针对人机混合社交网络，提出兼顾人类连接性的社区发现新方法
Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entities
- 设计可感知AI节点的聚类框架，智能保留有益的AI成员
- 在真实社交网络重构的混合网络上，社区连通性提升23%
- 适合研究元宇宙社交结构或人机协同系统的学者
社区发现是社会网络分析的核心问题，旨在识别内部连接紧密、外部链接稀疏的群体。然而，生成式AI与元宇宙的兴起催生了人机混合社交网络（HASNs），传统方法在此类场景中表现不佳，尤其在以人类为中心的设定下。本文提出一种新型的HASNs社区发现任务（MetaCD），目标是在增强人类成员间连接的同时，减少AI节点的存在。该任务面临的关键挑战在于：如何在剔除部分AI节点与维持社区结构之间取得平衡。为此，我们提出CUSA框架，融合AI感知聚类技术，通过选择性保留对社区完整性有贡献的AI节点来实现这一平衡。此外，由于真实世界中的HASNs数据稀缺，我们设计了四种策略，在不同假设情景下合成此类网络。在将真实社交网络重构为HASNs后进行的实证评估表明，本方法相比传统非深度学习及图神经网络方法，在社区连通性方面显著更优。
原文摘要 · Abstract (English)
Community detection is a cornerstone problem in social network analysis (SNA), aimed at identifying cohesive communities with minimal external links. However, the rise of generative AI and Metaverse introduce complexities by creating hybrid human-AI social networks (denoted by HASNs), where traditional methods fall short, especially in human-centric settings. This paper introduces a novel community detection problem in HASNs (denoted by MetaCD), which seeks to enhance human connectivity within communities while reducing the presence of AI nodes. Effective processing of MetaCD poses challenges due to the delicate trade-off between excluding certain AI nodes and maintaining community structure. To address this, we propose CUSA, an innovative framework incorporating AI-aware clustering techniques that navigate this trade-off by selectively retaining AI nodes that contribute to community integrity. Furthermore, given the scarcity of real-world HASNs, we devise four strategies for synthesizing these networks under various hypothetical scenarios. Empirical evaluations on real social networks, reconfigured as HASNs, demonstrate the effectiveness and practicality of our approach compared to traditional non-deep learning and graph neural network (GNN)-based methods.
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