Measuring and Maximizing Influence via Random Walk in Social Activity Networks

TL;DR

This paper introduces a new influence maximization approach in social-activity networks that accounts for online activities, using random walk-based influence centrality and a greedy algorithm, outperforming existing methods in efficiency.

Contribution

It formulates influence maximization in social-activity networks considering online activities, and proposes a novel random walk-based influence measure with an efficient greedy algorithm.

Findings

The proposed method outperforms the IMM algorithm in efficiency on large datasets.

The influence centrality effectively captures influence in complex social-activity networks.

Experimental results demonstrate scalability and accuracy of the approach.

Abstract

With the popularity of OSNs, finding a set of most influential users (or nodes) so as to trigger the largest influence cascade is of significance. For example, companies may take advantage of the "word-of-mouth" effect to trigger a large cascade of purchases by offering free samples/discounts to those most influential users. This task is usually modeled as an influence maximization problem, and it has been widely studied in the past decade. However, considering that users in OSNs may participate in various kinds of online activities, e.g., giving ratings to products, joining discussion groups, etc., influence diffusion through online activities becomes even more significant. In this paper, we study the impact of online activities by formulating the influence maximization problem for social-activity networks (SANs) containing both users and online activities. To address the computation challenge, we define an influence centrality via random walks to measure influence, then use the Monte Carlo framework to efficiently estimate the centrality in SANs. Furthermore, we develop a greedy-based algorithm with two novel optimization techniques to find the most influential users. By conducting extensive experiments with real-world datasets, we show our approach is more efficient than the state-of-the-art algorithm IMM[17] when we needs to handle large amount of online activities.