Efficient semi-streaming algorithms for local triangle counting in massive graphs
By Luca Becchetti, Paolo Boldi, Carlos Castillo, Aristides Gionis. Knowledge Discovery and Data Mining, 2008
338 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/50b85ba8ddebee55fcf6e192109afde820298af3
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
- Charalampos E. Tsourakakis
- Christos Faloutsos
- Tamara G. Kolda
- Seshadhri Comandur
- U. Kang
- Ali Pinar
- Sergei Vassilvitskii
- Siddharth Suri
In Computer Vision and Pattern Recognition
- Charalampos E. Tsourakakis
- U. Kang
- Christos Faloutsos
- Sergei Vassilvitskii
- Siddharth Suri
- Seshadhri Comandur
- Ali Pinar
- Gary L. Miller
In Statistical and Nonlinear Physics
- Charalampos E. Tsourakakis
- Christos Faloutsos
- U. Kang
- Seshadhri Comandur
- Tamara G. Kolda
- Ali Pinar
- Gary L. Miller
- Sergei Vassilvitskii
Citations in the corpus
- Community Detection and Mining in Social Media (2010, neutral): “Others explore I/O efficient computation (Becchetti et al., 2008; Desikan and Srivastava, 2008).”
- Wedge sampling for computing clustering coefficients and triangle counts on large graphs † (2013, methodology): “Triangles have also been used in graph mining applications such as spam detection and finding common topics on the WWW [6,7].”
- DOULION: counting triangles in massive graphs with a coin (2009, methodology): “presented in [6] a semi-streaming algorithm for computing triangles in a graph.”
- Counting triangles and the curse of the last reducer (2011, background): “) These speedup improvements are comparable to those obtained by [17] and [2], but on a much harder problem.”