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.
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior
2018343 citationsConstantinos Djouvas, Despoina Chatzakou et al.Proceedings of the International AAAI Conference on Web and Social Mediaprofile →
Countries citing papers authored by Michael Sirivianos
Since
Specialization
Citations
This map shows the geographic impact of Michael Sirivianos'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 Michael Sirivianos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Sirivianos more than expected).
Fields of papers citing papers by Michael Sirivianos
This network shows the impact of papers produced by Michael Sirivianos. 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 Michael Sirivianos. The network helps show where Michael Sirivianos may publish in the future.
Co-authorship network of co-authors of Michael Sirivianos
This figure shows the co-authorship network connecting the top 25 collaborators of Michael Sirivianos.
A scholar is included among the top collaborators of Michael Sirivianos 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 Michael Sirivianos. Michael Sirivianos is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Zannettou, Savvas, Jeremy Blackburn, Emiliano De Cristofaro, Michael Sirivianos, & Gianluca Stringhini. (2018). Understanding Web Archiving Services and Their (Mis)Use on Social Media. Proceedings of the International AAAI Conference on Web and Social Media. 12(1).7 indexed citations
8.
Djouvas, Constantinos, Despoina Chatzakou, Ilias Leontiadis, et al.. (2018). Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior. Proceedings of the International AAAI Conference on Web and Social Media. 12(1).343 indexed citations breakdown →
9.
Soriente, Claudio, et al.. (2017). Who is Fiddling with Prices?. Ktisis at Cyprus University of Technology (Cyprus University of Technology). 376–389.10 indexed citations
Sirivianos, Michael, Pan Hui, Nishanth Sastry, Yang Chen, & Hongqiang Harry Liu. (2015). Proceedings of the 7th International Workshop on Hot Topics in Planet-scale mObile computing and online Social neTworking.1 indexed citations
13.
Cao, Qiang, Michael Sirivianos, Xiaowei Yang, & Kamesh Munagala. (2015). Combating Friend Spam Using Social Rejections. Ktisis at Cyprus University of Technology (Cyprus University of Technology). 235–244.16 indexed citations
Sirivianos, Michael. (2009). FaceTrust: Assessing the Credibility of Online Personas via Social Networks.. IACR Cryptology ePrint Archive. 2009. 152–2.15 indexed citations
17.
Gjoka, Minas, Michael Sirivianos, Athina Markopoulou, & Xiaowei Yang. (2008). Poking facebook. Ktisis at Cyprus University of Technology (Cyprus University of Technology). 31–36.66 indexed citations
18.
Sirivianos, Michael, et al.. (2007). Free-riding in BitTorrent Networks with the Large View Exploit..109 indexed citations
19.
Sirivianos, Michael, et al.. (2007). Dandelion: cooperative content distribution with robust incentives. USENIX Annual Technical Conference. 12.73 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.