A new open-access study reports that fusing attribute features with graph structure and training classifiers collaboratively ...

In this chapter we will introduce a new method of clustering where the data to be clustered will be represented by graphs instead of vectors or other models. Specifically, we will extend the classical ...

Researchers report distributed-memory implementations of the WCC and CM algorithms that post-process network clusterings for ...

Motif-based graph local clustering is a popular method for graph mining tasks due to its various applications, such as community detection, network optimization and graph learning. However, the ...

Multivariate analysis in statistics is a set of useful methods for analyzing data when there are more than one variables under consideration. Multivariate analysis techniques may be used for several ...

Spectral clustering is quite complex, but it can reveal patterns in data that aren't revealed by other clustering techniques. Data clustering is the process of grouping data items so that similar ...

Part I of our series on graph analytics introduced us to graph analytics, and its brethren graph databases. We talked about the use of graph analytics to understand and visualize relationships between ...

A graph database, also referred to as a semantic database, is a software application designed to store, query and modify network graphs. A network graph is a visual construct that consists of nodes ...