James Caverlee

8.8k total citations · 2 hit papers
166 papers, 4.6k citations indexed

About

James Caverlee is a scholar working on Information Systems, Artificial Intelligence and Statistical and Nonlinear Physics. According to data from OpenAlex, James Caverlee has authored 166 papers receiving a total of 4.6k indexed citations (citations by other indexed papers that have themselves been cited), including 113 papers in Information Systems, 81 papers in Artificial Intelligence and 39 papers in Statistical and Nonlinear Physics. Recurrent topics in James Caverlee's work include Recommender Systems and Techniques (49 papers), Spam and Phishing Detection (42 papers) and Complex Network Analysis Techniques (39 papers). James Caverlee is often cited by papers focused on Recommender Systems and Techniques (49 papers), Spam and Phishing Detection (42 papers) and Complex Network Analysis Techniques (39 papers). James Caverlee collaborates with scholars based in United States, China and South Korea. James Caverlee's co-authors include Zhiyuan Cheng, Steve Webb, Kyumin Lee, Kyumin Lee, Ziwei Zhu, Jianling Wang, Xia Hu, Ying Ding, A. E. Frazho and Erjia Yan and has published in prestigious journals such as Information Sciences, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

James Caverlee

156 papers receiving 4.4k citations

Hit Papers

You are where you tweet 2010 2026 2015 2020 2010 2010 200 400 600
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.

  1. You are where you tweet
    2010 743 citations Zhiyuan Cheng, James Caverlee et al. profile →
  2. Uncovering social spammers
    2010 422 citations Kyumin Lee, James Caverlee et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James Caverlee United States 33 2.5k 2.1k 867 758 734 166 4.6k
Huiji Gao United States 22 1.5k 0.6× 1.3k 0.6× 552 0.6× 431 0.6× 568 0.8× 51 3.2k
Aixin Sun Singapore 45 3.7k 1.5× 4.9k 2.3× 1.1k 1.2× 836 1.1× 678 0.9× 223 8.7k
Prasenjit Mitra United States 39 2.0k 0.8× 3.4k 1.6× 786 0.9× 722 1.0× 1.2k 1.6× 234 5.8k
Liangjie Hong United States 22 1.4k 0.6× 1.7k 0.8× 833 1.0× 227 0.3× 405 0.6× 43 2.9k
Kevin Chen–Chuan Chang United States 35 2.3k 0.9× 3.4k 1.6× 1.2k 1.4× 1.9k 2.5× 199 0.3× 144 6.0k
Francesco Bonchi Italy 42 1.8k 0.7× 3.1k 1.4× 3.1k 3.6× 1.2k 1.6× 870 1.2× 188 6.8k
Defu Lian China 33 2.9k 1.1× 2.7k 1.2× 313 0.4× 549 0.7× 172 0.2× 168 4.8k
Qi He United States 21 1.7k 0.7× 1.9k 0.9× 1.3k 1.5× 465 0.6× 424 0.6× 64 3.6k
Jussara M. Almeida Brazil 38 1.9k 0.8× 1.6k 0.8× 852 1.0× 3.4k 4.5× 1.3k 1.8× 246 6.4k
Virgı́lio Almeida Brazil 44 3.0k 1.2× 1.8k 0.8× 1.1k 1.2× 3.4k 4.5× 1.1k 1.5× 202 6.6k

Countries citing papers authored by James Caverlee

Since Specialization
Citations

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

Fields of papers citing papers by James Caverlee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of James Caverlee

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

All Works

20 of 20 papers shown
1.
Yang, Rui, Edison Marrese-Taylor, Yuhe Ke, et al.. (2024). KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques. 155–166. 14 indexed citations
2.
Coleman, Benjamin, Wang-Cheng Kang, Jianmo Ni, et al.. (2024). Improving Data Efficiency for Recommenders and LLMs. 790–792.
3.
Ding, Kaize, Jianling Wang, Jundong Li, James Caverlee, & Huan Liu. (2023). Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification. ACM Transactions on Knowledge Discovery from Data. 18(4). 1–18. 4 indexed citations
4.
Wang, Jianling, et al.. (2023). Closed-book Question Generation via Contrastive Learning. 3150–3162. 6 indexed citations
5.
Cheng, Derek Zhiyuan, Tiansheng Yao, Xinyang Yi, et al.. (2023). Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN). 5608–5617. 9 indexed citations
6.
Liu, Weiwen, Yin Zhang, Jianling Wang, et al.. (2021). Item Relationship Graph Neural Networks for E-Commerce. IEEE Transactions on Neural Networks and Learning Systems. 33(9). 4785–4799. 47 indexed citations
7.
Caverlee, James, et al.. (2021). Summarizing User-Contributed Comments. Proceedings of the International AAAI Conference on Web and Social Media. 5(1). 534–537. 5 indexed citations
8.
He, Yun, Ziwei Zhu, Yin Zhang, Qin Chen, & James Caverlee. (2020). Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition. 4604–4614. 59 indexed citations
9.
Wang, Jianling, et al.. (2020). Time to Shop for Valentine's Day. 645–653. 26 indexed citations
10.
Qiu, Chenxi, et al.. (2018). CrowdEval: A Cost-Efficient Strategy to Evaluate Crowdsourced Worker's Reliability. IrInSubria (University of Insubria). 1486–1494. 7 indexed citations
11.
Niu, Wei, et al.. (2016). Community-based geospatial tag estimation. 279–286. 1 indexed citations
12.
Caverlee, James, Zhiyuan Cheng, Daniel Z. Sui, & Krishna Kamath. (2013). Towards Geo-Social Intelligence: Mining, Analyzing, and Leveraging Geospatial Footprints in Social Media.. IEEE Data(base) Engineering Bulletin. 36. 33–41. 11 indexed citations
13.
Kamath, Krishna & James Caverlee. (2013). Spatio-temporal meme prediction. 1341–1350. 12 indexed citations
14.
Lee, Kyumin, James Caverlee, Zhiyuan Cheng, & Daniel Z. Sui. (2013). Campaign extraction from social media. ACM Transactions on Intelligent Systems and Technology. 5(1). 1–28. 26 indexed citations
15.
Popescu, Ana-Maria, Krishna Kamath, & James Caverlee. (2013). Mining Potential Domain Expertise in Pinterest.. 5 indexed citations
16.
Ding, Ying, et al.. (2009). Profiling Social Networks: A Social Tagging Perspective. D-Lib Magazine. 15(3/4). 8 indexed citations
17.
Webb, Steve, James Caverlee, & Calton Pu. (2008). Social Honeypots: Making Friends With A Spammer Near You.. 77 indexed citations
18.
Caverlee, James, Ling Liu, & Steve Webb. (2008). Socialtrust. 104–114. 68 indexed citations
19.
Webb, Steve, James Caverlee, & Calton Pu. (2007). Characterizing Web Spam Using Content and HTTP Session Analysis.. 21 indexed citations
20.
Webb, Steve, James Caverlee, & Calton Pu. (2006). Introducing the Webb Spam Corpus: Using Email Spam to Identify Web Spam Automatically. 66 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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