26 research outputs found
Maximizing Friend-Making Likelihood for Social Activity Organization
The social presence theory in social psychology suggests that
computer-mediated online interactions are inferior to face-to-face, in-person
interactions. In this paper, we consider the scenarios of organizing in person
friend-making social activities via online social networks (OSNs) and formulate
a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), by
modeling both existing friendships and the likelihood of new friend making. To
find a set of attendees for socialization activities, HMGF is unique and
challenging due to the interplay of the group size, the constraint on existing
friendships and the objective function on the likelihood of friend making. We
prove that HMGF is NP-Hard, and no approximation algorithm exists unless P =
NP. We then propose an error-bounded approximation algorithm to efficiently
obtain the solutions very close to the optimal solutions. We conduct a user
study to validate our problem formulation and per- form extensive experiments
on real datasets to demonstrate the efficiency and effectiveness of our
proposed algorithm
Relevance Search via Bipolar Label Diffusion on Bipartite Graphs
The task of relevance search is to find relevant items to some given queries which can be viewed either as an information retrieval problem or as a semi-supervised learning problem In order to combine both of their advantages we develop a new relevance search method using label diffusion on bipartite graphs And we propose a heat diffusion-based algorithm namely bipartite label diffusion BLD Our method yields encouraging experimental results on a number of relevance search problem
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