A Spectral Clustering Approach To Finding Communities in Graph

By Scott White, Padhraic Smyth. SDM, 2005

707 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/9a92300b0ecc33f8e55a8ac945a51aaade549013

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In Artificial Intelligence

In Statistical and Nonlinear Physics

Citations in the corpus

Finding community structure in networks using the eigenvectors of matrices. (2006, methodology): “For instance, White and Smyth [ 55 ] have applied the standard technique of k-means clustering based on group centroids to a different but related optimization problem and have found good results.”
On constrained spectral clustering and its applications (2012, positive): “The advantage of spectral clustering has also been validated by many real-world applications, such as image segmentation (Shi and Malik (2000)) and mining social networks (White and Smyth (2005)).”
Automated Tag Clustering: Improving search and exploration in the tag space (2006, methodology): “The spectral clustering algorithm described by Scott White [5] helped splitting up into handier clusters.”
Scalable Learning of Collective Behavior (2012, background): “…network G ¼ ðV ;E; Y Þ where V is the vertex set, E is the edge set, and Yi Y are the class labels of a vertex vi 2 V , and known values of Yi for some subsets of vertices V L, how can we infer the values of Yi (or an estimated probability over each label) for the remaining vertices V U ¼ V V L?”
Empirical comparison of algorithms for network community detection (2010, background): “Modularity has been very influential in recent community detection literature, and one can use spectral techniques to approximate it [34, 28].”
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks (2008, background): “Extensive experimental results [13, 23] have demonstrated that Q is an effective measure for the community quality, where a maximal Q is a good indicator of the best community structure and therefore the best community number m.”
Learning Deep Representations for Graph Clustering (2014, background): “On one hand, many real-world problems can be cast as graph clustering such as image segmentation (Shi and Malik 2000), community detection (Smyth and White 2005), and VLSI design (Chan, Schlag, and Zien 1994); on the other hand, it is easy to transform a clustering problem in the vector space to a c”
Data Mining for Internet of Things: A Survey (2014, background): “Because it is expected that the IoT will create a considerable sum of profits to many domains, from the hightechnology researches to our daily life, several nationwise research organizations [140], [141] have focused on it.”
Unsupervised Pattern Discovery in Speech (2008, background): “In contrast to simple edge thresholding, a number of more sophisticated algorithms for automatic graph clustering have been proposed by researchers in other fields [32], [33].”
Analyzing communities and their evolutions in dynamic social networks (2009, background): “In addition, in Figure(5b) we also show the modularity values Q that are reported in [White and Smyth 2005].”
Community detection in graphs (2009, methodology): “White and Smyth (White and Smyth, 2003) and Zhou (Zhou, 2003a) used instead the average first passage time, i. e. the average number of steps needed to reach for the first time the target vertex from the source.”
Detecting communities and their evolutions in dynamic social networks—a Bayesian approach (2011, methodology): “…graph-based clustering algorithms (Chung 1997), such as the normalized cut algorithm proposed by Shi and Malik (2000), the modularity-based approaches proposed by White and Smyth (2005) and by Chen et al. (2009), and the graph-factorization clustering (GFC) algorithm proposed by Yu et al. (2005).”
Local Modeling of Attributed Graphs: Algorithms and Applications (2016, background): “For attributed graphs, we see major challenges that remain unsolved by traditional graph mining techniques [11, 38, 68, 109, 146], which consider plain graphs (without attributes).”
Handbook of Cluster Analysis (2015, background): “A typical example of similarity based clustering is community detection in social networks [47] (see also Chapter ??), where the observations are individual links between people, which may be due to friendship, shared interests, work relationships.”
Statistical properties of community structure in large social and information networks (2008, background): “Modularity has been very influential in the recent community detection literature [125, 52], and one can use spectral techniques to approximate it [154, 128].”