In complex networks, each node has some unique characteristics that define the importance
of the node based on the given application-specific context. These characteristics can be …

Large graphs involving millions of vertices are common in many practical applications and
are challenging to process. Practical-time implementations using high-end computers are …

This paper presents a scalable high-performance software library to be used for graph
analysis and data mining. Large combinatorial graphs appear in many applications of high …

The increasing importance of graph-data based applications is fueling the need for highly
efficient and parallel implementations of graph analysis software. In this paper we describe …

Connectivity plays a crucial role as agencies at the federal and state level focus on
expanding the public transit system to meet the demands of a multimodal transportation …

Betweenness is a centrality measure based on shortest paths, widely used in complex
network analysis. It is computationally-expensive to exactly determine betweenness; …

Estimating the importance or centrality of the nodes in large networks has recently attracted
increased interest. Betweenness is one of the most important centrality indices, which …

We present a new lock-free parallel algorithm for computing betweenness centrality of
massive complex networks that achieves better spatial locality compared with previous …

We present HyperGraphDB, a novel graph database based on generalized hypergraphs
where hyperedges can contain other hyperedges. This generalization automatically reifies …

The analysis of the relationship among data entities has lead to model them as graphs.
Since the size of the datasets has significantly grown in the recent years, it has become …