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.
Limiting the spread of misinformation in social networks
2011528 citationsDivyakant Agrawal, Amr El Abbadi et al.profile →
Big data and cloud computing
2011344 citationsDivyakant Agrawal, Amr El Abbadi 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 Divyakant Agrawal
Since
Specialization
Citations
This map shows the geographic impact of Divyakant Agrawal'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 Divyakant Agrawal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Divyakant Agrawal more than expected).
Fields of papers citing papers by Divyakant Agrawal
This network shows the impact of papers produced by Divyakant Agrawal. 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 Divyakant Agrawal. The network helps show where Divyakant Agrawal may publish in the future.
Co-authorship network of co-authors of Divyakant Agrawal
This figure shows the co-authorship network connecting the top 25 collaborators of Divyakant Agrawal.
A scholar is included among the top collaborators of Divyakant Agrawal 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 Divyakant Agrawal. Divyakant Agrawal is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Agrawal, Divyakant, et al.. (2017). Leader or majority: why have one when you can have both? improving read scalability in raft-like consensus protocols. IEEE International Conference on Cloud Computing Technology and Science. 14–14.11 indexed citations
Nawab, Faisal, Divyakant Agrawal, & Amr El Abbadi. (2013). Message Futures: Fast Commitment of Transactions in Multi-datacenter Environments. Conference on Innovative Data Systems Research.11 indexed citations
Agrawal, Divyakant, et al.. (1998). Using Broadcast Primitives in Replicated Databases.
13.
Kumar, Suresh, et al.. (1997). Caprera: An Activity Framework for Transaction Processing on Wide-Area Networks. Very Large Data Bases. 585–589.1 indexed citations
Alonso, Gustavo, Roger Günthör, M. Kamath, et al.. (1995). Exotica/FMDC: Handling Disconnected Clients in a Workflow Management System.. 99–110.35 indexed citations
16.
Agrawal, Divyakant, et al.. (1994). Experiences in the Design of a Kernel for Computational Modeling Systems.. 0.
17.
Agrawal, Divyakant & Amr El Abbadi. (1990). The Tree Quorum Protocol: An Efficient Approach for Managing Replicated Data. Very Large Data Bases. 243–254.85 indexed citations
18.
Agrawal, Divyakant & Amr El Abbadi. (1988). Reducing Storage for Quorum Consensus Algorithms. Very Large Data Bases. 419–430.13 indexed citations
19.
Agrawal, Divyakant, Arthur Bernstein, Pankaj Kumar Gupta, & Soumitra SenGupta. (1986). Distributed Multi-Version Optimistic Concurrency Control for Relational Databases.. 416–421.5 indexed citations
20.
Bhuyan, Laxmi N. & Divyakant Agrawal. (1986). Design and performance of generalized interconnection networks. IEEE Computer Society Press eBooks. 133–142.5 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.