C
Chayant Tantipathananandh
Researcher at University of Illinois at Chicago
Publications - 10
Citations - 1002
Chayant Tantipathananandh is an academic researcher from University of Illinois at Chicago. The author has contributed to research in topics: Dynamic network analysis & Social network. The author has an hindex of 8, co-authored 10 publications receiving 939 citations. Previous affiliations of Chayant Tantipathananandh include Google.
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Proceedings ArticleDOI
A framework for community identification in dynamic social networks
TL;DR: It is proved that finding the most explanatory community structure is NP-hard and APX-hard, and it is demonstrated empirically that the heuristics trace developments of community structure accurately for several synthetic and real-world examples.
Proceedings ArticleDOI
Biometric animal databases from field photographs: identification of individual zebra in the wild
Mayank Lahiri,Chayant Tantipathananandh,Rosemary Warungu,Daniel I. Rubenstein,Tanya Y. Berger-Wolf +4 more
TL;DR: A database of noisy photographs taken in the wild is used to build a biometric database of individual animals differentiated by their coat markings, which can be queried by its coat markings against the database to determine if the animal has been observed and identified before.
Journal ArticleDOI
Visualizing the evolution of community structures in dynamic social networks
TL;DR: This paper presents an interactive visualization methodology for dynamic social networks that focuses on revealing the community structure implied by the evolving interaction patterns between individuals, and applies this technique to analyze thecommunity structure in the US House of Representatives.
Proceedings ArticleDOI
Finding Communities in Dynamic Social Networks
TL;DR: The social cost model is extended and an optimization problem of finding community structure from the sequence of arbitrary graphs is formulated and a semi definite programming formulation and a heuristic rounding scheme is proposed.
Proceedings ArticleDOI
Constant-factor approximation algorithms for identifying dynamic communities
TL;DR: This paper designs and analyzes a approximation algorithm for inferring communities in dynamic networks with a provable approximation guarantee, and demonstrates the efficiency and effectiveness of the algorithm on real data sets.