{"ID":2837597,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.19170","arxiv_id":"2511.19170","title":"Perplexity-Homophily Index: Homophily through Diversity in Hypergraphs","abstract":"Real-world complex systems are often better modeled as hypergraphs, where edges represent group interactions involving multiple entities. Understanding and quantifying homophily (similarity-driven association) in such networks is essential for analyzing community formation and information flow. We propose a hyperedge-centric framework to quantify homophily in hypergraphs. Each interaction is represented as a hyperedge, and its interaction perplexity measures the effective number of distinct attributes it contains. Comparing this observed perplexity with a degree-preserving random baseline defines the diversity gap, which quantifies how diverse an interaction is than expected by chance. The global homophily score for a network, called Perplexity-Homophily Index, is computed by averaging the normalized diversity gap across all hyperedges. Experiments on synthetic and real-world datasets show that the proposed index captures the full distribution of homophily and reveals how homophilic and heterophilic tendencies vary with interaction size in hypergraphs.","short_abstract":"Real-world complex systems are often better modeled as hypergraphs, where edges represent group interactions involving multiple entities. Understanding and quantifying homophily (similarity-driven association) in such networks is essential for analyzing community formation and information flow. We propose a hyperedge-c...","url_abs":"https://arxiv.org/abs/2511.19170","url_pdf":"https://arxiv.org/pdf/2511.19170v1","authors":"[\"Gaurav Kumar\",\"Akrati Saxena\",\"Chandrakala Meena\"]","published":"2025-11-24T14:33:13Z","proceeding":"cs.SI","tasks":"[\"cs.SI\"]","methods":"[]","has_code":false}