Cumulative effects of triadic closure and homophily in social networks
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Abstract (arXiv)
Much of the structure in social networks has been explained by two seemingly independent network evolution mechanisms: triadic closure and homophily. While it is common to consider these mechanisms separately or in the frame of a static model, empirical studies suggest that their dynamic interplay is the very process responsible for the homophilous patterns of association seen in off- and online social networks. By combining these two mechanisms in a minimal solvable dynamic model, we confirm theoretically the long-held and empirically established hypothesis that homophily can be amplified by the triadic closure mechanism. This research approach allows us to estimate how much of the observed homophily in various friendship and communication networks is due to amplification for a given amount of triadic closure. We find that the cumulative advantage-like process leading to homophily amplification can, under certain circumstances, also lead to the widely documented core-periphery structure of social networks, as well as to the emergence of memory of previous homophilic constraints (equivalent to hysteresis phenomena in physics). The theoretical understanding provided by our results highlights the importance of early intervention in managing at the societal level the most adverse effects of homophilic decision-making, such as inequality, segregation and online echo chambers.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Sep 2018.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 3; 2024: 0; 2025: 2; 2026: 1.
10 papers.
- 2023Revisiting Link Prediction: A Data Perspectivecited by 29
- 2022Inference of hyperedges and overlapping communities in hypergraphscited by 13
- 2025Generative Graph Pattern Machinecited by 11
- 2022Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Surveycited by 8
- 2021Disentangling homophily, community structure and triadic closure in networkscited by 6
- 2021Investigating and Modeling the Dynamics of Long Tiescited by 2
- 2023Topological properties and organizing principles of semantic networkscited by 1
- 2025Studying and Improving Graph Neural Network-based Motif Estimationcited by 1
- 2023Delayed and Indirect Impacts of Link Recommendationscited by 0
- 2026How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphscited by 0
- 2026How Predicted Links Influence Network Evolution: Disentangling Choice and Algorithmic Feedback in Dynamic Graphs4 Mar 2026
- 2025Studying and Improving Graph Neural Network-based Motif Estimation30 May 2025
- 2025Generative Graph Pattern Machine22 May 2025
- 2023Revisiting Link Prediction: A Data Perspective1 Oct 2023
- 2023Topological properties and organizing principles of semantic networks24 Apr 2023
- 2023Delayed and Indirect Impacts of Link Recommendations17 Mar 2023
- 2022Inference of hyperedges and overlapping communities in hypergraphs12 Apr 2022
- 2022Towards Digital Twin Oriented Modelling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey15 Feb 2022
- 2021Investigating and Modeling the Dynamics of Long Ties22 Sep 2021
- 2021Disentangling homophily, community structure and triadic closure in networks7 Jan 2021