ADAPT-Net: an adaptive dynamic attention and persistence-aware transformer for overlapping community detection in complex social networks

An event-adaptive incremental framework for transient community detection in temporal networks

Scalable dynamic community detection on temporal graphs using graph neural networks

Dynamic community detection on temporal graphs seeks to identify evolving community structures while allowing node memberships to change over time. In this work, we formulate dynamic community detection over observed node-time instances, where each node-time instance in the temporal interaction stream is assigned a cluster label. We propose a diffusion-guided contrastive learning framework that uses a local temporal diffusion affinity matrix to construct positive and negative node-time pairs and organise the learned representations according to their temporal structural relationships. We then apply a clustering algorithm to the resulting embedding space to detect dynamic communities. Experiments on synthetic temporal networks show that the proposed method outperforms static community detection baselines and achieves competitive or better performance than existing dynamic community detection methods in terms of AMI and ARI, while maintaining good scalability. We further apply the method to a large-scale OpenAlex computer science collaboration network from 2016 to 2025, revealing persistent and evolving collaboration communities in real scientific data. These results suggest that time-node-level representation learning provides an effective framework for scalable dynamic community detection on temporal graphs.

Community Detection as Adaptive Diffusion

This work introduces ECHO (Encoding Communities via High-Order Operators), a scalable, self-supervised framework that treats communities as regions of adaptive diffusion on semantic manifolds that recovers communities from topology when node features are weak, while feature isolation is preferable when features are strong.

MACO-MGDT: Overlapping Community Detection in Heterogeneous Networks via Community-Aware Embeddings

The community detection is a basic task of network analysis, which allows identifying clusters of network nodes with rich internal relationships and structural similarity. The classical community detection algorithms usually assume the network is homogeneous and impose hard assignments of clusters, which restricts their use to real world networks where nodes tend to be a member of more than one cluster and where different types of nodes and attributes are prevalent. To overcome those issues we suggest MACO-MGDT, a single framework of overlapping community detection in non homogenous graphs. The suggested system combines community-conscious representation learning and prototype-based clustering in a two-step architecture. The Multi-scale Attribute-aware Community-Oriented (MACO) module is trained in the first stage to learn structure-aware node embeddings via type-sensitive feature transformations, multi-scale neighborhood aggregation, and community-sensitive attention mechanisms, and a modularity-directed objective is used to incentivize the structure-aware node embeddings to be shaped by latent community structures. The learned embeddings are refined in the second stage by a Multi-scale Graph Diffusion Transformer (MGDT) encoder that incorporates higher-order node interactions. According to the refined representations, a new prototype-based overlapping community detection mechanism is proposed, in which community prototypes can be initially set up by using K-means clustering and soft membership scores are calculated by using similarity-based assignment. The framework is trained with a multi-objective optimization strategy that jointly embeds cohesion, overlap regularization, entropy control, cluster balance, and prototype diversity. The experiments carried out on the heterogeneous citation network of the ACM show that the proposed framework can be used to identify the meaningful community structures. It has high clustering performance of a Silhouette Score of 0.8419, a Davies Bouldin Index of 0.5142 and high graph modularity. The findings also indicate that MACO-MGDT has a very good capability in capturing overlapping community membership especially on the node at an intersectional point. These results demonstrate the promise of the suggested method on scalable and interpretable community detection of complex heterogeneous networks.

Dual-Channel Hybrid Graph Neural Network for Mobility Social Relationship Inference

Spectradyn: an adaptive spectral-temporal framework for dynamic graph link prediction