Jun 2026· Journal of King Saud University: Computer and Information Sciences· Vol 38· 0 citations· 52 references
Abstract
Community detection aims to uncover mesoscopic organization in graphs by grouping nodes into densely connected communities, and it plays an important role in analyzing citation networks, social systems, and other complex relational data. In practice, this task is challenging because community structures are determined not only by pairwise connectivity, but also by higher-order structural patterns such as motifs. Existing Graph Masked Autoencoders (GMAEs), although effective for self-supervised graph representation learning, usually rely on random node or edge masking and pairwise reconstruction objectives. As a result, they may fail to preserve the higher-order structural regularities that are crucial for identifying coherent communities. To address this issue, we propose a Motif-aware Graph Masked Autoencoder (MGMAE) for community detection. The proposed framework explicitly incorporates triangle motifs into both the masking and reconstruction processes. Specifically, MGMAE first enumerates motif structures as higher-order supervision targets, then constructs perturbed graphs through a joint motif–edge masking strategy, and finally learns node representations by jointly reconstructing masked edges and masked motifs. In this way, the learned embeddings are encouraged to preserve both local connectivity and higher-order cohesion patterns that are closely related to community organization. Extensive experiments on six real-world attributed networks and five synthetic LFR benchmarks demonstrate that MGMAE achieves highly competitive performance against representative baselines in terms of NMI and ARI. Additional ablation and hyperparameter studies further verify the effectiveness of the proposed motif-aware masking and reconstruction design.
Community detection is essential for uncovering the functional organization of complex networks. While traditional methods often rely on edge density, motif-based approaches use higher-order structural patterns to identify communities. However, existing research frequently employs conventional motifs, such as triangles or 4-node cliques, or lacks validation against networks with ground-truth communities. This study addresses these limitations by systematically evaluating eight small motifs across both synthetic and real-world networks with known community structures. We propose a framework that transforms unweighted graphs into weighted representations by assigning weights to node pairs based on their co-occurrence frequency within specific graphlets, while also preserving information about the original edges, rather than creating a potentially sparse (hyper)network. Thus, graphlet adjacency captures the topological complexity of a node by accounting for both its direct edges and the local connectivity patterns of its neighbors; this higher-order information is vital for accurate community detection. Our results demonstrate that graphlet-based weighting significantly enhances community detection in networks. We find that no single "universal" motif optimizes performance across all real-world networks. Rather than favoring only dense, clique-based structures, our findings highlight that simpler motifs can also provide strong performance in networks. These results suggest that relying exclusively on cliques may overlook critical connectivity patterns, offering a new perspective on how higher-order structures define communities in networks.
Anastasiia Dziuba, Jure Pražnikar· Journal of Intelligence and...· 0 citations
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.
N. Vinod, Neha Priyesh, Sreelakshmy A J et al.· International Conference Com...· 0 citations
LUCID, an LLM-guided, interpretable, training-free, and unsupervised community detection method, designed as a four-stage pipeline that achieves state-of-the-art performance and consistently outperforms leading unsupervised and semi-supervised baselines.
Aoting Zeng, Kai Wang, Jianwei Wang et al.· 0 citations
Curvature-Guided Sheaf Diffusion is proposed, a fully unsupervised community-detection algorithm that uses the discrete Forman--Ricci curvature of each edge as its single topological signal, propagated through every stage of an end-to-end pipeline.
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.
Lun Liu, Chengyun Song· Pattern Analysis and Applica...· 0 citations
A novel dynamic community detection framework called Embedded Clustering Boundary Refinement (ECBR), which introduces a boundary-aware refinement mechanism that identifies structurally ambiguous nodes using neighborhood consistency analysis and reassigns them according to embedding-space similarity.
Asgarali Bouyer, Alireza Rouhi, Bahman Arasteh et al.· Machine Learning and Knowled...· 0 citations