764,561 research outputs found
Multi-Scale Link Prediction
The automated analysis of social networks has become an important problem due
to the proliferation of social networks, such as LiveJournal, Flickr and
Facebook. The scale of these social networks is massive and continues to grow
rapidly. An important problem in social network analysis is proximity
estimation that infers the closeness of different users. Link prediction, in
turn, is an important application of proximity estimation. However, many
methods for computing proximity measures have high computational complexity and
are thus prohibitive for large-scale link prediction problems. One way to
address this problem is to estimate proximity measures via low-rank
approximation. However, a single low-rank approximation may not be sufficient
to represent the behavior of the entire network. In this paper, we propose
Multi-Scale Link Prediction (MSLP), a framework for link prediction, which can
handle massive networks. The basis idea of MSLP is to construct low rank
approximations of the network at multiple scales in an efficient manner. Based
on this approach, MSLP combines predictions at multiple scales to make robust
and accurate predictions. Experimental results on real-life datasets with more
than a million nodes show the superior performance and scalability of our
method.Comment: 20 pages, 10 figure
Link Prediction via Matrix Completion
Inspired by practical importance of social networks, economic networks,
biological networks and so on, studies on large and complex networks have
attracted a surge of attentions in the recent years. Link prediction is a
fundamental issue to understand the mechanisms by which new links are added to
the networks. We introduce the method of robust principal component analysis
(robust PCA) into link prediction, and estimate the missing entries of the
adjacency matrix. On one hand, our algorithm is based on the sparsity and low
rank property of the matrix, on the other hand, it also performs very well when
the network is dense. This is because a relatively dense real network is also
sparse in comparison to the complete graph. According to extensive experiments
on real networks from disparate fields, when the target network is connected
and sufficiently dense, whatever it is weighted or unweighted, our method is
demonstrated to be very effective and with prediction accuracy being
considerably improved comparing with many state-of-the-art algorithms
The Secure Link Prediction Problem
Link Prediction is an important and well-studied problem for social networks.
Given a snapshot of a graph, the link prediction problem predicts which new
interactions between members are most likely to occur in the near future. As
networks grow in size, data owners are forced to store the data in remote cloud
servers which reveals sensitive information about the network. The graphs are
therefore stored in encrypted form.
We study the link prediction problem on encrypted graphs. To the best of our
knowledge, this secure link prediction problem has not been studied before. We
use the number of common neighbors for prediction. We present three algorithms
for the secure link prediction problem. We design prototypes of the schemes and
formally prove their security. We execute our algorithms in real-life datasets.Comment: This has been accepted for publication in Advances in Mathematics of
Communications (AMC) journa
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