Beyond Pairwise Network Interactions: Implications for Information Centrality

Organizations and policymakers increasingly rely on network metrics to decide whom to inform, monitor, or support. Yet most networks treat interactions as pairs, even when the underlying activity occurs in groups—project teams, chat channels, meetings, or news articles that mention multiple firms. Collapsing groups into one-to-one links can misidentify who matters. We propose a practical alternative: Model group interactions directly as a hypergraph and compute centrality from a two-step diffusion process that captures how information moves across and within groups. The approach provides a transparent way to incorporate domain knowledge (e.g., whether people enter large or small groups first) and produces testable interpretable rankings. We evaluate the method in three settings—open-source software, a high school interaction study, and financial comentions—and find that hypergraph-based, theory-informed centrality better explains outcomes such as project success, student popularity, and same-day returns than standard graph centralities. For practice and policy, this yields more effective targeting, earlier warning signals, and improved allocation of attention and resources in collaborative work, public health, and market surveillance. We release an open-source Python package (HyperCentral) to support adoption.