no code implementations • 26 Apr 2024 • Shuchang Tao, Liuyi Yao, Hanxing Ding, Yuexiang Xie, Qi Cao, Fei Sun, Jinyang Gao, HuaWei Shen, Bolin Ding
Specifically, the order-preserving reward incentivizes the model to verbalize greater confidence for responses of higher quality to align the order of confidence and quality.
no code implementations • 5 Sep 2023 • Kaike Zhang, Qi Cao, Fei Sun, Yunfan Wu, Shuchang Tao, HuaWei Shen, Xueqi Cheng
With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload.
no code implementations • 25 May 2023 • Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Bingbing Xu, Xueqi Cheng
To address these limitations, we analyze the causalities in graph adversarial attacks and conclude that causal features are key to achieve graph adversarial robustness, owing to their determinedness for labels and invariance across attacks.
1 code implementation • 9 May 2023 • YuanHao Liu, Qi Cao, HuaWei Shen, Yunfan Wu, Shuchang Tao, Xueqi Cheng
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.
1 code implementation • 16 Feb 2023 • Shuchang Tao, HuaWei Shen, Qi Cao, Yunfan Wu, Liang Hou, Xueqi Cheng
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.
1 code implementation • 3 Aug 2022 • Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Liang Hou, Fei Sun, Xueqi Cheng
In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes.
1 code implementation • 30 Aug 2021 • Shuchang Tao, Qi Cao, HuaWei Shen, JunJie Huang, Yunfan Wu, Xueqi Cheng
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.
1 code implementation • 22 Aug 2021 • JunJie Huang, HuaWei Shen, Qi Cao, Shuchang Tao, Xueqi Cheng
Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets.
1 code implementation • 12 Jul 2021 • Yunfan Wu, Qi Cao, HuaWei Shen, Shuchang Tao, Xueqi Cheng
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.
2 code implementations • 19 Jul 2020 • Shuchang Tao, Hua-Wei Shen, Qi Cao, Liang Hou, Xue-Qi Cheng
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.