Efficient aggregation for graph summarization

By Yuanyuan Tian, Richard A. Hankins, Jignesh M. Patel. SIGMOD Conference, 2008

502 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6e9cf091dd709b557b32e7239647753680f0645b

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In Computer Vision and Pattern Recognition

In Statistical and Nonlinear Physics

In Signal Processing

Citations in the corpus

Efficient community detection in large networks using content and links (2012, methodology): “Previously SA-Cluster-Inc has been shown to outperform k-SNAP [25] and PCL-DC to outperform methods including PLSA-PHITS [5], LDA-Link-Word [7] and Link-Content-Factorization [31].”
Ranking-based clustering of heterogeneous information networks with star network schema (2009, background): “Most current studies [20, 17, 19, 21] on information network are on homogeneous networks, i.”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, background): “Over the years, many researchers have used aggregation based methods [38], [39], [40] to compress graphs.”
Local Modeling of Attributed Graphs: Algorithms and Applications (2016, methodology): “The representative methods [3, 51, 60, 82, 137, 154] aim to partition the given graph into structurally dense and attribute-wise homogeneous clusters, detect deviations from frequent subgraphs [110], or search for community outliers in attributed graphs [51].”
Graph Clustering Based on Structural/Attribute Similarities (2009, methodology): “Many existing graph clustering/partitioning methods focused only on either topological structures [16,19,26] or vertex attributes [22].”
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.”
From "Think Like a Vertex" to "Think Like a Graph" (2013, background): “Graph mutation is a crucial requirement for many graph algorithms, especially for graph aggregation algorithms, such as graph coarsening [12, 11], graph sparsification [19], and graph summarization [22].”