no code implementations • 3 Aug 2022 • Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Liang Hou, Xueqi Cheng
Despite the initial success of node injection attacks, we find that the injected nodes by existing methods are easy to be distinguished from the original normal nodes by defense methods and limiting their attack performance in practice.
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.