Overlapping Communities Explain Core–Periphery Organization of Networks
By Jaewon Yang, Jure Leskovec. 2014
164 citations recorded by the source corpus.
Subfields: Ecology, Evolution, Behavior and Systematics, Molecular Biology, Statistical and Nonlinear Physics, Graph Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology, Evolution, Behavior and Systematics
In Molecular Biology
- Jure Leskovec
- Rok Sosic
- Aditya Grover
- William L. Hamilton
- Stephan Günnemann
- Marinka Zitnik
- Aleksandar Bojchevski
- Rex Ying
In Statistical and Nonlinear Physics
- Rok Sosic
- Jure Leskovec
- Aditya Grover
- Jaewon Yang
- William L. Hamilton
- Manuel Gomez-Rodriguez
- Bernhard Schölkopf
- Jon M. Kleinberg
In Graph Learning
- Jure Leskovec
- Rok Sosic
- Aditya Grover
- William L. Hamilton
- Stephan Günnemann
- Aleksandar Bojchevski
- Marinka Zitnik
- Rex Ying
Citations in the corpus
- node2vec: Scalable Feature Learning for Networks (2016, background): “Under the homophily hypothesis [7, 36] nodes that are highly interconnected and belong to similar network clusters or communities should be embedded closely together ( e.g. , nodes s 1 and u in Figure 1 belong to the same network community).”
- SNAP: A General Purpose Network Analysis and Graph Mining Library (2016, methodology): “SNAP provides efficient implementations of commonly used traditional algorithms for graph and network analysis, as well as recent algorithms that employ machine learning techniques on graph problems, such as community detection [Yang and Leskovec 2013; Yang and Leskovec 2014 III.”