提出兼顾社区内外影响的高效影响力传播算法，提升真实社交网络中的传播效果。
A Community-Aware Framework for Influence Maximization with Explicit Accounting for Inter-Community Influence
- 基于社区扩散度识别跨社区关键节点，动态分配种子用户
- 在多种权重模型下，运行时间降低100倍，接近最优传播效果
- 适合大规模病毒营销、假信息防控等需跨社区传播场景
影响力最大化（IM）旨在通过选择少量种子节点，在扩散模型下最大化信息传播范围。现有基于社区的方法通常假设社区间独立，忽略跨社区影响，限制了实际效果。本文提出Community-IM++，一种可扩展框架，通过基于社区扩散度（CDD）的启发式方法显式建模跨社区传播，并采用渐进预算策略。该算法先划分网络，计算CDD以优先选择连接不同社区的节点，再利用懒惰评估机制自适应地跨社区分配种子，减少冗余计算。在多种边权重模型下的大规模真实社交网络实验表明，Community-IM++在高达100倍更低的运行时间内实现接近贪心算法的传播效果，且在不同预算和结构条件下均优于Community-IM与度数启发式方法。结果证明其在病毒营销、虚假信息控制及公共卫生宣传等需要高效与跨社区覆盖的应用中具有实用性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。