Community-level core-periphery structures in collaboration networks

2025-11-24

AI Summary

This study addresses the limitation of traditional network analysis—which operates primarily at the node level and fails to capture coexisting community-level structural patterns—by proposing the first community-level core-periphery detection framework tailored to collaborative networks. Methodologically, it jointly optimizes community partitioning and role assignment through an objective function that models both inter-community connection density and strength, enabling attribute-driven interpretation (e.g., disciplinary or geographical) of collective roles. Empirical evaluation on an Italian co-authorship network demonstrates that the framework effectively uncovers hierarchical core-periphery structures tightly linked to institutional status, regional development, and research themes, while quantifying structural inequality in scientific collaboration. By transcending the node-centrality paradigm, this work provides a novel, structurally grounded perspective for analyzing organizational mechanisms underlying knowledge diffusion and innovation emergence.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Game Theory and Economic Paradigms: Coordination and Collaboration
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

Uncovering structural patterns in collaboration networks is key for understanding how knowledge flows and innovation emerges. These networks often exhibit a rich interplay of meso-scale structures, such as communities, core-periphery organization, and influential hubs, which shape the complexity of scientific collaboration. The coexistence of such structures challenges traditional approaches, which typically isolate specific network patterns at the node level. We introduce a novel framework for detecting core-periphery structures at the community level. Given a reference grouping of the nodes, the method optimizes an objective function that assigns core or peripheral roles to communities by accounting for the density and strength of their inter-community connections. The node-level partition may correspond to either inferred communities or to a node-attribute classification, such as discipline or location, enabling direct interpretation of how different social or organizational groups occupy central positions in the network. The method is motivated by an application to a co-authorship network of Italian academics in three different disciplines, where it reveals a hierarchical core-periphery structure associated with institutional role, regional location, and research topics.

Problem

• Detecting core-periphery structures at community level in collaboration networks
• Overcoming limitations of traditional node-level structural analysis methods
• Analyzing how social groups occupy central network positions through attributes

Innovation

• Detects core-periphery structures at community level
• Optimizes objective function for inter-community connections
• Applies to inferred communities or node-attribute classifications