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TPLP: Two-Phase Selection Link Prediction for Vertex in Graph Streams

  • Yang Xiao
  • Hong Huang
  • Feng ZhaoEmail author
  • Hai Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11440)

Abstract

Currently, data in many applications have naturally been modeled as streams over the massive graph infrastructure, e.g., social networks and electronic business. Graph streams are rapidly changing, enormous and endless networks that are too large to maintain in memory or on disks. An important problem in networks is link prediction, which aims to estimate the likelihood of the existence of a specific link. However, in graph streams, predicting the existence of links connected to one vertex is more common. For example, in social networks, we generally want to recommend several friends to a user rather than determining whether a specific user is your friend. Rapidly and accurately predicting groups of links becomes a formidable challenge because of the tremendous size and rapidly updated information of graph streams. In this paper, we propose the problem of link prediction for vertex in graph streams, which aims to predict the top-k vertices, i.e., the top-k links, that are most likely to connect to the target vertex in graph streams. A two-phase selection framework is proposed to predict top-k links with high efficiency and without loss of accuracy. We also propose a novel method for estimating common neighbor in graph streams, which is a very important measure in link prediction. Extensive experiments show that our algorithms are more efficient and more accurate than state-of-the-art methods.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Engineering Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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