ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2016, Vol. 53 ›› Issue (11): 2645-2653.doi: 10.7544/issn1000-1239.2016.20150388

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Multi-User Location Proximity Prediction in Offline Ephemeral Social Networks

Liao Guoqiong1,2, Wang Tingli1, Deng Kun1,2, Wan Changxuan1,2   

  1. 1(School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013); 2(Jiangxi Province Key Laboratory of Data and Knowledge Engineering (Jiangxi University of Finance and Economics), Nanchang 330013)
  • Online:2016-11-01

Abstract: Offline ephemeral social network (OffESN) is defined as a new kind of offline social networks created at a specific location for a specific purpose temporally, and lasting for a short period of time. With the popularity of mobile intelligent terminals and the development of short distance communication technologies such as Bluetooth and RFID, the OffESN is receiving more and more attentions from industry and academic communities. Location proximity relations are encounter relations of the users in the OffESN. Aiming to the characteristics such as dynamic change and short duration time, this paper intends to study the problem of multi-user location proximity in the OffESN. First of all, the paper puts forward relevant concepts in the OffESN and defines the problem to be solved. Then, it designs the overall framework of multi-user location proximity prediction, including network segments collection, overlay networks construction, network filter and maximal close subgraphs discovery. Based on the framework and the splitting idea, the paper suggests a maximal close subgraph discovery algorithm for predicting multi-user location proximity. The algorithm uses weighted edge betweenness (WEB) as the basis of splitting, and uses the aggregate density as the termination condition of spitting, which can effectively solve the problem that both numbers of location proximity relations and the users in each location proximity are uncertain. Finally, the experiments on two real datasets verify the feasibility and efficiency of the suggested prediction strategy.

Key words: social networks, ephemeral social networks, location proximity, maximal close subgraphs, link prediction

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