Jun 2026· Machine Learning and Knowledge Extraction· Vol 8, pp. 177· 0 citations
TL;DR
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.
Abstract
Traditional dynamic community detection methods often struggle to simultaneously preserve local structural consistency, capture global topological relationships, and efficiently adapt to continuous graph updates in large-scale environments. To solve these limitations, this paper proposes a novel dynamic community detection framework called Embedded Clustering Boundary Refinement (ECBR). The proposed method integrates unsupervised GraphSAGE and Node2Vec embeddings to jointly capture local neighborhood aggregation patterns and global structural equivalence among nodes. The generated embeddings are fused through feature concatenation and z-score normalization to construct a unified latent representation space. Subsequently, Mini-Batch KMeans clustering is employed to efficiently generate the initial community structure while maintaining scalability for large-scale graphs. To further improve partition quality, ECBR introduces a boundary-aware refinement mechanism that identifies structurally ambiguous nodes using neighborhood consistency analysis and reassigns them according to embedding-space similarity. In addition, the framework incorporates an adaptive dynamic update strategy capable of distinguishing between major topological shifts and localized structural changes. Significant graph perturbations trigger complete model retraining, whereas minor modifications are handled through computationally efficient incremental updates on local subgraphs. Experimental evaluations were conducted on synthetic LFR benchmark networks and several real-world dynamic interaction datasets, including high school, workplace, and hospital contact networks. The results demonstrate that ECBR consistently outperforms several state-of-the-art methods, including QCA, DyPerm, DCDID, IncNSA, and DCDBFE, achieving better NMI and ARI scores across diverse network conditions. The experimental findings confirm that ECBR provides a scalable, robust, and highly effective solution for dynamic community detection in evolving large-scale social networks.
Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated promising performance, existing methods often suffer from the"structural isolation"issue during mini-batch training, making it challenging to capture cohesive community structures that characterize the global topological distribution. To address these challenges, we propose SCISE, a Scalable unsupervised graph Clustering framework that preserves structural Integrity by synergizing community-aware sampling with constrained Structural Entropy. Specifically, we first introduce the Structural Entropy Community Constraint operator (SECC), which optimizes structural information within a constrained solution space to mitigate community fragmentation and enhance partition cohesion. Second, to prevent global information loss during batch training, we design a Community-Aware Sampling Expansion (CSampE) mechanism that incorporates the community context of target nodes into sampling batches, effectively breaking structural barriers and preserving topological integrity. Finally, we devise a Structural Contrastive Learning (StructCL) module that refines edge weights based on intra-batch structural similarity, guiding the encoder to learn representations in a higher-order structural space. Extensive experiments on six mainstream benchmark datasets demonstrate that SCISE significantly outperforms state-of-the-art algorithms, with ablation studies and robustness analyses further validating its effectiveness and reliability for real-world large-scale graphs.
Jingyun Zhang, Hao Peng, Jianxin Li et al.· 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
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.
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.
Mo Li, Zhaosong Zhao, Linlin Ding et al.· Annual International ACM SIG...· 0 citations
SimGAT, a structure-aware graph attention model built on SimRank-derived structural embeddings, is proposed, which computes structural similarity in the SimRank2Vec embedding space and injects it as a topological prior into the graph attention mechanism, enabling neighborhood aggregation to be jointly guided by node attributes and global structural relationships.
Chengda Xu, Yinglong Zhang· Journal of King Saud Univers...· 0 citations
A combined HGT-based framework incorporating contrastive representation learning and deep clustering with multi-round training via pseudo-labels is presented, enabling the model to better deal with label scarcity, heterogeneous dependencies, and overlapping semantics in practical, complex, attributed networks.
Hamza Haddad, Hicham Attariuas, A. Younes· Edelweiss Applied Science an...· 0 citations