Graph Clustering Based on Structural/Attribute Similarities

By Yang Zhou, Hong Cheng, Jeffrey Xu Yu. Proceedings of the VLDB Endowment, 2009

909 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3f397e7dab0e253e0859d18bb5711b5471c657fe

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In Statistical and Nonlinear Physics

Citations in the corpus

A survey of community search over big graphs (2019, methodology): “cal community extraction in directed networks. A recent survey can be found in [140]. 10.1.3 Keyword-Based Attributed Graphs To identify communities from keyword-based attributed graphs, recent works [217,177,160,34,99,165] often use clustering techniques. Zhou et al. [217] computed vertices’ pairwi”
Toward Scalable Systems for Big Data Analytics: A Technology Tutorial (2014, background): “ing sets of nodes with similar content [242].”
Social Network Data Analytics (2011, methodology): “Also notice that a variation of the 80-20 model, the so called ‘bmodel’ [37], generates such self-similar traf c.”
Community Detection in Networks with Node Attributes (2013, methodology): “ty pair and, thus, do not suffer from the assumptions made by soft-membership models. As shown in Table I, heuristic single-assignment clustering methods for networks with node attributes [12], [35], [43] detect hard node-community memberships, however, because each node can belong to exactly one co”
Local Modeling of Attributed Graphs: Algorithms and Applications (2016, methodology): “Recent methods have been proposed for attributed graphs, however, they either use all the given attributes [3, 51, 82, 154] or they perform an unsupervised feature selection [57, 66, 98, 130].”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, methodology): “Clustering methods include attribute based models [19] and methods which directly maximize (resp.”
Effective Community Search for Large Attributed Graphs (2016, background): “In general, CD algorithms aim to retrieve all communities for a graph [22, 11, 34, 21, 20, 29, 23].”
MGAE: Marginalized Graph Autoencoder for Graph Clustering (2017, background): “Zhou et al. add virtual attribute nodes and edges in a network and compute the similarity based on the augmented network [41].”
A Survey of Text Clustering Algorithms (2012, methodology): “A recent method to perform clustering with both structural and attribute similarities is proposed in [113].”
Node Classification in Social Networks (2011, background): “[37] considers partitioning a graph based on both structural and attribute similarity of nodes of the graph.”
A model-based approach to attributed graph clustering (2012, methodology): “Existing works [19, 1, 20, 9, 16] on attributed graph clustering are mainly distance-based, which requires to deliberately design a distance measure that can take both structural and attribute information into consideration.”
Evolution of social-attribute networks: measurements, modeling, and implications using google+ (2012, background): “Heterogeneous networks are shown recently to work better than traditional homogeneous networks for various data mining tasks such as link prediction [17, 58, 48, 49], attribute inference [17, 58] and community detection [51, 50, 62].”
Local search of communities in large graphs (2014, methodology): “Considering the influence of attributes of edges on the semantic of community, SA-Cluster, which utilizes both structure and attribute information, is proposed for graph clustering [22].”
Event Detection in Social Streams (2012, neutral): “On the other hand, some recent methods for pattern discovery in networks use both content and structure [25, 28], though these methods are not deﬁned for the problem of event detection in the temporal scenario .”
Clustering attributed graphs: Models, measures and methods (2015, methodology): “Attributed graph (Zhou et al., 2009; Cruz et al., 2013; Cruz & Bothorel, 2013) (a)”
ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation (2025, unknown): “To mitigate L1 , we detect high-quality ACs by exploiting both links and the attributes of nodes, ensuring that each AC comprises nodes that are not only densely connected but also share similar semantic themes [85].”