? 一种有效使用社交行为计算社交网络中影响力节点的两相模型
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (2) :286-304    DOI: 10.1007/s11390-018-1820-9
Artificial Intelligence and Pattern Recognition << Previous Articles | Next Articles >>
一种有效使用社交行为计算社交网络中影响力节点的两相模型
Mehdi Azaouzi, Lotfi Ben Romdhane
Modeling of Automated Reasoning Systems Research Laboratory LR17 ES05 Higher Institute of Computer Science and Telecom, University of Sousse, Sousse 526-4002, Tunisia
An Efficient Two-Phase Model for Computing Influential Nodes in Social Networks Using Social Actions
Mehdi Azaouzi, Lotfi Ben Romdhane
Modeling of Automated Reasoning Systems Research Laboratory LR17 ES05 Higher Institute of Computer Science and Telecom, University of Sousse, Sousse 526-4002, Tunisia

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摘要 社交网络中的影响力度量在数据挖掘社区引起了广泛关注。影响最大化是指寻找有影响力的用户的过程,这些用户能最大化地利用信息或产品。在现实环境中,一个用户在某个社交网络的影响可以通过此社交网络的其他用户在其发表的事物上表现的一系列行为(如,点赞,分享,转发,评论)来建立模型。据我们所知,现有模型中对这些行为同等对待。然而,很显然,对某发表"点赞"的影响力比不上对它"分享"。这说明每个行为的影响(或重要性)有它的等级。本文针对社交网络中影响力最大化提出了一个模型:基于社会行为的影响力最大化模型,SAIM。在SAIM中,对某个个体的影响力进行度量时,不同行为不会同等对待,并且此过程分为两个主要步骤。首先,我们计算社交网络中每个个体的影响力,这个主要通过使用PageRank分析用户行为实现。最终我们得到一个加权社会网络,在此,每个节点由其影响力标记。接着,我们使用一个新概念"influence-BFS tree"计算一组优化的影响力节点。对大规模现实和合成社交网络上的实验表明,在可接受的时间范围内,我们的模型SAIM在计算最小一组影响力节点时性能良好,此组节点可最大化传播信息。
关键词社交网络   社会影响   社会行为   个性化PageRank   influence-BFS tree     
Abstract: The measurement of influence in social networks has received a lot of attention in the data mining community. Influence maximization refers to the process of finding influential users who make the most of information or product adoption. In real settings, the influence of a user in a social network can be modeled by the set of actions (e.g., "like", "share", "retweet", "comment") performed by other users of the network on his/her publications. To the best of our knowledge, all proposed models in the literature treat these actions equally. However, it is obvious that a "like" of a publication means less influence than a "share" of the same publication. This suggests that each action has its own level of influence (or importance). In this paper, we propose a model (called Social Action-Based Influence Maximization Model, SAIM) for influence maximization in social networks. In SAIM, actions are not considered equally in measuring the "influence power" of an individual, and it is composed of two major steps. In the first step, we compute the influence power of each individual in the social network. This influence power is computed from user actions using PageRank. At the end of this step, we get a weighted social network in which each node is labeled by its influence power. In the second step of SAIM, we compute an optimal set of influential nodes using a new concept named "influence-BFS tree". Experiments conducted on large-scale real-world and synthetic social networks reveal the good performance of our model SAIM in computing, in acceptable time scales, a minimal set of influential nodes allowing the maximum spreading of information.
Keywordssocial network   social influence   social action   personalized PageRank   influence-BFS tree     
Received 2016-12-13;
About author: Mehdi Azaouzi is currently a Ph.D. student at the National School of Computer Sciences, University of Manouba, Tunisia. He received his Bachelor's degree and his Master's degree from the Faculty of Sciences, University of Monastir, Tunisia, both in computer science, in 2009 and 2012, respectively. His current research interests include social networks analyses, graph mining, and graph indexing. He is member of the MARS (Modeling of Automated Reasoning Systems) Research Laboratory
引用本文:   
Mehdi Azaouzi, Lotfi Ben Romdhane.一种有效使用社交行为计算社交网络中影响力节点的两相模型[J]  Journal of Computer Science and Technology , 2018,V33(2): 286-304
Mehdi Azaouzi, Lotfi Ben Romdhane.An Efficient Two-Phase Model for Computing Influential Nodes in Social Networks Using Social Actions[J]  Journal of Computer Science and Technology, 2018,V33(2): 286-304
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