182,229 research outputs found
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
Predicting Social Links for New Users across Aligned Heterogeneous Social Networks
Online social networks have gained great success in recent years and many of
them involve multiple kinds of nodes and complex relationships. Among these
relationships, social links among users are of great importance. Many existing
link prediction methods focus on predicting social links that will appear in
the future among all users based upon a snapshot of the social network. In
real-world social networks, many new users are joining in the service every
day. Predicting links for new users are more important. Different from
conventional link prediction problems, link prediction for new users are more
challenging due to the following reasons: (1) differences in information
distributions between new users and the existing active users (i.e., old
users); (2) lack of information from the new users in the network. We propose a
link prediction method called SCAN-PS (Supervised Cross Aligned Networks link
prediction with Personalized Sampling), to solve the link prediction problem
for new users with information transferred from both the existing active users
in the target network and other source networks through aligned accounts. We
proposed a within-target-network personalized sampling method to process the
existing active users' information in order to accommodate the differences in
information distributions before the intra-network knowledge transfer. SCAN-PS
can also exploit information in other source networks, where the user accounts
are aligned with the target network. In this way, SCAN-PS could solve the cold
start problem when information of these new users is total absent in the target
network.Comment: 11 pages, 10 figures, 4 table
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
Applications of Structural Balance in Signed Social Networks
We present measures, models and link prediction algorithms based on the
structural balance in signed social networks. Certain social networks contain,
in addition to the usual 'friend' links, 'enemy' links. These networks are
called signed social networks. A classical and major concept for signed social
networks is that of structural balance, i.e., the tendency of triangles to be
'balanced' towards including an even number of negative edges, such as
friend-friend-friend and friend-enemy-enemy triangles. In this article, we
introduce several new signed network analysis methods that exploit structural
balance for measuring partial balance, for finding communities of people based
on balance, for drawing signed social networks, and for solving the problem of
link prediction. Notably, the introduced methods are based on the signed graph
Laplacian and on the concept of signed resistance distances. We evaluate our
methods on a collection of four signed social network datasets.Comment: 37 page
Predicting Anchor Links between Heterogeneous Social Networks
People usually get involved in multiple social networks to enjoy new services
or to fulfill their needs. Many new social networks try to attract users of
other existing networks to increase the number of their users. Once a user
(called source user) of a social network (called source network) joins a new
social network (called target network), a new inter-network link (called anchor
link) is formed between the source and target networks. In this paper, we
concentrated on predicting the formation of such anchor links between
heterogeneous social networks. Unlike conventional link prediction problems in
which the formation of a link between two existing users within a single
network is predicted, in anchor link prediction, the target user is missing and
will be added to the target network once the anchor link is created. To solve
this problem, we use meta-paths as a powerful tool for utilizing heterogeneous
information in both the source and target networks. To this end, we propose an
effective general meta-path-based approach called Connector and Recursive
Meta-Paths (CRMP). By using those two different categories of meta-paths, we
model different aspects of social factors that may affect a source user to join
the target network, resulting in the formation of a new anchor link. Extensive
experiments on real-world heterogeneous social networks demonstrate the
effectiveness of the proposed method against the recent methods.Comment: To be published in "Proceedings of the 2016 IEEE/ACM International
Conference on Advances in Social Networks Analysis and Mining (ASONAM)
Personalized Degrees: Effects on Link Formation in Dynamic Networks from an Egocentric Perspective
Understanding mechanisms driving link formation in dynamic social networks is
a long-standing problem that has implications to understanding social structure
as well as link prediction and recommendation. Social networks exhibit a high
degree of transitivity, which explains the successes of common neighbor-based
methods for link prediction. In this paper, we examine mechanisms behind link
formation from the perspective of an ego node. We introduce the notion of
personalized degree for each neighbor node of the ego, which is the number of
other neighbors a particular neighbor is connected to. From empirical analyses
on four on-line social network datasets, we find that neighbors with higher
personalized degree are more likely to lead to new link formations when they
serve as common neighbors with other nodes, both in undirected and directed
settings. This is complementary to the finding of Adamic and Adar that neighbor
nodes with higher (global) degree are less likely to lead to new link
formations. Furthermore, on directed networks, we find that personalized
out-degree has a stronger effect on link formation than personalized in-degree,
whereas global in-degree has a stronger effect than global out-degree. We
validate our empirical findings through several link recommendation experiments
and observe that incorporating both personalized and global degree into link
recommendation greatly improves accuracy.Comment: To appear at the 10th International Workshop on Modeling Social Media
co-located with the Web Conference 201
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