Yi Chang

11.5k total citations · 4 hit papers
201 papers, 5.7k citations indexed

About

Yi Chang is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Yi Chang has authored 201 papers receiving a total of 5.7k indexed citations (citations by other indexed papers that have themselves been cited), including 123 papers in Artificial Intelligence, 84 papers in Information Systems and 34 papers in Computer Vision and Pattern Recognition. Recurrent topics in Yi Chang's work include Information Retrieval and Search Behavior (45 papers), Web Data Mining and Analysis (38 papers) and Topic Modeling (37 papers). Yi Chang is often cited by papers focused on Information Retrieval and Search Behavior (45 papers), Web Data Mining and Analysis (38 papers) and Topic Modeling (37 papers). Yi Chang collaborates with scholars based in United States, China and United Kingdom. Yi Chang's co-authors include Chikashi Nobata, Achint Thomas, Joel Tetreault, Yashar Mehdad, Jiliang Tang, Anlei Dong, Zhaohui Zheng, Philip S. Yu, Huan Liu and Yue Wang and has published in prestigious journals such as Nature Communications, IEEE Transactions on Pattern Analysis and Machine Intelligence and Nature Cell Biology.

In The Last Decade

Yi Chang

186 papers receiving 5.4k 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 on Evaluation of Large Language Models

2024 1.0k citations Xu Wang, Jindong Wang et al. ACM Transactions on Intelligent Systems and Technology profile →
Abusive Language Detection in Online User Content

2016 666 citations Yi Chang et al. profile →
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction

2020 309 citations Yi Chang et al. profile →
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks

2022 185 citations Yi Chang et al. IEEE Transactions on Knowledge and Data Engineering profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yi Chang United States	35	3.7k	1.8k	767	576	465	201	5.7k
Jiafeng Guo China	36	3.8k 1.0×	1.9k 1.1×	955 1.2×	525 0.9×	300 0.6×	198	5.3k
Pádraig Cunningham Ireland	37	3.1k 0.8×	1.1k 0.6×	950 1.2×	633 1.1×	518 1.1×	205	5.9k
Andrew McCallum United States	29	4.0k 1.1×	2.2k 1.2×	481 0.6×	999 1.7×	511 1.1×	73	5.9k
Jaime Carbonell United States	45	6.6k 1.8×	2.3k 1.3×	983 1.3×	515 0.9×	582 1.3×	278	8.6k
Matthew Richardson United States	21	2.9k 0.8×	1.5k 0.8×	696 0.9×	1.2k 2.1×	426 0.9×	42	5.0k
Chenliang Li China	32	3.4k 0.9×	1.5k 0.8×	595 0.8×	417 0.7×	240 0.5×	134	4.5k
Evgeniy Gabrilovich United States	33	6.0k 1.6×	2.3k 1.3×	762 1.0×	416 0.7×	460 1.0×	83	7.8k
Marcos André Gonçalves Brazil	38	3.2k 0.8×	2.3k 1.3×	757 1.0×	579 1.0×	286 0.6×	313	5.8k
Jing Jiang Singapore	39	5.1k 1.4×	2.4k 1.3×	954 1.2×	1.3k 2.2×	516 1.1×	169	7.5k
Eugene Agichtein United States	39	4.2k 1.1×	4.1k 2.3×	543 0.7×	456 0.8×	437 0.9×	141	7.1k

Countries citing papers authored by Yi Chang

Since Specialization

Citations

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

Fields of papers citing papers by Yi Chang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yi Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Yi Chang. A scholar is included among the top collaborators of Yi Chang 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 Yi Chang. Yi Chang 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

Cao, Dandan, Yijun Liu, Yanfei Cheng, et al.. (2025). Time-series single-cell transcriptomic profiling of luteal-phase endometrium uncovers dynamic characteristics and its dysregulation in recurrent implantation failures. Nature Communications. 16(1). 137–137. 10 indexed citations

Guo, Ruocheng, Wanyu Wang, Xuetao Wei, et al.. (2025). STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation. 1530–1540.

