Matched Participants Maximization Based on Social Spread

Abstract

With the great advantage of information spread in the social network, more and more team activities would like to be organized through the social platforms. These activities require users to match partners and then participate in groups, e.g., group-buying and blind-date. In this paper, we consider to organize such activities with matching constraints in social networks to attract as many matched participants as possible through a limited seed set. An Interest-based Forwarding model which is similar to Independent Cascading is used to model information propagation. We investigate two matching strategies to forming groups: (1) neighbor matching (NM), i.e., only direct neighbors can match and (2) global matching (GM), i.e., matching is organized by an external organizer. We prove the matched participants maximization (MPM) problem to optimize the seed set selection to maximize the expected number of final participants is NP-hard and the computation of the target function is #P-hard, under both the NM and GM strategies. To solve MPM-NM efficiently, we propose a Matching Reachable Set method and a $(1-1/e-\epsilon )$-approximation algorithm. Sandwich method is used for solving MPM-GM by using the result of MPM-NM as a lower-bound and constructing an upper bound in an extended graph. A $\beta (1 - 1/e-\epsilon )$-approximation algorithm is proposed for MPM-GM. At last, experiments on the real-world databases verifies the effectiveness and efficiency of the proposed algorithms.

This work is supported by the National Natural Science Foundation of China (Grant NO. 12071478, 11671400, 61972404, 61672524), and partially by NSF 1907472.