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Found 9 papers, 7 papers with code
Adversarial Immunization for Certifiable Robustness on Graphs
Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.
Single Node Injection Attack against Graph Neural Networks
In this paper, we focus on an extremely limited scenario of single node injection evasion attack, i. e., the attacker is only allowed to inject one single node during the test phase to hurt GNN's performance.
Signed Bipartite Graph Neural Networks
Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets.
INMO: A Model-Agnostic and Scalable Module for Inductive Collaborative Filtering
INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table.
Graph Adversarial Immunization for Certifiable Robustness
In this paper, we propose and formulate graph adversarial immunization, i. e., vaccinating part of graph structure to improve certifiable robustness of graph against any admissible adversarial attack.
Adversarial Camouflage for Node Injection Attack on Graphs
In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes.
Popularity Debiasing from Exposure to Interaction in Collaborative Filtering
In this paper, we propose a new criterion for popularity debiasing, i. e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion.
IDEA: Invariant Causal Defense for Graph Adversarial Robustness
Through modeling and analyzing the causal relationships in graph adversarial attacks, we design two invariance objectives to learn the causal features.
Robust Recommender System: A Survey and Future Directions
With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload.
