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

arXiv: https://arxiv.org/abs/2512.23973 · HTML: https://mlsanity.com/paper/2512.23973/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 30 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Online Influence Maximization (Extended Version)](https://mlsanity.com/paper/1506.01188/) (2015)
- [A Community-Aware Framework for Social Influence Maximization](https://mlsanity.com/paper/2207.08937/) (2022)
- [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)
- [Model-Independent Online Learning for Influence Maximization](https://mlsanity.com/paper/1703.00557/) (2017)

## Built on by (most cited first, 1 of 1)

- [LOFA: Online Influence Maximization under Full-Bandit Feedback using Lazy Forward Selection](https://mlsanity.com/paper/2601.00933/) (2026)

## Abstract (arXiv)

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 influenceunicodex2014a 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.