Wang, Xu, Jindong Wang, Yuan Wu, et al.. (2024). A Survey on Evaluation of Large Language Models. ACM Transactions on Intelligent Systems and Technology. 15(3). 1–45. 1022 indexed citations breakdown →

Tang, Fan, et al.. (2024). Modality-Consistent Prompt Tuning With Optimal Transport. IEEE Transactions on Circuits and Systems for Video Technology. 35(3). 2499–2512.

Chen, Hechang, et al.. (2024). Boosting Weak-to-Strong Agents in Multiagent Reinforcement Learning via Balanced PPO. IEEE Transactions on Neural Networks and Learning Systems. 36(5). 9136–9149.

Huang, Changqin, Qionghao Huang, Xiaodi Huang, et al.. (2024). XKT: Toward Explainable Knowledge Tracing Model With Cognitive Learning Theories for Questions of Multiple Knowledge Concepts. IEEE Transactions on Knowledge and Data Engineering. 36(11). 7308–7325. 16 indexed citations

Piao, Haiyin, et al.. (2024). Discovering Expert-Level Air Combat Knowledge via Deep Excitatory-Inhibitory Factorized Reinforcement Learning. ACM Transactions on Intelligent Systems and Technology. 15(4). 1–28.

Wang, Li, et al.. (2023). A novel relation aware wrapper method for feature selection. Pattern Recognition. 140. 109566–109566. 36 indexed citations

Chen, Hechang, et al.. (2023). Generalized multi-agent competitive reinforcement learning with differential augmentation. Expert Systems with Applications. 238. 121760–121760. 3 indexed citations

10.

Hu, Liang, et al.. (2023). Enhancing Locally Adaptive Smoothing of Graph Neural Networks Via Laplacian Node Disagreement. IEEE Transactions on Knowledge and Data Engineering. 36(3). 1099–1112. 4 indexed citations

11.

Li, Ximing, et al.. (2023). TC-DWA:Text Clustering with Dual Word-Level Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 7113–7121. 2 indexed citations

12.

Hu, Liang, et al.. (2022). AdaNS: Adaptive negative sampling for unsupervised graph representation learning. Pattern Recognition. 136. 109266–109266. 5 indexed citations

13.

Xia, Xiaobo, Tongliang Liu, Bo Han, et al.. (2021). Robust early-learning: Hindering the memorization of noisy labels. International Conference on Learning Representations. 66 indexed citations

14.

Wei, Pengfei, et al.. (2020). MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler.. Neural Information Processing Systems. 33. 14463–14474. 2 indexed citations

15.

Xia, Congying, Chenwei Zhang, Xiaohui Yan, Yi Chang, & Philip L. H. Yu. (2018). Zero-shot User Intent Detection via Capsule Neural Networks. 3090–3099. 129 indexed citations

16.

Zhou, Tianyi, Hua Ouyang, Jeff Bilmes, Yi Chang, & Carlos Guestrin. (2016). Scaling Submodular Maximization via Pruned Submodularity Graphs. International Conference on Artificial Intelligence and Statistics. 316–324. 1 indexed citations

17.

Kang, Changsung, et al.. (2015). Learning to rank related entities in Web search. Neurocomputing. 166. 309–318. 11 indexed citations

18.

Huang, Zhiheng, Yi Chang, Bo Long, et al.. (2012). Iterative Viterbi A* Algorithm for K-Best Sequential Decoding. Meeting of the Association for Computational Linguistics. 611–619. 11 indexed citations

19.

Bai, Jing, Fernando Díaz, Yi Chang, Zhaohui Zheng, & Keke Chen. (2010). Cross-Market Model Adaptation with Pairwise Preference Data for Web Search Ranking. Journal of Bioresource Management. 18–26. 3 indexed citations

20.

Zhang, Ruiqiang, Yi Chang, Zhaohui Zheng, Donald Metzler, & Jian‐Yun Nie. (2009). Search Engine Adaptation by Feedback Control Adjustment for Time-sensitive Query. North American Chapter of the Association for Computational Linguistics. 165–168. 3 indexed citations

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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