Jeffrey Erman

Computer Networks and Communications · 2006–2014

A researcher in the Science corpus: 4 papers; trusts 12 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 36 citations into it; #7,783 of 9,886 by trust here, #3,753 by citations
• Artificial Intelligence — 24 citations into it; #28,615 of 35,345 by trust here, #19,873 by citations
• Hardware and Architecture — 8 citations into it

Whom Jeffrey Erman trusts overall

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

1. Subhabrata Sen
2. Thomas Karagiannis
3. Andrew William Moore
4. Konstantina (Dina) Papagiannaki
5. Matthew Roughan
6. Oliver Spatscheck
7. J. Ross Quinlan
8. Sebastian Zander
9. Anil K. Jain
10. Patrick Haffner

Who trusts Jeffrey Erman overall

1. Thuy T. T. Nguyen
2. Jun Zhang
3. Muhammad Usama
4. Hyunchul Kim
5. Raouf Boutaba
6. Zubair Muhammad Fadlullah
7. Mayra Zegarra Rodriguez
8. Grenville J. Armitage
9. Giuseppe Ateniese
10. Yang Xiang

Papers from Jeffrey Erman's perspective

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

1. Toward the Accurate Identification of Network Applications (Passive and Active Network Measurement Conference)
2. Accurate, scalable in-network identification of p2p traffic using application signatures (The Web Conference)
3. Flow Clustering Using Machine Learning Techniques (Passive and Active Network Measurement Conference)
4. Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification (ACM/SIGCOMM Internet Measurement Conference)
5. Transport layer identification of P2P traffic (ACM/SIGCOMM Internet Measurement Conference)
6. Profiling internet backbone traffic: behavior models and applications (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
7. Internet traffic classification using bayesian analysis techniques (Measurement and Modeling of Computer Systems)
8. C4.5: Programs for Machine Learning
9. BLINC: multilevel traffic classification in the dark (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
10. Analyzing peer-to-peer traffic across large networks (International Memory Workshop)

Their papers

• Identifying diverse usage behaviors of smartphone apps (2011) — 465 citations
• Understanding the impact of network dynamics on mobile video user engagement (2014) — 147 citations
• Offline/realtime traffic classification using semi-supervised learning (2007) — 312 citations
• Traffic classification using clustering algorithms (2006) — 790 citations