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Found 8 papers, 0 papers with code
Adversarially Robust Signed Graph Contrastive Learning from Balance Augmentation
Similar to how structure learning can restore unsigned graphs, balance learning can be applied to signed graphs by improving the balance degree of the poisoned graph.
Universally Robust Graph Neural Networks by Preserving Neighbor Similarity
This theoretical proof explains the empirical observations that the graph attacker tends to connect dissimilar node pairs based on the similarities of neighbor features instead of ego features both on homophilic and heterophilic graphs.
Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach
Graph anomaly detection (GAD) has achieved success and has been widely applied in various domains, such as fraud detection, cybersecurity, finance security, and biochemistry.
Homophily-Driven Sanitation View for Robust Graph Contrastive Learning
We conduct extensive experiments to evaluate the performance of our proposed model, GCHS (Graph Contrastive Learning with Homophily-driven Sanitation View), against two state of the art structural attacks on GCL.
Labeled Subgraph Entropy Kernel
In this paper, we propose a novel labeled subgraph entropy graph kernel, which performs well in structural similarity assessment.
Simple yet Effective Gradient-Free Graph Convolutional Networks
Linearized Graph Neural Networks (GNNs) have attracted great attention in recent years for graph representation learning.
Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach
The architecture is based on a novel mapping from real-world data to Hilbert space.
Two-level Graph Neural Network
Moreover, we provide a mathematical analysis of the LPI problem which demonstrates that subgraph-level information is beneficial to overcoming the problems associated with LPI.
