Graph Clustering and Minimum Cut Trees
By Gary William Flake, Robert Endre Tarjan, Kostas Tsioutsiouliklis. Internet Mathematics, 2004
390 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Signal Processing
View Paper: https://www.semanticscholar.org/paper/747057927b86d766f1d88a88f6a2e1179ca42904
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Michael W. Mahoney
- Jure Leskovec
- Anirban Dasgupta
- Jiajia Liu
- Kevin J. Lang
- Jianfeng Ma
- Nei Kato
- David P. Woodruff
In Statistical and Nonlinear Physics
- Jure Leskovec
- Michael W. Mahoney
- Kevin J. Lang
- Anirban Dasgupta
- Nei Kato
- Jaewon Yang
- Manuel Gomez-Rodriguez
- Rok Sosic
In Signal Processing
- Michael W. Mahoney
- Anirban Dasgupta
- Nei Kato
- Jure Leskovec
- Kevin J. Lang
- David P. Woodruff
- Charalampos E. Tsourakakis
- Francis R. Bach
Citations in the corpus
- Internet of Underwater Things and Big Marine Data Analytics—A Comprehensive Survey (2020, neutral): “[214] as well as Wang et al.”
- A survey of frequent subgraph mining algorithms (2012, neutral): “, 2005) Graph clustering (Flake et al., 2004; Newman, 2004; Huang & Lai, 2006) Graph indexing (Shasha et al.”
- Device-to-Device Communication in LTE-Advanced Networks: A Survey (2015, methodology): “The network connectivity is calculated with the minimum cut tree method [75], in which the network is partitioned into two parts by removing the links of minimum cut costs.”
- Statistical properties of community structure in large social and information networks (2008, background): “Flake, Tarjan, and Tsioutsiouliklis [68] introduce a similar bicriterion that is based on network flow ideas, and Flake et al.”
- Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, background): “Flake, Tarjan, and Tsioutsiouliklis [69] introduce a similar bicriterion that is based on network flow ideas, and Flake et al. [67, 68] defined a community as a set of nodes that has more intra-edges than inter-edges.”