Graph Clustering

By Charu C. Aggarwal. 2017

1,065 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Graph Learning

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In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

In Graph Learning

Citations in the corpus

Prioritizing network communities (2018, methodology): “Comparing the performance of CR ANK to alternative ranking techniques, such as modularity 5 and conductance 21 (Supplementary Note 7), we observe that CR ANK performs 149 and 37% better than modularity and conductance, respectively, in terms of the Spearman ’ s rank correlation between the…”
Graph of Thoughts: Solving Elaborate Problems with Large Language Models (2023, background): “They have numerous applications (particularly set intersection) in problems ranging from genome or document comparisons to pattern matching [20, 57, 38, 1, 27, 49, 10, 9].”
Handbook of Big Data (2016, background): “Conductance is probably the combinatorial quantity that most-closely captures the intuition underlying this bicriteria [33,55,57].”
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations (2019, methodology): “By employing such local clustering (Schaeffer 2007), we can maintain sparsity of the cluster assignment matrix S similar to the original graph adjacency matrix A i.”
Modularity of Mind (2017, methodology): “There are a number of methods of testing this kind of modularity (Boccaletti et al. 2006; Fortunato 2010; Reijneveld et al. 2007; Schaeffer 2007).”
Distance geometry and data science (2019, methodology): “A recursive clustering by two-way partitioning is a general methodology which is part of a family of hierarchical clustering methods [163].”
Attributed Graph Clustering via Adaptive Graph Convolution (2019, background): “In contrast, many graph-based clustering methods [Schaeffer, 2007] only leverage graph con-∗ nectivity patterns, e.g., user friendships in social networks, paper citation links in citation networks, and genetic interactions in protein-protein interaction networks.”
Community detection in networks: A user guide (2016, background): “More info on network clustering can be found in several review articles (Chakraborty et al., 2016; Coscia et al., 2011; Fortunato, 2010; Malliaros and Vazirgiannis, 2013; Newman, 2012; Parthasarathy et al., 2011; Porter et al., 2009; Schaeffer, 2007; Xie et al., 2013).”
A review on algorithms for maximum clique problems (2015, neutral): “In addition to these applications, the MCP is tightly related to some important combinatorial optimization problems such as clique partitioning (Wang, Alidaee, Glover, & Kochenberger, 2006), graph clustering (Schaeffer, 2007), graph vertex coloring (Chams, Hertz, & Werra, 1987; Wu & Hao, 2012a), max”
A roadmap for the computation of persistent homology (2015, methodology): “The use of clustering techniques and other ideas from areas such as computer science, machine learning, and uncertainty quantiﬁcation — along with mathematical and statistical models — are often very useful for data analysis (see, e.g., [57, 61, 75, 108] and many other references).”
Design Structure Matrix Extensions and Innovations: A Survey and New Opportunities (2016, methodology): “Determining modules via clustering analysis [46], [47], [50], [56], [311], [318]–[327]”