Subhabrata Sen

Computer Networks and Communications · 2002–2013

A researcher in the Science corpus: 9 papers; trusts 15 researchers and is trusted by 25 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 55 citations into it; #983 of 9,886 by trust here, #816 by citations
• Electrical and Electronic Engineering — 34 citations into it; #15,978 of 16,539 by trust here, #6,558 by citations
• Artificial Intelligence — 16 citations into it; #12,760 of 35,345 by trust here, #19,018 by citations

Whom Subhabrata Sen trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Lada A. Adamic
2. Eytan Adar
3. Ben Y. Zhao
4. Matei Ripeanu
5. Robert E. Schapire
6. S. Muthu Muthukrishnan
7. T. J. Watson
8. Bernardo A. Huberman
9. John D. Kubiatowicz
10. Adriana Iamnitchi

Who trusts Subhabrata Sen overall

1. Jeffrey Erman
2. Thomas Karagiannis
3. Thuy T. T. Nguyen
4. Martin Hilbert
5. Sebastian Zander
6. Hyunchul Kim
7. Martin F. Arlitt
8. Grenville J. Armitage
9. Anukool Lakhina
10. Konstantina (Dina) Papagiannaki

Papers from Subhabrata Sen's perspective

Ordered by how strongly the researchers Subhabrata Sen trusts endorse each paper. Same corpus, a different order for every researcher.

1. Chord: A scalable peer-to-peer lookup service for internet applications (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
2. A scalable content-addressable network (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
3. A Maximum Entropy Approach to Natural Language Processing
4. An Infrastructure for Fault-tolerant Wide-area Location and Routing
5. New directions in traffic measurement and accounting (International Memory Workshop)
6. On the characteristics and origins of internet flow rates (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
7. Logistic Regression, AdaBoost and Bregman Distances (Machine-mediated learning)
8. Energy consumption in mobile phones: a measurement study and implications for network applications (ACM/SIGCOMM Internet Measurement Conference)
9. The Boosting Approach to Machine Learning An Overview
10. Anatomizing application performance differences on smartphones (ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services)

Their papers

• A close examination of performance and power characteristics of 4G LTE networks (2012) — 1,062 citations
• Profiling resource usage for mobile applications: a cross-layer approach (2011) — 299 citations
• Characterizing radio resource allocation for 3G networks (2010) — 297 citations
• An in-depth study of LTE: effect of network protocol and application behavior on performance (2013) — 414 citations
• ACAS: automated construction of application signatures (2005) — 431 citations
• Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification (2004) — 584 citations
• Accurate, scalable in-network identification of p2p traffic using application signatures (2004) — 918 citations
• Sketch-based change detection: methods, evaluation, and applications (2003) — 663 citations
• Analyzing peer-to-peer traffic across large networks (2002) — 959 citations