62,226 research outputs found
Negative Link Prediction in Social Media
Signed network analysis has attracted increasing attention in recent years.
This is in part because research on signed network analysis suggests that
negative links have added value in the analytical process. A major impediment
in their effective use is that most social media sites do not enable users to
specify them explicitly. In other words, a gap exists between the importance of
negative links and their availability in real data sets. Therefore, it is
natural to explore whether one can predict negative links automatically from
the commonly available social network data. In this paper, we investigate the
novel problem of negative link prediction with only positive links and
content-centric interactions in social media. We make a number of important
observations about negative links, and propose a principled framework NeLP,
which can exploit positive links and content-centric interactions to predict
negative links. Our experimental results on real-world social networks
demonstrate that the proposed NeLP framework can accurately predict negative
links with positive links and content-centric interactions. Our detailed
experiments also illustrate the relative importance of various factors to the
effectiveness of the proposed framework
A Model of Consistent Node Types in Signed Directed Social Networks
Signed directed social networks, in which the relationships between users can
be either positive (indicating relations such as trust) or negative (indicating
relations such as distrust), are increasingly common. Thus the interplay
between positive and negative relationships in such networks has become an
important research topic. Most recent investigations focus upon edge sign
inference using structural balance theory or social status theory. Neither of
these two theories, however, can explain an observed edge sign well when the
two nodes connected by this edge do not share a common neighbor (e.g., common
friend). In this paper we develop a novel approach to handle this situation by
applying a new model for node types. Initially, we analyze the local node
structure in a fully observed signed directed network, inferring underlying
node types. The sign of an edge between two nodes must be consistent with their
types; this explains edge signs well even when there are no common neighbors.
We show, moreover, that our approach can be extended to incorporate directed
triads, when they exist, just as in models based upon structural balance or
social status theory. We compute Bayesian node types within empirical studies
based upon partially observed Wikipedia, Slashdot, and Epinions networks in
which the largest network (Epinions) has 119K nodes and 841K edges. Our
approach yields better performance than state-of-the-art approaches for these
three signed directed networks.Comment: To appear in the IEEE/ACM International Conference on Advances in
Social Network Analysis and Mining (ASONAM), 201
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
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