Distributed Network Flows
Distributed Network Flows is a computational framework within network flow theory that enables the decentralized optimization of flow allocation across a network by coordinating localized decision-making at individual nodes or edges through iterative message-passing algorithms.
8
Publications
823
Citations
23
Authors
10
Institutions
Publications per year
2017–2026
3
Parents
8
Connectivity and inference problems for temporal networks
David Kempe, Jon Kleinberg, Amit Kumar · 2000 · 320 citations
1–5 of 8
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10

Cornell University
Ithaca, United States
1 h-index
3 publications
960 citations

Australian National University
Canberra, Australia
1 h-index
2 publications
40 citations
Legal Aid New South Wales
Sydney, Australia
1 h-index
1 publications
34 citations
Tsinghua–Berkeley Shenzhen Institute
Shenzhen, China
1 h-index
2 publications
68 citations

The University of Melbourne
Melbourne, Australia
1 h-index
3 publications
237 citations

Hohai University
Nanjing, China
1 h-index
2 publications
64 citations

Data61
Sydney, Australia
1 h-index
1 publications
20 citations

University Town of Shenzhen
Shenzhen, China
1 h-index
1 publications
34 citations
1–8 of 10
2 publications · 66 citations
2
h-index
1 publications · 277 citations
1
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1 publications · 34 citations
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1–3