Graph Anomaly Detection with Few Labels: A Data-Centric Approach Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models LPFormer: An Adaptive Graph Transformer for Link Prediction HiGPT: Heterogeneous Graph Language Model ...
In this paper, we propose a novel Semi-supervised learning framework for graph Anomaly Detection via Multi-view Contrastive Learning (SADMCL for abbreviation), which uses very few labels to promote detection performance. To be specific, we employ multi-view contrastive learning, i.e., sample-...
[arXiv 2022] Link Prediction with Contextualized Self-Supervision [paper] [arXiv 2022] Dual Space Graph Contrastive Learning [paper] [arXiv 2022] Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation [paper] [arXiv 2022] From Unsupervised to Few-shot Graph Anomaly Detection: A...
Many recent SSL methods have provided well-designed pretext tasks based contrastive learning that are applicable for graphs to deal with graph anomaly detection, the task of detecting anomalies (e.g., anomalous nodes, edges, sub-graphs) in static graphs. Note that in a static graph, oftentimes...
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical Guarantees, 📝CVPR, Code Transferable Graph Backdoor Attack, 📝RAID, Code Adversarial Robustness of Graph-based Anomaly Detection, 📝arXiv Label specificity attack: Change your label as I want, ...
In recent years, with deep learning development, graph-based deep anomaly detection has attracted more and more researchers' attention due to graph data's strong expression ability. However, at present, graph-based methods mainly focus on node-level anomaly detection, while edge-level anomaly ...
In recent years, with deep learning development, graph-based deep anomaly detection has attracted more and more researchers' attention due to graph data's strong expression ability. However, at present, graph-based methods mainly ...
In many anomaly detection tasks, labeled data are scarce, but data augmentation and contrastive learning can help reduce the dependency on labeled data. With minor adjustments, our GCPAL framework can be applied to various domains. 6 Conclusion and Future Work This paper proposes a novel graph ...
Learning a Deep ConvNet for Multi-label Classification with Partial Labels. CVPR 2019. paper Thibaut Durand, Nazanin Mehrasa, Greg Mori. Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection. CVPR 2019. paper Jia-Xing Zhong, Nannan Li, Weijie Kon...
Awesome papers (codes) about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs) and their applications (i.e. Recommender Systems). Survey Papers 2025 Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding (WSDM, 2025) [paper][code] ...