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.
MVAE: Multimodal Variational Autoencoder for Fake News Detection
2019430 citationsDhruv Khattar, Manish Gupta et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Vasudeva Varma
Since
Specialization
Citations
This map shows the geographic impact of Vasudeva Varma'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 Vasudeva Varma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vasudeva Varma more than expected).
This network shows the impact of papers produced by Vasudeva Varma. 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 Vasudeva Varma. The network helps show where Vasudeva Varma may publish in the future.
Co-authorship network of co-authors of Vasudeva Varma
This figure shows the co-authorship network connecting the top 25 collaborators of Vasudeva Varma.
A scholar is included among the top collaborators of Vasudeva Varma 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 Vasudeva Varma. Vasudeva Varma is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Agrawal, Yash, V. K. Anand, Manish Gupta, S. Arunachalam, & Vasudeva Varma. (2021). Goal-Directed Extractive Summarization of Financial Reports. Digital Eprints Services at ISB (DESI) (Indian School of Business). 2817–2821.2 indexed citations
6.
Varma, Vasudeva, et al.. (2021). Knowledge-based Neural Framework for Sexism Detection and Classification.. 402–414.5 indexed citations
7.
Gupta, Manish, et al.. (2017). SSAS: Semantic Similarity for Abstractive Summarization. International Joint Conference on Natural Language Processing. 2. 198–203.2 indexed citations
8.
Palshikar, Girish Keshav, et al.. (2017). TCS Research at TAC 2017: Joint Extraction of Entities and Relations from Drug Labels using an Ensemble of Neural Networks.. Theory and applications of categories.1 indexed citations
9.
Kumar, Vaibhav, et al.. (2017). Deep Neural Architecture for News Recommendation.. CLEF (Working Notes).18 indexed citations
Kumar, Niraj, Kannan Srinathan, & Vasudeva Varma. (2011). Using Unsupervised System with least linguistic features for TAC-AESOP Task.. Theory and applications of categories.3 indexed citations
16.
Kumar, Niraj, Kannan Srinathan, & Vasudeva Varma. (2010). Evaluating Information Coverage in Machine Generated Summary and Variable Length Documents.. 163.1 indexed citations
17.
Varma, Vasudeva, et al.. (2009). Passage Retrieval Using Answer Type Profiles in Question Answering. Pacific Asia Conference on Language, Information, and Computation. 2. 559–568.1 indexed citations
18.
Varma, Vasudeva, et al.. (2009). Exploiting the Use of Prior Probabilities for Passage Retrieval in Question Answering. Recent Advances in Natural Language Processing. 99–102.1 indexed citations
19.
Pingali, Prasad, et al.. (2008). A Character n-gram Based Approach for Improved Recall in Indian Language NER. International Joint Conference on Natural Language Processing. 67–74.16 indexed citations
20.
Pingali, Prasad & Vasudeva Varma. (2007). Multi-lingual Indexing Support for CLIR using Language Modeling.. IEEE Data(base) Engineering Bulletin. 30. 70–85.3 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.