# A Community-Aware Framework for Social Influence Maximization

arXiv: https://arxiv.org/abs/2207.08937 · HTML: https://mlsanity.com/paper/2207.08937/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 55 references that are in the map.
- First posted on arXiv 18 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Online Influence Maximization (Extended Version)](https://mlsanity.com/paper/1506.01188/) (2015)
- [Online Influence Maximization under Linear Threshold Model](https://mlsanity.com/paper/2011.06378/) (2020)
- [Model-Independent Online Learning for Influence Maximization](https://mlsanity.com/paper/1703.00557/) (2017)

## Built on by (most cited first, 7 of 7)

- [CyNetDiff -- A Python Library for Accelerated Implementation of Network Diffusion Models](https://mlsanity.com/paper/2404.17059/) (2024)
- [Fractional Budget Allocation for Influence Maximization under General Marketing Strategies](https://mlsanity.com/paper/2407.05669/) (2024)
- [COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport](https://mlsanity.com/paper/2406.12304/) (2024)
- [A Community-Aware Framework for Influence Maximization with Explicit Accounting for Inter-Community Influence](https://mlsanity.com/paper/2512.23973/) (2025)
- [γ-weakly θ-up-concavity: A Unified Framework for Non-Convex Optimization Beyond DR-Submodular and OSS Functions](https://mlsanity.com/paper/2602.13506/) (2026)
- [Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entities](https://mlsanity.com/paper/2502.10750/) (2025)
- [LOFA: Online Influence Maximization under Full-Bandit Feedback using Lazy Forward Selection](https://mlsanity.com/paper/2601.00933/) (2026)

## Abstract (arXiv)

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.