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- A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning397
A unified architecture of network community-finding methods to characterize the state-of-the-art of the field of community detection is developed and a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning is introduced.
- Graph Neural Networks for Graphs With Heterophily: A Survey343
A systematic taxonomy that governs existing heterophilic GNN models for heterophilic graphs is proposed, aiming to facilitate the development of more effective GNNs across a spectrum of practical applications and learning tasks in the graph research community.
- Heterogeneous Graph Neural Network via Attribute Completion228
It is held that missing attributes can be acquired by a learnable manner, and a general framework for Heterogeneous Graph Neural Network via Attribute Completion (HGNN-AC), including pre-learning of topological embedding and attribute completion with attention mechanism is proposed.
- Semantic Community Identification in Large Attribute Networks211
A novel nonnegative matrix factorization (NMF) model with two sets of parameters, the community membership matrix and community attribute matrix is proposed and the use of node attributes improves upon community detection and provides a semantic interpretation to the resultant network communities.
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- Graph Convolutional Networks Meet Markov Random Fields: Semi-Supervised Community Detection in Attribute Networks149
This work proposes to integrate the techniques of GCN and MRF to solve the problem of semi-supervised community detection in attributed networks with semantic information and exploits both network topology and node semantic information in a complete end-to-end deep network architecture.
- Powerful Graph Convolutional Networks with Adaptive Propagation Mechanism for Homophily and Heterophily140
A novel propagation mechanism is designed, which can automatically change the propagation and aggregation process according to homophily or heterophily between node pairs, and it is theoretically proved that the model can constrain the similarity of representations between nodes according to theirhomophily degree.
- Multi-Intention Oriented Contrastive Learning for Sequential Recommendation128
This work formalizes the denoising problem by selecting the user's main intention, and proposes a novel framework, namely Multi-Intention Oriented Contrastive Learning Recommender (IOCRec), which fuse local and global intentions to unify sequential patterns and intent-level self-supervision signals.
- Topology Optimization based Graph Convolutional Network114
A novel Topology Optimization based Graph Convolutional Networks (TO-GCN) to fully utilize the potential information by jointly refining the network topology and learning the parameters of the FCN is proposed.
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- On Proximity and Structural Role-based Embeddings in Networks102
The misconceptions and key differences between structural roles and communities are clarified, and the general mechanisms (e.g., random walks and feature diffusion) that give rise to community- or role-based structural embeddings are formalized.
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- Semi-supervised community detection based on non-negative matrix factorization with node popularity83
The proposed SSNMF model is improved by introducing the node popularity, namely PSSNMF, which helps to utilize the prior information more effectively and improves the accuracy of community detection, especially on networks with large degree heterogeneity and unbalanced community structure.
- Identifying overlapping communities as well as hubs and outliers via nonnegative matrix factorization76
A novel model where vertices are measured by their centrality in communities and defined as an optimization problem, calculated by nonnegative matrix factorization is proposed, which demonstrates its ability of identifying overlapping communities, hubs and outliers and validate its superior performance in terms of clustering quality.
- Trafformer: Unify Time and Space in Traffic Prediction75
A new model named Trafformer is proposed, which unifies spatial and temporal information in one transformer-style model and outperforms existing methods and provides a promising future direction for the spatial-temporal traffic prediction problem.
- Incorporating network structure with node contents for community detection on large networks using deep learning74
A new approach to seamlessly combine the models of modularity and normalized-cut via the autoencoder is proposed and can provide a nonlinearly deep representation for a large-scale network and reached an efficient community detection.
- Complex Network Clustering Algorithms65
This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area.
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- Dual-site fluorescent probe for multi-response detection of ClO− and H2O2 and bio-imaging60
The first dual-site fluorescent probe (Geisha-1) for the quantitative detection of ClO- and H2O2 is reported, constructed by chemically grafting N,N-dimethylthiocarbamate and borate to a fluorescence resonance energy transfer (FRET) platform.
- A Unified Weakly Supervised Framework for Community Detection and Semantic Matching55
This paper proposes a unified nonnegative matrix factorization framework simultaneously for community detection and semantic matching by integrating both semi-supervised information and node content and shows that the framework is efficient to match each community with specific semantic information, and the performance are superior over the compared methods.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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