A Community-Aware Framework for Social Influence Maximization

TL;DR

This paper introduces a community-aware divide-and-conquer framework for influence maximization in social networks, leveraging community structures to improve efficiency and influence spread.

Contribution

It presents a novel framework that learns community structures and optimizes seed selection within communities, outperforming standard methods in speed and influence.

Findings

The framework outperforms standard influence maximization methods in run-time.

It achieves higher influence spread compared to heuristic methods.

Higher community modularity correlates with better performance of the framework.

Abstract

We consider the problem of Influence Maximization (IM), the task of selecting $k$ seed nodes in a social network such that the expected number of nodes influenced is maximized. We propose a community-aware divide-and-conquer framework that involves (i) learning the inherent community structure of the social network, (ii) generating candidate solutions by solving the influence maximization problem for each community, and (iii) selecting the final set of seed nodes using a novel progressive budgeting scheme. Our experiments on real-world social networks show that the proposed framework outperforms the standard methods in terms of run-time and the heuristic methods in terms of influence. We also study the effect of the community structure on the performance of the proposed framework. Our experiments show that the community structures with higher modularity lead the proposed framework to perform better in terms of run-time and influence.