Structural Diversity and Homophily: A Study Across More than One Hundred Big Networks
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Abstract (arXiv)
A widely recognized organizing principle of networks is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures embedded in common neighbors have on link formation is much less well-understood. To explore this problem, we begin by characterizing the structural diversity of common neighborhoods. Using a collection of 120 large-scale networks, we demonstrate that the impact of the common neighborhood diversity on link existence can vary substantially across networks. We find that its positive effect on Facebook and negative effect on LinkedIn suggest different underlying networking needs in these networks. We also discover striking cases where diversity violates the principle of homophily---that is, where fewer mutual connections may lead to a higher tendency to link with each other. We then leverage structural diversity to develop a common neighborhood signature (CNS), which we apply to a large set of networks to uncover unique network superfamilies not discoverable by conventional methods. Our findings shed light on the pursuit to understand the ways in which network structures are organized and formed, pointing to potential advancement in designing graph generation models and recommender systems.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Feb 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 1; 2025: 1; 2026: 1.
6 papers.
- 2024Position: Graph Foundation Models are Already Herecited by 84
- 2023Revisiting Link Prediction: A Data Perspectivecited by 29
- 2023LPFormer: An Adaptive Graph Transformer for Link Predictioncited by 17
- 2021Network Generation with Differential Privacycited by 2
- 2025Parameter-Free Structural-Diversity Message Passing for Graph Neural Networkscited by 0
- 2026Topology-induced Operators Reveal Complementary Graph Representations without Trainingcited by 0
- 2026Topology-induced Operators Reveal Complementary Graph Representations without Training8 Sep 2026
- 2025Parameter-Free Structural-Diversity Message Passing for Graph Neural Networks27 Aug 2025
- 2024Position: Graph Foundation Models are Already Here3 Feb 2024
- 2023LPFormer: An Adaptive Graph Transformer for Link Prediction17 Oct 2023
- 2023Revisiting Link Prediction: A Data Perspective1 Oct 2023
- 2021Network Generation with Differential Privacy17 Nov 2021