N
Niloy Ganguly
Researcher at Indian Institute of Technology Kharagpur
Publications - 366
Citations - 7277
Niloy Ganguly is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Computer science & Social media. The author has an hindex of 35, co-authored 341 publications receiving 5998 citations. Previous affiliations of Niloy Ganguly include Dresden University of Technology & Microsoft.
Papers
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Proceedings ArticleDOI
Understanding and combating link farming in the twitter social network
Saptarshi Ghosh,Bimal Viswanath,Farshad Kooti,Naveen Kumar Sharma,Gautam Korlam,Fabrício Benevenuto,Niloy Ganguly,Krishna P. Gummadi +7 more
TL;DR: It is shown that a simple user ranking scheme that penalizes users for connecting to spammers can effectively address the link farming problem in Twitter by disincentivizing users from linking with other users simply to gain influence.
Proceedings Article
Design Patterns from Biology for Distributed Computing
Ozalp Babaoglu,Geoffrey Canright,Andreas Deutsch,G. A. Di Caro,Frederick Ducatelle,Luca Maria Gambardella,Niloy Ganguly,Márk Jelasity,Roberto Montemanni,Alberto Montresor,Tore Urnes +10 more
TL;DR: In this article, a conceptual framework that captures several basic biological processes in the form of a family of design patterns is proposed, such as plain diffusion, replication, chemotaxis, and stigmergy.
Journal ArticleDOI
Design patterns from biology for distributed computing
Ozalp Babaoglu,Geoffrey Canright,Andreas Deutsch,Gianni A. Di Caro,Frederick Ducatelle,Luca Maria Gambardella,Niloy Ganguly,Márk Jelasity,Roberto Montemanni,Alberto Montresor,Tore Urnes +10 more
TL;DR: This article proposes a conceptual framework that captures several basic biological processes in the form of a family of design patterns that inherit desirable properties of biological systems including adaptivity and robustness and shows how to implement important functions for distributed computing based on these patterns.
Proceedings ArticleDOI
Feature weighting in content based recommendation system using social network analysis
TL;DR: A hybridization of collaborative filtering and content based recommendation system where attributes used for content based recommendations are assigned weights depending on their importance to users.
Proceedings ArticleDOI
Stop clickbait: detecting and preventing clickbaits in online news media
TL;DR: Wang et al. as mentioned in this paper proposed clickbait detection and personalized blocking approaches to detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines.