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.
[1]
Peng Wang,et al.
Link prediction in social networks: the state-of-the-art
,
2014,
Science China Information Sciences.
[2]
A. Barabasi,et al.
Uncovering disease-disease relationships through the incomplete interactome
,
2015,
Science.
[3]
Charu C. Aggarwal,et al.
gSketch: On Query Estimation in Graph Streams
,
2011,
Proc. VLDB Endow..
[4]
Hsinchun Chen,et al.
Recommendation as link prediction in bipartite graphs: A graph kernel-based machine learning approach
,
2013,
Decis. Support Syst..
[5]
Andrew McGregor,et al.
Graph stream algorithms: a survey
,
2014,
SGMD.
[6]
Charu C. Aggarwal,et al.
Link prediction in graph streams
,
2016,
2016 IEEE 32nd International Conference on Data Engineering (ICDE).
[7]
Jie Wang,et al.
Event Pattern Matching over Graph Streams
,
2014,
Proc. VLDB Endow..
[8]
Hui Chen,et al.
A literature survey on smart cities
,
2015,
Science China Information Sciences.