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An event detection method for social networks based on link prediction

机译：基于链接预测的社交网络事件检测方法

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摘要

In the field of social network analysis, network evolution and event detection are the main current challenges. To meet them, current research work proposed many different models based on different network evolutions, and then evaluated the simulation results with different similarity indexes. However, the previous work usually had three problems: (1) Each method is only designed for a particular network; (2) There are many network statistics, so the evaluation on the performances of network models lacks an unified platform; (3) Without considering the temporal information, it is hard to track the network evolution and detect events. This paper proposes an event detection method for social networks based on link prediction, which can also evaluate the volatility of different networks. The main contributions are as follows. (1) The volatility of the network evolution can be effectively reflected on the similarity index corresponding to a good link prediction performance. (2) Inspired by link prediction, a similarity computing algorithm (SimC) is proposed to compute the similarity of networks. (3) Based on the output of SimC, the volatility of network evolution is evaluated and an event detection algorithm (EventD) is proposed. The experimental results show that the proposed method can effectively solve the problem of tracking network evolutions and detecting events. (C) 2017 Elsevier Ltd. All rights reserved.
机译：在社交网络分析领域，网络发展和事件检测是当前的主要挑战。为了满足这些要求，当前的研究工作基于不同的网络演进提出了许多不同的模型，然后使用不同的相似性指标评估了仿真结果。但是，先前的工作通常存在三个问题：（1）每种方法仅针对特定网络而设计； （2）网络统计数据众多，因此对网络模型性能的评估缺乏统一的平台； （3）如果不考虑时间信息，就很难跟踪网络演化和检测事件。提出了一种基于链接预测的社交网络事件检测方法，该方法还可以评估不同网络的波动性。主要贡献如下。 （1）网络演化的波动性可以有效地反映在与良好的链路预测性能相对应的相似性指标上。 （2）受链路预测的启发，提出了一种相似度计算算法（SimC）来计算网络的相似度。 （3）基于SimC的输出，评估了网络演化的易变性，并提出了一种事件检测算法（EventD）。实验结果表明，该方法可以有效解决跟踪网络演化和事件检测的问题。 （C）2017 Elsevier Ltd.保留所有权利。

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