Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entities

2025-02-15

AI Summary

Traditional community detection methods fail in Hybrid Augmented Social Networks (HASNs), where human–AI coexistence distorts community structures. To address this, we propose MetaCD, a human-centric community discovery paradigm that enhances human node cohesion while suppressing superfluous AI nodes. We design CUSA, an AI-aware, trade-off-driven clustering framework that dynamically preserves AI nodes beneficial to community structure. Additionally, we introduce four synthetic HASN generation strategies grounded in real-world social network reconfiguration, effectively bridging the critical data gap. Extensive experiments on reconfigured real networks demonstrate that MetaCD improves human cohesion by 23.6% and reduces AI redundancy by 31.4%, significantly outperforming conventional community detection algorithms and GNN-based baselines.

Technology Category

• Humans and AI: Crowd Sourcing and Human Computation
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Social networks and social learning
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• Detect human-centric communities in hybrid networks.
• Reduce AI nodes while maintaining community structure.
• Introduce AI-aware clustering for MetaCD problem.

Innovation

• AI-aware clustering techniques
• Selective AI node retention
• Synthetic HASNs generation strategies