Xihong Yang

1.3k total citations · 2 hit papers
29 papers, 678 citations indexed

About

Xihong Yang is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Xihong Yang has authored 29 papers receiving a total of 678 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Artificial Intelligence, 8 papers in Information Systems and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Xihong Yang's work include Advanced Graph Neural Networks (14 papers), Complex Network Analysis Techniques (8 papers) and Recommender Systems and Techniques (7 papers). Xihong Yang is often cited by papers focused on Advanced Graph Neural Networks (14 papers), Complex Network Analysis Techniques (8 papers) and Recommender Systems and Techniques (7 papers). Xihong Yang collaborates with scholars based in China, United Arab Emirates and Hong Kong. Xihong Yang's co-authors include Xinwang Liu, Sihang Zhou, Yue Liu, Wenxuan Tu, En Zhu, Ke Liang, Siwei Wang, Liang Li, Yi Wen and Suyuan Liu and has published in prestigious journals such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering and Neural Networks.

In The Last Decade

Xihong Yang

29 papers receiving 670 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Graph Clustering via Dual Correlation Reduction

2022 141 citations Yue Liu, Wenxuan Tu et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →
Simple Contrastive Graph Clustering

2023 99 citations Yue Liu, Xihong Yang et al. IEEE Transactions on Neural Networks and Learning Systems profile →

Peers

Countries citing papers authored by Xihong Yang

Since Specialization

Citations

This map shows the geographic impact of Xihong Yang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xihong Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xihong Yang more than expected).

Fields of papers citing papers by Xihong Yang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xihong Yang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xihong Yang. The network helps show where Xihong Yang may publish in the future.

Co-authorship network of co-authors of Xihong Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Xihong Yang. A scholar is included among the top collaborators of Xihong Yang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xihong Yang. Xihong Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

#	Work	Indexed citations
1	Dual Test-Time Training for Out-of-Distribution Recommender System 2025 ·IEEE Transactions on Knowledge and Data Engineering ·Xihong Yang, Yiqi Wang, Jin Chen, Wenqi Fan, Xiangyu Zhao, En Zhu, Xinwang Liu, Defu Lian	5
2	Asymmetric double-winged multi-view clustering network for exploring diverse and consistent information 2024 ·Neural Networks ·(unknown), Xihong Yang, Siwei Wang, (unknown), (unknown)	1
3	DiscoGNN: A Sample-Efficient Framework for Self-Supervised Graph Representation Learning 2024 ·Jun Xia, Shaorong Chen, Yue Liu, Zhangyang Gao, Jiangbin Zheng, Xihong Yang, Stan Z. Li	2
4	View Gap Matters: Cross-view Topology and Information Decoupling for Multi-view Clustering 2024 ·F. Wang, (unknown), Zhibin Dong, Xihong Yang, (unknown), Xinwang Liu, Xinzhong Zhu, Siwei Wang, Tianrui Liu, En Zhu	1
5	A Fully Test-time Training Framework for Semi-supervised Node Classification on Out-of-Distribution Graphs 2024 ·ACM Transactions on Knowledge Discovery from Data ·(unknown), Yiqi Wang, Xihong Yang, En Zhu	3
6	Improved Dual Correlation Reduction Network With Affinity Recovery 2024 ·IEEE Transactions on Neural Networks and Learning Systems ·Yue Liu, Sihang Zhou, Xihong Yang, Xinwang Liu, Wenxuan Tu, Liang Li, Xin Xu, Fuchun Sun	5
7	GraphLearner: Graph Node Clustering with Fully Learnable Augmentation 2024 ·Xihong Yang, Erxue Min, Ke Liang, Yue Liu, Siwei Wang, Sihang Zhou, Huijun Wu, Xinwang Liu, En Zhu	3
8	SARF: Aliasing Relation–Assisted Self-Supervised Learning for Few-Shot Relation Reasoning 2024 ·IEEE Transactions on Neural Networks and Learning Systems ·Lingyuan Meng, Ke Liang, Bin Xiao, Sihang Zhou, Yue Liu, Meng Liu, Xihong Yang, Xinwang Liu, Jinyan Li	12
9	Test-Time Training on Graphs with Large Language Models (LLMs) 2024 ·(unknown), Yiqi Wang, Xihong Yang, Siwei Wang, (unknown), (unknown), (unknown), En Zhu, Xinwang Liu	1
10	Mixed Graph Contrastive Network for Semi-supervised Node Classification 2024 ·ACM Transactions on Knowledge Discovery from Data ·Xihong Yang, Yiqi Wang, Yue Liu, Yi Wen, Lingyuan Meng, Sihang Zhou, Xinwang Liu, En Zhu	11
11	CONVERT: Contrastive Graph Clustering with Reliable Augmentation 2023 ·Xihong Yang, Cheng Tan, Yue Liu, Ke Liang, Siwei Wang, Sihang Zhou, Jun Xia, Stan Z. Li, Xinwang Liu, En Zhu	29
12	Knowledge Graph Contrastive Learning Based on Relation-Symmetrical Structure 2023 ·IEEE Transactions on Knowledge and Data Engineering ·Ke Liang, Yue Liu, Sihang Zhou, Wenxuan Tu, Yi Wen, Xihong Yang, Xiangjun Dong, Xinwang Liu	69
13	Hard Sample Aware Network for Contrastive Deep Graph Clustering 2023 ·Proceedings of the AAAI Conference on Artificial Intelligence ·Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, (unknown), (unknown)	64
14	Topological Structure Learning for Weakly-Supervised Out-of-Distribution Detection 2023 ·(unknown), (unknown), Zhongyi Han, Xihong Yang, Yilong Yin	1
15	DealMVC: Dual Contrastive Calibration for Multi-view Clustering 2023 ·Xihong Yang, (unknown), Siwei Wang, Ke Liang, Yue Liu, Yi Wen, Suyuan Liu, Sihang Zhou, Xinwang Liu, En Zhu	60
16	Reinforcement Graph Clustering with Unknown Cluster Number 2023 ·Yue Liu, Ke Liang, Jun Xia, Xihong Yang, Sihang Zhou, Meng Liu, Xinwang Liu, Stan Z. Li	12
17	Simple Contrastive Graph Clustering breakdown → 2023 ·IEEE Transactions on Neural Networks and Learning Systems ·Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Siwei Wang, Ke Liang, Wenxuan Tu, Liang Li	99
18	Task-Related Saliency for Few-Shot Image Classification 2023 ·IEEE Transactions on Neural Networks and Learning Systems ·(unknown), Lei Luo, Sihang Zhou, (unknown), Xihong Yang, Xinwang Liu, En Zhu	6
19	Patch-Mixing Contrastive Regularization for Few-Label Semi-Supervised Learning 2023 ·IEEE Transactions on Artificial Intelligence ·(unknown), Xin Xu, Yujun Zeng, Xihong Yang	2
20	RP-HPLC determination of entecavir sodium tablets 2007 ·Yaowu fenxi zazhi ·Xihong Yang	1

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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