Search Results for author:
Found 9 papers, 3 papers with code
Collective Certified Robustness against Graph Injection Attacks
We investigate certified robustness for GNNs under graph injection attacks.
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.
Cost Aware Untargeted Poisoning Attack against Graph Neural Networks,
Graph Neural Networks (GNNs) have become widely used in the field of graph mining.
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks
Furthermore, we extend two state-of-the-art certified robustness frameworks to address node injection attacks and compare our approach against them.
Coupled-Space Attacks against Random-Walk-based Anomaly Detection
In addition, we conduct transfer attack experiments in a black-box setting, which show that our feature attack significantly decreases the anomaly scores of target nodes.
Towards Adversarially Robust Recommendation from Adaptive Fraudster Detection
The robustness of recommender systems under node injection attacks has garnered significant attention.
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection
Graph-based Anomaly Detection (GAD) is becoming prevalent due to the powerful representation abilities of graphs as well as recent advances in graph mining techniques.
Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks
In addition, a pooling method based on percentile is proposed to improve the accuracy of the model.
