Hybrid-HGT: A unified deep learning framework for community detection in heterogeneous attributed graphs

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.

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

UniHG: A Large-scale Universal Heterogeneous Graph Dataset and Benchmark for Representation Learning and Cross-Domain Transferring

Irregular data in the real world are usually organized as heterogeneous graphs consisting of multiple types of nodes and edges. However, current heterogeneous graph research confronts three fundamental challenges: i) Benchmark Deficiency, ii) Semantic Disalignment, and iii) Propagation Degradation. In this paper, we construct a large-scale, universal, and joint multi-domain heterogeneous graph dataset named UniHG to facilitate heterogeneous graph representation learning and cross-domain knowledge mining. Overall, UniHG contains 77.31 million nodes and 564 million directed edges with thousands of labels and attributes, which is currently the largest universal heterogeneous graph dataset available to the best of our knowledge. To perform effective learning and provide comprehensively benchmarks on UniHG , two key measures are taken, including i) the semantic alignment strategy for multi-attribute entities, which projects the feature description of multi-attribute nodes and edges into a common embedding space to facilitate information aggregation; ii) proposing the novel Heterogeneous Graph Decoupling (HGD) framework with a specifically designed Anisotropy Feature Propagation (AFP) module for learning effective multi-hop anisotropic propagation kernels. These two strategies enable efficient information propagation among a tremendous number of multi-attribute entities and meanwhile mine multi-attribute association adaptively through the multi-hop aggregation in large-scale heterogeneous graphs. Comprehensive benchmark results demonstrate that our model significantly out-performs existing methods with an accuracy improvement of 28.93%. And the UniHG can facilitate downstream tasks, achieving an NDCG

Rethinking Generalization in Graphs: A Hierarchical Interaction Perspective for Generalist Detection

Hierarchical Interaction MOdeling for zero-shot generalist GAD enables anomaly detection across diverse graph domains without retraining or access to target-domain supervision by modeling the evolutionary trajectories of node representations across hierarchical structural depths, thereby capturing interaction patterns that exhibit strong cross-domain stability.

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.

Interpretable Unsupervised Community Detection with LLM-Symbolized Structured Processes

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.