Graph summarization with bounded error

By Saket Navlakha, Rajeev Rastogi, Nisheeth Shrivastava. SIGMOD Conference, 2008

379 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c948d5342f4ffe8163fc91893a100c9617a2a305

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

1. Palash Goyal
2. Emilio Ferrara
3. Nhathai Phan
4. Dejing Dou
5. Min Wu
6. Hong Cheng
7. Jeffrey Xu Yu
8. Abdolreza Mirzaei

In Computer Vision and Pattern Recognition

1. Nhathai Phan
2. Min Wu
3. Hong Cheng
4. Jeffrey Xu Yu
5. Abdolreza Mirzaei
6. Chee Keong Kwoh
7. Liu Ren
8. Dejing Dou

Citations in the corpus

• The power of protein interaction networks for associating genes with diseases (2010, neutral): “…compare approaches based on direct network neighbors (Oti et al., 2006, and neighborhood), unsupervised graph partitioning [graph summarization (GS; Navlakha et al., 2008) and Markov clustering (MCL; Van Dongen, 2008)], semi-supervised graph partitioning (VI-Cut; Navlakha et al., 2009a), random…”
• Analysis of Physical Activity Propagation in a Health Social Network (2014, background): “Tian et al. [26] and Zhang et al. [29] consider algorithms to build graph summaries based on node attributes, while Navlakha et al. [13] use Minimum Description Length principle (MDL) [19] to find good structural summaries of graphs.”
• Learning the Structural Vocabulary of a Network (2017, neutral): “Another option could be to use graph summarization methods to present a compressed version of the network to the input layer (Boldi & Vigna, 2004; Navlakha et al., 2008).”
• Graph Embedding Techniques, Applications, and Performance: A Survey (2017, methodology): “[41] used Minimum Description Length (MDL) [42] from information theory to summarize a graph into a graph summary and edge correction.”
• Graph Clustering Based on Structural/Attribute Similarities (2009, methodology): “Navlakha et al. [15] proposed a graph summarization method using the MDL principle.”
• Sequence Synopsis: Optimize Visual Summary of Temporal Event Data (2018, positive): “[30] has applied MDL for graph summarization.”
• Hierarchical graph embedding in vector space by graph pyramid (2017, background): “[27] enhanced this summary representation with a set of edge-corrections necessary to regenerate the original graph.”
• Computational approaches for detecting protein complexes from protein interaction networks: a survey (2010, methodology): “(GS) technique [48] to cluster a PPI graph into functional modules.”