A Community-Aware Framework for Influence Maximization with Explicit Accounting for Inter-Community Influence

TL;DR

Community-IM++ is a scalable influence maximization framework that explicitly models inter-community influence, significantly improving efficiency and effectiveness in large social networks for applications like marketing and public health.

Contribution

It introduces a novel heuristic and adaptive seed allocation strategy to explicitly account for cross-community influence, enhancing influence spread in large networks.

Findings

Achieves near-greedy influence spread with up to 100x lower runtime.

Outperforms existing community-based methods and heuristics.

Effective across various network structures and influence models.

Abstract

Influence Maximization (IM) seeks to identify a small set of seed nodes in a social network to maximize expected information spread under a diffusion model. While community-based approaches improve scalability by exploiting modular structure, they typically assume independence between communities, overlooking inter-community influence$\unicode{x2014}$a limitation that reduces effectiveness in real-world networks. We introduce Community-IM++, a scalable framework that explicitly models cross-community diffusion through a principled heuristic based on community-based diffusion degree (CDD) and a progressive budgeting strategy. The algorithm partitions the network, computes CDD to prioritize bridging nodes, and allocates seeds adaptively across communities using lazy evaluation to minimize redundant computations. Experiments on large real-world social networks under different edge weight models show that Community-IM++ achieves near-greedy influence spread at up to 100 times lower runtime, while outperforming Community-IM and degree heuristics across budgets and structural conditions. These results demonstrate the practicality of Community-IM++ for large-scale applications such as viral marketing, misinformation control, and public health campaigns, where efficiency and cross-community reach are critical.