1 code implementation • 3 Dec 2024 • Guanghui Zhu, Zipeng Ji, Jingyan Chen, LiMin Wang, Chunfeng Yuan, Yihua Huang
GNAS (Graph Neural Architecture Search) has demonstrated great effectiveness in automatically designing the optimal graph neural architectures for multiple downstream tasks, such as node classification and link prediction.
1 code implementation • 15 Aug 2023 • Guanghui Zhu, Mengyu Chen, Chunfeng Yuan, Yihua Huang
To this end, we propose a totally new method named partial graph attack (PGA), which selects the vulnerable nodes as attack targets.
no code implementations • 4 Jul 2023 • Guanghui Zhu, Zhennan Zhu, Hongyang Chen, Chunfeng Yuan, Yihua Huang
Then, we propose a novel framework to utilize the rich type semantic information in heterogeneous graphs comprehensively, namely HAGNN (Hybrid Aggregation for Heterogeneous GNNs).
1 code implementation • 8 Jan 2023 • Guanghui Zhu, Zhennan Zhu, Wenjie Wang, Zhuoer Xu, Chunfeng Yuan, Yihua Huang
Moreover, to improve the performance of the downstream graph learning task, attribute completion and the training of the heterogeneous GNN should be jointly optimized rather than viewed as two separate processes.
1 code implementation • 7 Oct 2022 • Zhuoer Xu, Guanghui Zhu, Changhua Meng, Shiwen Cui, ZhenZhe Ying, Weiqiang Wang, Ming Gu, Yihua Huang
In this paper, we propose an efficient automated attacker called A2 to boost AT by generating the optimal perturbations on-the-fly during training.
no code implementations • 21 Jul 2022 • Jingfan Chen, Wenqi Fan, Guanghui Zhu, Xiangyu Zhao, Chunfeng Yuan, Qing Li, Yihua Huang
Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles (i. e., a set of items that fake users have interacted with) into a target recommender system to achieve malicious purposes, such as promote or demote a set of target items.
no code implementations • 12 Mar 2022 • Guanghui Zhu, Haojun Hou, Jingfan Chen, Chunfeng Yuan, Yihua Huang
Specifically, TRASA first converts the session to a graph and then encodes the shortest path between items through the gated recurrent unit as their transition relation.
1 code implementation • 17 Oct 2020 • Guanghui Zhu, Zhuoer Xu, Xu Guo, Chunfeng Yuan, Yihua Huang
Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.
1 code implementation • 29 May 2020 • Jingfan Chen, Guanghui Zhu, Chunfeng Yuan, Yihua Huang
Bayesian optimization is a broadly applied methodology to optimize the expensive black-box function.