In many modern applications, the generated data is a dynamic network. These networks are
graphs that change over time by a sequence of update operations (node addition, node …

Connected components is a fundamental kernel in graph applications. The fastest existing
parallel multicore algorithms for connectivity are based on some form of edge sampling …

\em Structural Clustering (\strclu) is one of the most popular graph clustering paradigms. In
this paper, we consider \strclu under Jaccard similarity on a dynamic graph, G=(V, E) …

The continuous and rapid growth of highly interconnected datasets, which are both
voluminous and complex, calls for the development of adequate processing and analytical …

The arboricity a (G) of a graph G is defined as the minimum number of edge-disjoint forests
that the edge set of G can be partitioned into. It is a fundamental metric and has been widely …

Graph clustering is a fundamental data mining task that clusters vertices into different
groups. The structural graph clustering algorithm (SCAN) is a widely used graph clustering …

Finding the connected components of a graph is a fundamental problem with uses
throughout computer science and engineering. The task of computing connected …

Structural graph clustering (SCAN) is a classic graph clustering algorithm. In SCAN, a key
step is to compute the structural similarity between vertices according to the overlap ratio of …

The network size is rapidly growing and providing many opportunities to examine
networking data (graph data). The structural clustering algorithm (SCAN) builds the cluster …

Finding the connected components of a graph is a fundamental problem with uses
throughout computer science and engineering. The task of computing connected …