Human-Centric Community Detection in Hybrid Metaverse Networks with Integrated AI Entities
Track: Social networks and social media
Keywords: community detection, human-centric, social networks, generative AI, Metaverse
TL;DR: 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.
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 the Metaverse introduces new complexities by creating hybrid communities of human users and AI entities. Traditional community detection approaches that overlook the interwoven presence of humans and AIs are inadequate for managing such hybrid networks, known as human-AI social networks (denoted by HASNs), especially when prioritizing human-centric communities. 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 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 design 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.
Submission Number: 1967
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