Centrality Measures, Upper Bound, and Influence Maximization in Large Scale Directed Social Networks
TL;DR: Two new centrality measures, Diffusion Degree for independent cascade model of information diffusion and Maximum Influence Degree are proposed, which provide the maximum theoretically possible influence Upper Bound for a node.
Abstract: The paper addresses the problem of finding top k influential nodes in large scale directed social networks. We propose two new centrality measures, Diffusion Degree for independent cascade model of information diffusion and Maximum Influence Degree. Unlike other existing centrality measures, diffusion degree considers neighbors' contributions in addition to the degree of a node. The measure also works flawlessly with non uniform propagation probability distributions. On the other hand, Maximum Influence Degree provides the maximum theoretically possible influence Upper Bound for a node. Extensive experiments are performed with five different real life large scale directed social networks. With independent cascade model, we perform experiments for both uniform and non uniform propagation probabilities. We use Diffusion Degree Heuristic DiDH and Maximum Influence Degree Heuristic MIDH, to find the top k influential individuals. k seeds obtained through these for both the setups show superior influence compared to the seeds obtained by high degree heuristics, degree discount heuristics, different variants of set covering greedy algorithms and Prefix excluding Maximum Influence Arborescence PMIA algorithm. The superiority of the proposed method is also found to be statistically significant as per T-test.
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TL;DR: An organizing scheme for single‐topic user groups is proposed for facilitating user sharing and communicating under common interests and contains 3 features: topic impact evaluation, interest degree measurement, and trust chain‐based organizing.
Abstract: Summary
Social network sites (SNS) presently face the task of grouping users into small subsets within themselves. In this study, an organizing scheme for single-topic user groups is proposed for facilitating user sharing and communicating under common interests. The main rationales of the proposed scheme are (1) only an influential single topic is selected through its impact evaluation to attract users; (2) only the users having high degree of interest, explicit or implicit, on the topic should be grouped; and (3) trustworthy relationships among users are taken into consideration to enlarge the scale of user group. The proposed organizing scheme comprises 3 features: topic impact evaluation, interest degree measurement, and trust chain-based organizing. The main structure of our proposed scheme is (1) an overview of the proposed scheme and its formal related definitions; (2) a topic impact evaluation method, ie, an importance evaluation and a popularity calculation; (3) a user interest degree measurement method, ie, explicit and implicit interest evaluation with dynamic factors included; (4) a trust chain calculation method based on the topology features of the trust chain; (5) an organizing algorithm for single topic user group, and finally, some experimental results and discussions to illustrate the effectiveness and feasibility of our scheme.
70 citations
TL;DR: An effective discrete shuffled frog-leaping algorithm (DSFLA) is proposed to solve influence maximization problem in a more efficient way and is superior than several state-of-the-art alternatives.
Abstract: Influence maximization problem aims to select a subset of k most influential nodes from a given network such that the spread of influence triggered by the seed set will be maximum. Greedy based algorithms are time-consuming to approximate the expected influence spread of given node set accurately and not well scalable to large-scale networks especially when the propagation probability is large. Conventional heuristics based on network topology or confined diffusion paths tend to suffer from the problem of low solution accuracy or huge memory cost. In this paper an effective discrete shuffled frog-leaping algorithm (DSFLA) is proposed to solve influence maximization problem in a more efficient way. Novel encoding mechanism and discrete evolutionary rules are conceived based on network topology structure for virtual frog population. To facilitate the global exploratory solution, a novel local exploitation mechanism combining deterministic and random walk strategies is put forward to improve the suboptimal meme of each memeplex in the frog population. The experimental results of influence spread in six real-world networks and statistical tests show that DSFLA performs effectively in selecting targeted influential seed nodes for influence maximization and is superior than several state-of-the-art alternatives.
69 citations
TL;DR: The possibilities of the linear threshold model for the definition of centrality measures to be used on weighted and labeled social networks are explored and a new centrality measure to rank the users of the network, the Linear Threshold Rank (LTR), and a centralization measure to determine to what extent the entire network has a centralized structure are explored.
Abstract: Centrality and influence spread are two of the most studied concepts in social network analysis. In recent years, centrality measures have attracted the attention of many researchers, generating a large and varied number of new studies about social network analysis and its applications. However, as far as we know, traditional models of influence spread have not yet been exhaustively used to define centrality measures according to the influence criteria. Most of the considered work in this topic is based on the independent cascade model. In this paper we explore the possibilities of the linear threshold model for the definition of centrality measures to be used on weighted and labeled social networks. We propose a new centrality measure to rank the users of the network, the Linear Threshold Rank (LTR), and a centralization measure to determine to what extent the entire network has a centralized structure, the Linear Threshold Centralization (LTC). We appraise the viability of the approach through several case studies. We consider four different social networks to compare our new measures with two centrality measures based on relevance criteria and another centrality measure based on the independent cascade model. Our results show that our measures are useful for ranking actors and networks in a distinguishable way.
50 citations
TL;DR: Experimental results on benchmark data show the superiority of the proposed community detection algorithm compared to other well known methods, particularly when the network contains overlapping communities.
47 citations
TL;DR: A two level approach, designed based on Suspected-Infected (SI) epidemic model for maximizing the influence spread, and multithreading approach for implementation of algorithm for the proposed SI model aids to further elevate the performance of proposed algorithm in terms of influence spread per second.
42 citations
References
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28 Jun 2009
TL;DR: In this paper, the existence of an influential agent corresponds to strictly positive information theoretic capacity over an infinite-sized noisy broadcast tree network in the first impression case, and positive recurrent property of an appropriate (countable state space) Markov chain in the long-term case.
Abstract: Motivated by the recent emergence of large online social networks, we seek to understand the effects the underlying social network (graph) structure and the information dynamics have on the creation of influence of an individual. We examine a natural model for information dynamics under two important temporal scales: a first impression setting and a long— term or equilibrated setting. We obtain a characterization of relevant network structures under these temporal aspects, thereby allowing us to formalize the existence of influential agents. Specifically, we find that the existence of an influential agent corresponds to: (a) strictly positive information theoretic capacity over an infinite-sized noisy broadcast tree network in the first impression case, and (b) positive recurrent property of an appropriate (countable state space) Markov chain in the long-term case. As an application of our results, we evaluate the parameter space of the popular “small world” network model to identify when the network structure supports the existence of influential agents.
2 citations