Activity Maximization by Effective Information Diffusion in Social Networks

TL;DR

This paper introduces the activity maximization problem in social networks, aiming to select seed users to maximize total excitement, and proposes a scalable algorithm with proven approximation guarantees validated by experiments.

Contribution

It formulates a novel activity maximization problem, analyzes its complexity, and develops a randomized algorithm with theoretical guarantees and empirical validation.

Findings

The proposed method outperforms heuristics in effectiveness.

The algorithm is scalable to large datasets.

Experimental results confirm the theoretical guarantees.

Abstract

In a social network, even about the same information the excitements between different pairs of users are different. If you want to spread a piece of new information and maximize the expected total amount of excitements, which seed users should you choose? This problem indeed is substantially different from the renowned influence maximization problem and cannot be tackled using the existing approaches. In this paper, motivated by the demand in a few interesting applications, we model the novel problem of activity maximization. We tackle the problem systematically. We first analyze the complexity and the approximability of the problem. We develop an upper bound and a lower bound that are submodular so that the Sandwich framework can be applied. We then devise a polling-based randomized algorithm that guarantees a data dependent approximation factor. Our experiments on three real data sets clearly verify the effectiveness and scalability of our method, as well as the advantage of our method against the other heuristic methods.