Community Detection as Adaptive Diffusion

DiffGCC: diffusion-enhanced global–local graph contrastive clustering

DiffGCC is a generative graph contrastive clustering framework that couples global–local feature encoding with a latent-space diffusion denoising mechanism and substantially outperforms existing methods across ACC, NMI, ARI, and F1, with particularly strong gains on denser, noisier product graphs.

One-for-All Community Search on Unseen Graphs

Community search is a fundamental graph-based retrieval problem that aims to identify a query-dependent subgraph whose nodes exhibit strong internal connectivity. While recent learning-based methods improve retrieval effectiveness via graph representation learning, they follow a ''one-use-one-train'' paradigm that requires retraining or fine-tuning for each target graph, leading to high data dependency, high training costs, and limited generalization. To handle this, we propose OFA-CS, a ''one-for-all'' community search framework trained once on source datasets and directly deployed to arbitrary unseen graphs without retraining or fine-tuning, while preserving strong performance. Specifically, we introduce a Spectral-Aware Feature Alignment module to unify feature dimensionality and align cross-domain semantics in a community-aware manner. We further develop a Graph Diffusion Tokenized Transformer that constructs hybrid token sequences from local and global structural contexts for Transformer encoding, and applies diffusion-based refinement to mitigate distribution shifts on unseen graphs. With the unified representations, communities are efficiently retrieved via a modularity-driven search procedure. Extensive experiments on diverse real-world graphs demonstrate that OFA-CS achieves strong cross-domain generalization and competitive retrieval effectiveness against state-of-the-art methods, without requiring target-domain supervision.

Constrained deep nonnegative matrix factorization with graph convolutional networks for unsupervised community detection

Community detection is a key task for revealing functional organization in complex networks. Graph neural networks (GNNs) capture non-linear relationships but often suffer from over-smoothing as layers increase. Deep nonnegative matrix factorization (DNMF) models are interpretable via hierarchical learning but are linear and sensitive to topological noise. We propose GCNCDNMF, a framework that integrates graph convolutional networks (GCNs) with a constrained deep nonnegative matrix factorization (CDNMF) module. The CDNMF component uses a deep autoencoder-like structure with graph regularization to preserve community structures. The hierarchical embeddings from CDNMF are injected into the GCN propagation steps, which mitigates over-smoothing. In return, the GCN’s non-linear reconstructions refine the network topology and feed back to CDNMF, improving noise robustness. Experiments on five real-world benchmark datasets (Cora, Citeseer, Email, Cornell, and Texas) show that GCNCDNMF consistently outperforms state-of-the-art methods in accuracy, normalized mutual information, and adjusted Rand index. The code for this work is publicly available at: https://github.com/LiShunli0719/GCNCDNMF_main.

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

Overlapping community detection in dynamic complex networks is challenging because memberships evolve over time, interactions arrive irregularly, and genuine bridge roles must be distinguished from noisy multi-community assignments. The aim of this study is to develop and evaluate adaptive dynamic attention and persistence-aware transformer (ADAPT-Net), an adaptive dynamic attention and persistence-aware transformer for overlapping community detection in complex social networks. The proposed framework integrates adaptive event-driven graph partitioning, multiscale graph attention encoding, learnable node-centric scale fusion, cross-snapshot link-aware temporal attention, and density-preserving overlap regularization. The primary controlled evaluation uses one processed benchmark instance derived from the Real-world Dynamic Networks (DynaMo) collection. Under a unified five-fold held-out protocol, ADAPT-Net records an F1-score of 96.70 ± 0.65, precision of 95.75 ± 1.50, recall of 97.69 ± 0.49, Jaccard similarity of 93.62 ± 1.22, Omega Index of 88.91 ± 3.01, normalized mutual information (NMI) of 84.43 ± 2.59, area under the receiver operating characteristic curve (AUC–ROC) of 99.77 ± 0.11, and area under the precision–recall curve (AUPRC) of 99.59 ± 0.17. Additional label-independent structural validation is reported for Reddit, dblp computer science bibliography (DBLP), and Enron using modularity, coverage, conductance, internal density, overlap rate, mean memberships per node, and temporal switch rate. The controlled and external analyses address different questions: the processed DynaMo-derived benchmark instance measures agreement with the reference memberships contained in the evaluation folds, whereas the external networks assess structural cohesion and temporal stability without equivalent reference overlaps. The current implementation is trained offline on snapshot sequences and supports snapshot-level inference rather than fully online parameter updates.

Enhancing link prediction in complex networks using GraphSAGE with graph diffusion convolution

A diffusion-enhanced inductive link prediction framework that combines Graph Diffusion Convolution (GDC), structural node descriptors, and neighborhood aggregation from GraphSAGE is proposed that achieves higher accuracy than the other models on the benchmark datasets.

HGOOD : Hypergraph-enhanced Graph Contrastive Learning for Graph Out-of-Distribution Detection