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- A Comprehensive Survey on Graph Anomaly Detection With Deep Learning898
A taxonomy that follows a task-driven strategy and categorizes existing work according to the anomalous graph objects that they can detect is provided, to create a “one-stop-shop” that provides a unified understanding of the problem categories and existing approaches.
- A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT557
This survey aims to shed light on the research of the PFMs on scalability, security, logical reasoning ability, cross-domain learning ability, cross-domain learning ability, and user-friendly interactive ability for artificial general intelligence.
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- Infrared and visible image fusion via detail preserving adversarial learning456
This paper proposes an end-to-end model for infrared and visible image fusion based on detail preserving adversarial learning that is able to overcome the limitations of the manual and complicated design of activity-level measurement and fusion rules in traditional fusion methods.
- Tri-party deep network representation439
TriDNR is a tri-party deep network representation model, using information from three parties: node structure, node content, and node labels (if available) to jointly learn optimal node representation, which results in up to 79% classification accuracy gain, compared to state-of-the-art methods.
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- A Comprehensive Survey on Automatic Knowledge Graph Construction401
A comprehensively survey of more than 300 methods to summarize the latest developments in knowledge graph construction and presents the paradigms to compare the distinction among these methods along the axis of the data environment, motivation, and architecture.
- 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.
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- Graph Structure Learning with Variational Information Bottleneck249
VIB-GSL is the first attempt to advance the Information Bottleneck principle for graph structure learning, providing a more elegant and universal framework for mining underlying task-relevant relations.
- A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions247
A novel taxonomy of deep clustering is introduced based on how two fundamental components: the representation learning module and the clustering module interact, specifically through multistage, generative, iterative, and simultaneous approaches.
- SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism202
A novel hierarchical subgraph-level selection and embedding-based graph neural network for graph classification, namely SUGAR, to learn more discriminative subgraph representations and respond in an explanatory way and a reinforcement pooling mechanism, which improves the generalization ability of the model.
- A Deep Framework for Cross-Domain and Cross-System Recommendations184
DCDCSR framework outperforms the state-of-the-art CDR and CSR approaches in terms of recommendation accuracy and takes into account the rating sparsity degrees of individual users and items in different domains or systems and uses them to guide the DNN training process for utilizing the rating data more effectively.
- eFraudCom: An E-commerce Fraud Detection System via Competitive Graph Neural Networks182
A competitive graph neural networks (CGNN)-based fraud detection system (eFraudCom) to detect fraud behaviors at one of the largest e-commerce platforms, “Taobao”, and demonstrates that the proposed deep framework CGNN is superior to other baselines in detecting fraud behaviors.
- H2-FDetector: A GNN-based Fraud Detector with Homophilic and Heterophilic Connections179
A Graph Neural Network-based Fraud Detector with Homophilic and Heterophilic Interactions (H2-FDetector for short), which apparently outperforms state-of-the-art baselines on two real public benchmark fraud detection tasks.
- Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs172
This paper incorporates neighborhood subgraph-level information of entities, and proposes a neighborhood-aware attentional representation method NAEA for multilingual knowledge graphs that significantly and consistently outperforms state-of-the-art entity alignment models.
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- ComGA139
A novel community-aware attributed graph anomaly detection framework (ComGA), designed to improve the performance of anomaly detection, and a tailored deep graph convolutional network (tGCN) to anomaly detection on attributed graphs.
- Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification132
This paper proposes VFGNN, a federated GNN learning paradigm for privacy-preserving node classification task under data vertically partitioned setting, which can be generalized to existing GNN models.
- An unsupervised parameter learning model for RVFL neural network130
A novel unsupervised network parameter learning method for RVFL, named sparse pre-trained random vector functional link (SP-RVFL for short) network, which uses a sparse autoencoder with ℓ1-norm regularization to adaptively learn superior network parameters for specific learning tasks.
- Time series feature learning with labeled and unlabeled data128
A new Semi-Supervised Shapelets Learning model is presented to efficiently learn shapelets by using both labeled and unlabeled time series data in an integrated model that considers the least squares regression, the power of the pseudo-labels, shapelets regularization, and spectral analysis.
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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