Maximizing Friend-Making Likelihood for Social Activity Organization

TL;DR

This paper introduces the Hop-bounded Maximum Group Friending problem to optimize in-person social activity planning via online networks, addressing the challenge of maximizing new friend-making likelihood within group constraints.

Contribution

It formulates a new NP-hard problem, proves its computational difficulty, and proposes an efficient approximation algorithm validated through user studies and real data experiments.

Findings

The problem is NP-Hard with no efficient approximation unless P=NP.

The proposed algorithm achieves near-optimal solutions efficiently.

Experimental results confirm the algorithm's effectiveness in real-world scenarios.

Abstract

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.