Influence-oriented community analysis in social networks

The emergence of online social networks has fundamentally changed the way people communicate with each other. Scholars have never ceased devoting their time and energy to the phenomenon since its emergence. Among researches around the social network, One line of study that draws a significant amount of attention recently is the discovery of communities, i.e., relatively densely connected sub-networks. Discovering such structures or communities provides insight into the relationship between individuals and composition of a social network. However, these studies mainly focus on the inner connection between individuals inside a community structure and neglect the external influence of a community as a whole. Another line of study in the field of the social network is influence analysis which analyze the ability of individuals to convince other users to adopt a new product (or an innovative idea, a service, a political opinion, etc.) with word-of-mouth effect which propagates information through network structures that can trigger cascades of further adoptions. However, these studies mainly focus on the relationship between individuals and the information diffusion process and neglect the community structures in a social network. There is a lack of studies that analyze the social influence of communities, which is fundamentally important for understanding the relationship between network structures and the information diffusion among it and has many practical applications. For example, a company may try to find the most influential community to advertise their products; an organization may intend to initiate a campaign in hope to attract more diverse customers, i.e., maximizing the number of influenced communities instead of customers; an association may hope to minimize the influence of a malicious information spread by one of its opponents, so that the community consisted of its core customers would be affected the least. To fill in this meaningful blank, in this thesis, we intend to analyze communities on the aspect of social influence and solve three research questions as follows. First, how to identify the communities with the dense intra-connections and the highest outer influence on the users outside the communities? Second, how to maximize both the spread and the diversity of the diffusion at the end of the information propagation by selecting a fixed number of influential users from a social network to spread the information. The higher diversity means more communities are influenced. Third, how to minimize the influence of a set of initial active nodes, which has been infected by a piece of malicious information, over a target community? The aim is to protect from this disinformation, by deleting a fixed number of edges in a social network. To address the first research question, we propose a new metric to measure the likelihood of the community to attract the other users outside the community within the social network, i.e., the community's outer influence. There are lots of applications that need to rank the communities using their outer influence, e.g., Ads trending analytics, social opinion mining and news propagation pattern discovery by monitoring the influential communities. We refer to such problem as Most Influential Community Search. While the most influential community search problem in large social networks is essential in various applications, it is mostly ignored by the academic research community. In this work, we systematically investigate this problem. Firstly, we propose a new community model, maximal kr-Clique community, which has desirable characters, i.e., society, cohesiveness, connectivity, and maximum. And then, we developed a novel tree-based index structure, denoted as C-Tree, to maintain the offline computed r-cliques. To efficiently search the most influential maximal kr-clique communities with the maximum outer influence, we developed four advanced index-based algorithms, which can improve the search performance of non-indexed solution by about 200 times. The efficiency and effectiveness of constructing index structure and evaluating the search algorithms have been verified using six real datasets including Facebook, Google+, Gowalla, Twitter, Youtube, and Amazon. A small case study shows the value of the most influential communities using DBLP data. To solve the second research question, we investigate Diverse Influence Maximization (DIM) to efficiently find k nodes which, at the end of propagation process, can maximize the number of activated nodes and the diversity of the activated nodes. In this work, an evaluation metric has been proposed to balance the two objectives. To address the computational challenges, we develop two efficient algorithms and one advanced PSP-Tree index. The effectiveness and efficiency of our DIM solution are verified by the extensive experimental studies on five real-world social network datasets. To address the last research question, we study the community-targeted influence minimization problem. Unlike previous influence minimization work, this study considers the influence minimization concerning a particular group of social network users, called targeted influence minimization. Thus, the objective is to protect a set of users, called target nodes, from malicious information originating from another group of users, called active nodes. This study also addresses two fundamental, but largely ignored, issues in different influence minimization problems: (i) the impact of a budget on the solution; (ii) robust sampling. To this end, two scenarios are investigated, namely unconstrained and constrained budget. Given an unconstrained budget, we provide an optimal solution; Given a constrained budget, we show the problem is NP-hard and develop a greedy algorithm with an (1 − 1/e)-approximation. More importantly, to solve the influence minimization problem in large, real-world social networks, we propose a robust sampling-based solution with a desirable theoretic bound. Extensive experiments using real social network datasets offer insight into the effectiveness and efficiency of the proposed solutions

Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks

Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called "Collective Influence (CI)" has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes' significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct "virtual" information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes' importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.Comment: 11 pages, 4 figure

Searching for superspreaders of information in real-world social media

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation

Interest communities and flow roles in directed networks: the Twitter network of the UK riots

Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection in networks through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e., groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer, and topic. The study of flows also allows us to generate an interest distance, which affords a personalised view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterised by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks.Comment: 32 pages, 14 figures. Supplementary Spreadsheet available from: http://www2.imperial.ac.uk/~mbegueri/Docs/riotsCommunities.zip or http://rsif.royalsocietypublishing.org/content/11/101/20140940/suppl/DC