Managing and Mining Graph Data

By Charu C. Aggarwal, Haixun Wang. Managing and Mining Graph Data, 2010

565 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Big data challenge: a data management perspective (2013, background): “Examples are social networks and RDF knowledge bases [7].”
A Degeneracy Framework for Graph Similarity (2018, background): “In almost all graphs, density is an indication of importance [Lee et al. , 2010].”
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning (2021, background): “They include hierarchical clustering [10], [11], modularity optimization [12], [13], [14],”
Similarity Search in Graph Databases: A Multi-Layered Indexing Approach (2017, background): “Today’s highly networked world is facing numerous challenges raised in particular by the abundance of complex and interconnected data, which, without loss of generality, are often modeled and interpreted as graphs [1], [11].”
Data Classification: Algorithms and Applications (2014, background): “Such cases arise commonly in the case of chemical and biological data, and are discussed in detail in [7].”
Clustering attributed graphs: Models, measures and methods (2015, background): “Although several surveys on graph clustering have been written (Schaeffer, 2007; Fortunato, 2010; Aggarwal & Wang, 2010; Coscia et al., 2011), most of the approaches to cluster attributed graphs are more recent and have not been included in these works.”