Konstantina (Dina) Papagiannaki

Computer Networks and Communications · 2004–2010

A researcher in the Science corpus: 6 papers; trusts 19 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

Whom Konstantina (Dina) Papagiannaki trusts overall

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

  1. Subhabrata Sen
  2. Andrew William Moore
  3. Balachander Krishnamurthy
  4. Thomas Karagiannis
  5. Dongyu Qiu
  6. Matthew Roughan
  7. Cristian Estan
  8. Matei Ripeanu
  9. Oliver Spatscheck
  10. Paul R. Barford

Who trusts Konstantina (Dina) Papagiannaki overall

  1. Jeffrey Erman
  2. Thuy T. T. Nguyen
  3. Manuel Crotti
  4. Hyunchul Kim
  5. Anukool Lakhina
  6. Andrew William Moore
  7. Grenville J. Armitage
  8. Tom Auld
  9. Martin F. Arlitt
  10. Anirban Mahanti

Papers from Konstantina (Dina) Papagiannaki's perspective

Ordered by how strongly the researchers Konstantina (Dina) Papagiannaki 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. Free Riding on Gnutella (First Monday)
  3. Packet-level traffic measurements from the Sprint IP backbone (IEEE Network)
  4. MapReduce: simplified data processing on large clusters
  5. Accurate, scalable in-network identification of p2p traffic using application signatures (The Web Conference)
  6. VL2: a scalable and flexible data center network (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
  7. A scalable, commodity data center network architecture (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
  8. Traffic matrix estimation: existing techniques and new directions (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
  9. Transport layer identification of P2P traffic (ACM/SIGCOMM Internet Measurement Conference)
  10. Flow Clustering Using Machine Learning Techniques (Passive and Active Network Measurement Conference)

Their papers