Erxue Min

1.1k total citations · 1 hit paper
12 papers, 604 citations indexed

About

Erxue Min is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Erxue Min has authored 12 papers receiving a total of 604 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 5 papers in Information Systems. Recurrent topics in Erxue Min's work include Complex Network Analysis Techniques (4 papers), Recommender Systems and Techniques (3 papers) and Stochastic Gradient Optimization Techniques (3 papers). Erxue Min is often cited by papers focused on Complex Network Analysis Techniques (4 papers), Recommender Systems and Techniques (3 papers) and Stochastic Gradient Optimization Techniques (3 papers). Erxue Min collaborates with scholars based in China, United Kingdom and United States. Erxue Min's co-authors include Jun Long, Jianjing Cui, Qiang Liu, Gen Zhang, Xifeng Guo, Wei Chen, Sophia Ananiadou, Yu Rong, Yatao Bian and Junzhou Huang and has published in prestigious journals such as IEEE Access, IEEE Multimedia and Security and Communication Networks.

In The Last Decade

Erxue Min

12 papers receiving 576 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.

A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture

2018 340 citations Erxue Min, Xifeng Guo et al. IEEE Access profile →

Peers

Countries citing papers authored by Erxue Min

Since Specialization

Citations

This map shows the geographic impact of Erxue Min'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 Erxue Min with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Erxue Min more than expected).

Fields of papers citing papers by Erxue Min

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Erxue Min. 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 Erxue Min. The network helps show where Erxue Min may publish in the future.

Co-authorship network of co-authors of Erxue Min

This figure shows the co-authorship network connecting the top 25 collaborators of Erxue Min. A scholar is included among the top collaborators of Erxue Min 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 Erxue Min. Erxue Min is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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12 of 12 papers shown

#	Work	Indexed citations
1	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
2	Hyperbolic Contrastive Learning for Cross-Domain Recommendation 2024 ·(unknown), Heng Chang, (unknown), (unknown), (unknown), (unknown), Shuaiqiang Wang, Dawei Yin, Erxue Min	4
3	Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction 2023 ·Rare & Special e-Zone (The Hong Kong University of Science and Technology) ·Erxue Min, Da Luo, Kangyi Lin, (unknown), Y. Liu	4
4	PESTO: A Post-User Fusion Network for Rumour Detection on Social Media 2023 ·Erxue Min, Sophia Ananiadou	3
5	Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media 2022 ·Proceedings of the ACM Web Conference 2022 ·Erxue Min, Yu Rong, Yatao Bian, Tingyang Xu, Peilin Zhao, Junzhou Huang, Sophia Ananiadou	43
6	Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer 2022 ·Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval ·Erxue Min, Yu Rong, Tingyang Xu, Yatao Bian, (unknown), Kangyi Lin, Junzhou Huang, Sophia Ananiadou, Peilin Zhao	27
7	Sentiment-Aware Emoji Insertion Via Sequence Tagging 2021 ·IEEE Multimedia ·(unknown), Yiping Song, Xingkong Ma, Erxue Min, Bo Liu	2
8	PTfuzz: Guided Fuzzing With Processor Trace Feedback 2018 ·IEEE Access ·Gen Zhang, (unknown), (unknown), (unknown), Erxue Min	51
9	TR-IDS: Anomaly-Based Intrusion Detection through Text-Convolutional Neural Network and Random Forest 2018 ·Security and Communication Networks ·Erxue Min, Jun Long, Qiang Liu, Jianjing Cui, Wei Chen	122
10	Analysis of the Variance Reduction in SVRG and a New Acceleration Method 2018 ·IEEE Access ·Erxue Min, Jun Long, Jianjing Cui	3
11	A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture breakdown → 2018 ·IEEE Access ·Erxue Min, Xifeng Guo, Qiang Liu, Gen Zhang, Jianjing Cui, Jun Long	340
12	SVRG with adaptive epoch size 2017 ·Erxue Min, Yawei Zhao, Jun Long, Chengkun Wu, Kuan Li, Jianping Yin	2

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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