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Journal of Software:2015.26(9):2339-2355

基于链路预测的社会网络事件检测方法
胡文斌,彭超,梁欢乐,杜博
(武汉大学 计算机学院, 湖北 武汉 430072;软件工程国家重点实验室(武汉大学), 湖北 武汉 430072)
Event Detection Method Based on Link Prediction for Social Network Evolution
HU Wen-Bin,PENG Chao,LIANG Huan-Le,DU Bo
(Computer School, Wuhan University, Wuhan 430072, China;State Key Laboratory of Software Engineering (Wuhan University), Wuhan 430072, China)
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Received:March 21, 2014    Revised:June 10, 2014
> 中文摘要: 网络演化分析与事件检测,是当前社会网络研究的热点和难点.现有的研究工作主要是针对网络提出不同的模型,并用网络特征指标对仿真结果进行评价.这些方法存在如下问题:(1) 每种方法仅针对特定网络,通用性不高;(2) 特征指标多种多样,不同模型的表现情况缺乏统一的评价标准;(3) 未考虑网络演化的时间特性,难以描述网络演化的波动性,无法检测事件.针对上述问题,提出一种基于链路预测的社会网络事件检测方法LinkEvent(由相似性计算算法SimC和事件检测算法EventD组成),它可以对不同网络的波动性进行统一评价,并依此建立事件检测模型.主要工作包括:(1) 证明了链路预测可以反映网络演化机制,相同机制下的模型演化法和链路预测在分析网络演化上具有内在的一致性;(2) 基于链路预测,提出一种网络相似性计算算法SimC(similar computing),并在考虑微观因素的基础上进行改进;(3) 利用相似性计算结果,提出一种事件检测算法EventD(event detecting)检测出新事件.在不同特征的网络上进行实验,结果表明:所提出的LinkEvent方法能够较好地解决网络演化波动性问题,实现事件检测;同时也证明了利用链路预测技术进行网络演化分析的可行性以及相似性计算和事件检测算法的有效性.
Abstract:Tracking the evolution and detecting events are popular and difficult problems in the field of social network analysis. Most of the research focuses on proposing different models to fit different network characteristics. This type of approach usually has three problems: (1) Each model is designed for one particular network and cannot well fit other networks; (2) There are many network statistics, so the evaluation of these network models lacks of unified platforms; (3) Without taking temporal information into account, these network models can hardly track the evolution and detect events. To solve these problems, this paper presents a method for event detection in social networks based on link prediction, which can evaluate the fluctuation of the networks and detect the events in social networks. The main work is as follow: (1) Demonstrates the method "modelling and evaluating" is in accord with link prediction on revealing the network evolution mechanism; (2) Proposes an algorithm similarity computing (SimC) to compute the similarity of networks and further improves this algorithm by taking micro factors into account; (3) Evaluates the fluctuation of the network evolution and proposes an event detecting (EventD) algorithm to detect the events. The results of the experiment show that the presented method can effectively solve the problem of tracking the evolution and detecting events.
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基金项目:国家自然科学基金(70901060, 61471274); 湖北省自然科学基金(2011CDB461); 软件工程国家重点实验室(武汉大学)开放基金(SKLSE 2010-08-15); 武汉市科技局青年晨光计划(201150431101); 武汉市科技重大计划项目(2015010101010023) 国家自然科学基金(70901060, 61471274); 湖北省自然科学基金(2011CDB461); 软件工程国家重点实验室(武汉大学)开放基金(SKLSE 2010-08-15); 武汉市科技局青年晨光计划(201150431101); 武汉市科技重大计划项目(2015010101010023)
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胡文斌,彭超,梁欢乐,杜博.基于链路预测的社会网络事件检测方法.软件学报,2015,26(9):2339-2355

HU Wen-Bin,PENG Chao,LIANG Huan-Le,DU Bo.Event Detection Method Based on Link Prediction for Social Network Evolution.Journal of Software,2015,26(9):2339-2355