detecting influence aspects, on which objects are connected, and influence degree (or influence strength), with which one graph node influences another graph node on a given aspect. Existing techniques focus on inferring either the overall influence degrees or the influence types from graphs. In this paper, we propose a systematic approach to extract influence aspects and learn aspect-level influence strength. In particular, we first present a novel instance-merging based method to extract influence aspects from the context of object connections. We then introduce two generative models, Observed Aspect Influence Model (OAIM) and Latent Aspect Influence Model (LAIM), to model the topological structure of graphs, the text content associated with graph objects, and the context in which the objects are connected. To learn OAIM and LAIM, we design both non-parallel and parallel Gibbs sampling algorithms. We conduct extensive experiments on synthetic and real data sets to show the effectiveness and efficiency of our methods. The experimental results show that our models can discover more effective results than existing approaches. Our learning algorithms also scale well on large data sets. [ABSTRACT FROM PUBLISHER]

Expert finding has become a hot topic along with the flourishing of social networks, such as micro-blogging services like Twitter. Finding experts in Twitter is an important problem because tweets from experts are valuable sources that carry rich information (e.g., trends) in various domains. However, previous methods cannot be directly applied to Twitter expert finding problem. Recently, several attempts use the relations among users and Twitter Lists for expert finding. Nevertheless, these approaches only partially utilize such relations. To this end, we develop a probabilistic method to jointly exploit three types of relations (i.e., follower relation, user-list relation, and list-list relation) for finding experts. Specifically, we propose a Semi-Supervised Graph-based Ranking approach (\sfSSGR) to offline calculate the global authority of users. In \sfSSGR, we employ a normalized Laplacian regularization term to jointly explore the three relations, which is subject to the supervised information derived from Twitter crowds. We then online compute the local relevance between users and the given query. By leveraging the global authority and local relevance of users, we rank all of users and find top-N users with highest ranking scores. Experiments on real-world data demonstrate the effectiveness of our proposed approach for topic-specific expert finding in Twitter. [ABSTRACT FROM PUBLISHER]

In this work, we study the problem of clearing contamination spreading through a large network where we model the problem as a graph searching game. The problem can be summarized as constructing a search strategy that will leave the graph clear of any contamination at the end of the searching process in as few steps as possible. We show that this problem is NP-hard even on directed acyclic graphs and provide an efficient approximation algorithm. We experimentally observe the performance of our approximation algorithm in relation to the lower bound on several large online networks including Slashdot, Epinions, and Twitter. [ABSTRACT FROM PUBLISHER]

Although $k$ NN search on a road network $G_r$ , i.e., finding $k$ nearest objects to a query user $q$ on $G_r$ , has been extensively studied, existing works neglected the fact that the $q$ 's social information can play an important role in this $k$ NN query. Many real-world applications, such as location-based social networking services, require such a query. In this paper, we study a new problem: $k$ NN search on road networks by incorporating social influence (RSkNN). Specifically, the state-of-the-art Independent Cascade (IC) model in social network is applied to define social influence. One critical challenge of the problem is to speed up the computation of the social influence over large road and social networks. To address this challenge, we propose three efficient index-based search algorithms, i.e., road network-based (RN-based), social network-based (SN-based), and hybrid indexing algorithms. In the RN-based algorithm, we employ a filtering-and-verification framework for tackling the hard problem of computing social influence. In the SN-based algorithm, we embed social cuts into the index, so that we speed up the query. In the hybrid algorithm, we propose an index, summarizing the road and social networks, based on which we can obtain query answers efficiently. Finally, we use real road and social network data to empirically verify the efficiency and efficacy of our solutions. [ABSTRACT FROM PUBLISHER]

Recently, location-based social networks (LBSNs) gave the opportunity to users to share geo-tagged information along with photos, videos, and SMSs. Recommender systems can exploit this geographic information to provide much more accurate and reliable recommendations to users. In this paper, we present and compare 16 real life LBSNs, bringing into surface their advantages/disadvantages, their special functionalities, and their impact in the mobile social Web. Moreover, we describe and compare extensively 43 state-of-the-art recommendation algorithms for LBSNs. We categorize these algorithms according to: personalization type, recommendation type, data factors/features, problem modeling methodology, and data representation. In addition to the above categorizations which cannot cover all algorithms in an integrated way, we also propose a hybrid $k$ -partite graph taxonomy to categorize them based on the number of the involved $k$ -partite graphs. Finally, we compare the recommendation algorithms with respect to their evaluation methodology (i.e., datasets and metrics) and we highlight new perspectives for future work in LBSNs. [ABSTRACT FROM PUBLISHER]

Social media networks are dynamic. As such, the order in which network ties develop is an important aspect of the network dynamics. This study proposes a novel dynamic network model, the Nodal Attribute-based Temporal Exponential Random Graph Model (NATERGM) for dynamic network analysis. The proposed model focuses on how the nodal attributes of a network affect the order in which the network ties develop. Temporal patterns in social media networks are modeled based on the nodal attributes of individuals and the time information of network ties. Using social media data collected from a knowledge sharing community, empirical tests were conducted to evaluate the performance of the NATERGM on identifying the temporal patterns and predicting the characteristics of the future networks. Results showed that the NATERGM demonstrated an enhanced pattern testing capability and an increased prediction accuracy of network characteristics compared to benchmark models. The proposed NATERGM model helps explain the roles of nodal attributes in the formation process of dynamic networks. [ABSTRACT FROM PUBLISHER]

With the rise of social networks, large-scale graph analysis becomes increasingly important. Because SQL lacks the expressiveness and performance needed for graph algorithms, lower-level, general-purpose languages are often used instead. For greater ease of use and efficiency, we propose SociaLite, a high-level graph query language based on Datalog. As a logic programming language, Datalog allows many graph algorithms to be expressed succinctly. However, its performance has not been competitive when compared to low-level languages. With SociaLite, users can provide high-level hints on the data layout and evaluation order; they can also define recursive aggregate functions which, as long as they are meet operations, can be evaluated incrementally and efficiently. Moreover, recursive aggregate functions make it possible to implement more graph algorithms that cannot be implemented in Datalog. We evaluated SociaLite by running nine graph algorithms in total; eight for social network analysis (shortest paths, PageRank, hubs and authorities, mutual neighbors, connected components, triangles, clustering coefficients, and betweenness centrality) and one for biological network analysis (Eulerian cycles). We use two real-life social graphs, LiveJournal and Last.fm, for the evaluation as well as one synthetic graph. The optimizations proposed in this paper speed up almost all the algorithms by 3 to 22 times. SociaLite even outperforms typical Java implementations by an average of 50 percent for the graph algorithms tested. When compared to highly optimized Java implementations, SociaLite programs are an order of magnitude more succinct and easier to write. Its performance is competitive, with only 16 percent overhead for the largest benchmark, and 25 percent overhead for the worst case benchmark. Most importantly, being a query language, SociaLite enables many more users who are not proficient in software engineering to perform network analysis easily and efficiently. [ABSTRACT FROM PUBLISHER]