FLGAT: A federated graph attention framework for privacy-preserving community detection

Pratibha Joshi and Buddha Singh

Chaos, Solitons & Fractals, 2025, vol. 200, issue P1

Abstract: Community detection plays a vital role in analysing real-world networks. However, traditional centralized community detection algorithms often violate data privacy constraints particularly when applied to sensitive or distributed data sources where aggregating raw graph data is either impractical or strictly prohibited. To address this challenge, the paper proposes a privacy-preserving community detection framework that is Federated Graph Attention Framework (FLGAT). In this framework, the global graph is partitioned into edge-disjoint subgraphs with a shared node set, each assigned to a distinct client in a federated environment. This ensures that raw graph data remains local to each client by preserving data privacy. Within each client, node embeddings are learned using a self-supervised GAT model trained with a hybrid loss function that combines contrastive InfoNCE loss with a modularity aware component. This encourages the embeddings to capture both semantic similarity and structural community. These local models are then collaboratively integrated using the Network Average Degree (NAD) weighted aggregation strategy, effectively integrating diverse local models based on graph structure quality. The globally learned embeddings are then utilized to construct a similarity graph that captures the relational patterns between nodes across the distributed network. Finally, the Louvain algorithm is applied to this similarity graph to detect communities by optimizing modularity. The FLGAT framework is assessed using three LFR benchmark networks and four real-world networks. It consistently outperforms existing approaches across key metrics modularity (Q), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI) while strictly adhering to privacy-preserving principles.

Keywords: Federated learning; Graph attention networks; Privacy preservation; Community detection; Complex network (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077925009841
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:200:y:2025:i:p1:s0960077925009841

DOI: 10.1016/j.chaos.2025.116971

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().