The link‐prediction problem for social networks

Abstract

Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.

Computational social science: Making the links

“…By writing software to automatically extract names from the papers, the pair built up a digital network several orders of magnitude larger than any that had been examined before, with each link representing two researchers who had collaborated. By following how the network changed over time, the researchers identified several measures of closeness among the researchers that could be used to forecast future collaborations 5 .…”

Link prediction using discrete-time quantum walk

“…Two indices based on classical random walks, Local Random Walk and Superpose Random Walk index, are considered. In addition, in [16] the Common Neighbours index, the Adamic-Adar index [17] and Resource Allocation index [18] had better performance in most cases. Here the three methods are also considered as the basic predictors.…”

An Efficient Modified Common Neighbor Approach for Link Prediction in Social Networks

“…They tested the predictive power of some proximity metrics, including Common neighbours, Adamic/Adar, Katz measure. They further worked on this problem in [5] with more descriptive analysis of same proximity metrics as in [4]. Here in both the works [4][5], their hypothesis was that link prediction could be performed from topological analysis alone.…”

“… Traditional weighted Common neighbor method [11] which is not considered to be better than unweighted Common neighbor [4][5], just takes simple addition of all of the weights of common nodes" links with nodes X and Y, no clear logic is shown for this addition so need to be modified.  Bigram approach [4][5] is not truly defined for link prediction in social networks. The actual bigram approach refers to find semantic relations between any two nodes X and Y (e.g.…”

“…Liben-Nowell and Kleinberg [4,5] explain link prediction problem as: "Given a snapshot of a social network at time t, we seek to accurately predict the edges that will be added to the network during the interval from time t to a given future time t'. "…”