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Snap Inc.; University of Notre Dame
- https://tzhao.io/
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GAug Public
AAAI'21: Data Augmentation for Graph Neural Networks
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A curated list of graph data augmentation papers.
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Graph-Anomaly-Loss Public
TNNLS: A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning; CIKM'20: Error-bounded Graph Anomaly Loss for GNNs.
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Materials for SDM 2023 tutorial: Augmentation Methods for Graph Learning
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snap-research-website Public
Forked from superbnoo/snap-research-websitehttps://research.snap.com/
HTML UpdatedAug 20, 2022 -
graph-based-deep-learning-literature Public
Forked from naganandy/graph-based-deep-learning-literaturelinks to conference publications in graph-based deep learning
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AOO Public
BigData'18: Actionable Objective Optimization for Suspicious Behavior Detection on Large Bipartite Graphs
Python MIT License UpdatedDec 8, 2020 -
DeepFD-pyTorch Public
A PyTorch implementation of DeepFD (Deep Structure Learning for Fraud Detection)
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GNNs-easy-to-use Public
An PyTorch implementation of graph neural networks (GCN, GraphSAGE and GAT) that can be simply imported and used.