Densest subgraph discovery (DSD) is a fundamental problem in graph mining. It has been
studied for decades, and is widely used in various areas, including network science …

Densest subgraph discovery (DSD) is a fundamental topic in graph mining. It has been
extensively studied in the literature and has found many real applications in a wide range of …

Algorithms for finding dense regions in an input graph have proved to be effective tools in
graph mining and data analysis. Recently, Tatti and Gionis [WWW 2015] presented a novel …

As one of the most fundamental problems in graph data mining, the densest subgraph
discovery (DSD) problem has found a broad spectrum of real applications, such as social …

Finding the densest subgraph (DS) from a graph is a fundamental problem in graph
databases. The DS obtained, which reveals closely related entities, has been found to be …

As a fundamental problem in graph data mining, Densest Subgraph Discovery (DSD) aims
to find the subgraph with the highest density from a graph. It has been studied for several …

With the prevalence of graphs for modeling complex relationships among objects, the topic
of graph mining has attracted a great deal of attention from both academic and industrial …

Finding dense subgraphs from a large graph is a fundamental graph mining task with many
applications. The notion is recently formulated of locally densest subgraph (LDS) is recently …

In many real-world applications (eg, email networks, social networks, and phone call
networks), the relationships between entities can be modeled as a temporal graph, in which …

A colorful star motif is a star-shaped graph where any two nodes have different colors.
Counting the colorful star motif can help to analyze the structural properties of real-life …