Hemank Lamba

1.5k total citations · 1 hit paper
36 papers, 821 citations indexed

About

Hemank Lamba is a scholar working on Artificial Intelligence, Information Systems and Statistical and Nonlinear Physics. According to data from OpenAlex, Hemank Lamba has authored 36 papers receiving a total of 821 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Artificial Intelligence, 14 papers in Information Systems and 12 papers in Statistical and Nonlinear Physics. Recurrent topics in Hemank Lamba's work include Complex Network Analysis Techniques (11 papers), Spam and Phishing Detection (9 papers) and Misinformation and Its Impacts (5 papers). Hemank Lamba is often cited by papers focused on Complex Network Analysis Techniques (11 papers), Spam and Phishing Detection (9 papers) and Misinformation and Its Impacts (5 papers). Hemank Lamba collaborates with scholars based in United States, India and China. Hemank Lamba's co-authors include Ponnurangam Kumaraguru, Aditi Gupta, Anupam Joshi, Kit T. Rodolfa, Rayid Ghani, Jürgen Pfeffer, Momin M. Malik, Emaad Manzoor, Leman Akoglu and Neil Shah and has published in prestigious journals such as SHILAP Revista de lepidopterología, IBM Journal of Research and Development and Soft Computing.

In The Last Decade

Hemank Lamba

32 papers receiving 780 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.

Faking Sandy

2013 356 citations Hemank Lamba, Ponnurangam Kumaraguru et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Hemank Lamba United States	12	443	317	301	158	143	36	821
Aditi Gupta India	12	667 1.5×	499 1.6×	300 1.0×	230 1.5×	197 1.4×	37	975
Ayşe Göker United Kingdom	13	175 0.4×	395 1.2×	343 1.1×	203 1.3×	135 0.9×	40	880
Shady Elbassuoni Lebanon	19	357 0.8×	340 1.1×	822 2.7×	126 0.8×	303 2.1×	55	1.3k
Michal Jacovi Israel	16	162 0.4×	488 1.5×	228 0.8×	158 1.0×	130 0.9×	33	821
Richard McCreadie United Kingdom	18	127 0.3×	415 1.3×	515 1.7×	154 1.0×	104 0.7×	66	885
Sameena Shah United States	14	308 0.7×	214 0.7×	406 1.3×	216 1.4×	66 0.5×	44	760
Xichen Zhang Canada	9	581 1.3×	406 1.3×	361 1.2×	63 0.4×	94 0.7×	29	809
Benjamin Markines United States	8	165 0.4×	386 1.2×	365 1.2×	484 3.1×	79 0.6×	12	913
Tamer Elsayed Qatar	19	230 0.5×	410 1.3×	730 2.4×	66 0.4×	88 0.6×	88	1.1k
Fabian Abel Germany	14	137 0.3×	432 1.4×	383 1.3×	247 1.6×	139 1.0×	39	820

Countries citing papers authored by Hemank Lamba

Since Specialization

Citations

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

Fields of papers citing papers by Hemank Lamba

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hemank Lamba

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

All Works

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20 of 20 papers shown

1.

Cohen, Daniel J., et al.. (2025). Explain then Rank: Scale Calibration of Neural Rankers Using Natural Language Explanations from LLMs. 22716–22730.

2.

Lamba, Hemank, et al.. (2024). CFE2: Counterfactual Editing for Search Result Explanation. 145–155. 1 indexed citations

3.

Amarasinghe, Kasun, Kit T. Rodolfa, Hemank Lamba, & Rayid Ghani. (2023). Explainable machine learning for public policy: Use cases, gaps, and research directions. SHILAP Revista de lepidopterología. 5. 24 indexed citations

4.

Lamba, Hemank, et al.. (2022). "This is damn slick!". 2116–2129. 11 indexed citations

5.

Malik, Momin M., et al.. (2022). Glowing Experience or Bad Trip? A Quantitative Analysis of User Reported Drug Experiences on Erowid.org. Proceedings of the International AAAI Conference on Web and Social Media. 16. 675–686. 1 indexed citations

6.

Malik, Momin M., et al.. (2021). Population Bias in Geotagged Tweets. Proceedings of the International AAAI Conference on Web and Social Media. 9(4). 18–27. 54 indexed citations

7.

Rodolfa, Kit T., Hemank Lamba, & Rayid Ghani. (2020). Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy. arXiv (Cornell University). 56 indexed citations

8.

Klug, Daniel, et al.. (2020). Need for Tweet. Zenodo (CERN European Organization for Nuclear Research). 322–326. 16 indexed citations

9.

Wei, Te-En, Hahn-Ming Lee, Albert B. Jeng, Hemank Lamba, & Christos Faloutsos. (2019). WebHound: a data-driven intrusion detection from real-world web access logs. Soft Computing. 23(22). 11947–11965. 4 indexed citations

10.

Lamba, Hemank, et al.. (2018). Stop the KillFies! Using Deep Learning Models to Identify Dangerous Selfies. 1341–1345. 3 indexed citations

11.

Lamba, Hemank, et al.. (2017). From Camera to Deathbed: Understanding Dangerous Selfies on Social Media. Proceedings of the International AAAI Conference on Web and Social Media. 11(1). 576–579. 5 indexed citations

12.

Shah, Neil, Hemank Lamba, Alex Beutel, & Christos Faloutsos. (2017). The Many Faces of Link Fraud. 1069–1074. 19 indexed citations

13.

Lamba, Hemank, et al.. (2017). Model-based Cluster Analysis for Identifying Suspicious Activity Sequences in Software. 17–22. 3 indexed citations

14.

Lamba, Hemank & Jürgen Pfeffer. (2016). Maximizing the Spread of Positive Influence by Deadline. 67–68. 2 indexed citations

15.

Lamba, Hemank, et al.. (2016). Incorporating Side Information in Tensor Completion. 65–66. 15 indexed citations

16.

Lamba, Hemank, Momin M. Malik, & Jürgen Pfeffer. (2015). A Tempest in a Teacup? Analyzing Firestorms on Twitter. Figshare. 17–24. 24 indexed citations

17.

Liu, Leslie S., Chunhua Yang, Yu Wang, et al.. (2014). SoLoMo analytics for telco Big Data monetization. IBM Journal of Research and Development. 58(5/6). 9:1–9:13. 8 indexed citations

18.

Narayanam, Ramasuri, Dinesh Garg, & Hemank Lamba. (2014). Discovering signature of social networks with application to community detection. 1–7.

19.

Jennings, Nicholas R., et al.. (1999). Cooperating Agents for 3D Scientific Data Interpretation. ePrints Soton (University of Southampton). 3 indexed citations

20.

Jennings, Nicholas R., et al.. (1998). 3D Scientific Data Interpretation Using Cooperating Agents. ePrints Soton (University of Southampton). 1 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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