Paper Info
Title
Maximizing Friend-Making Likelihood for Social Activity Organization.
Abstract
The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, inperson 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 perform extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm.
Year
DOI
Venue
2015
10.1007/978-3-319-18038-0_1
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PART I
DocType
Volume
ISSN
Journal
9077
0302-9743
Citations 
PageRank 
References 
3
0.38
11
Authors
4
Name
Order
Citations
PageRank
Chih-Ya Shen110317.13
De-Nian Yang258666.66
Wang-Chien Lee35765346.32
Ming Chen465071277.71