Although a graphics processing unit (GPU) is a specialized device tailored primarily for compute-intensive, highly dataparallel computations; significant acceleration can be achieved on memory-intensive graph algorithms as well. In this work, we investigate the performance of a graph algorithm for computing vertex betweenness centrality for small world networks on 2 NVIDIA Tesla and Fermi GPUs ...

This study uses bibliographic coupling to identify missing relevant patent links, in order to construct a comprehensive citation network. Missing citation links can be added by taking the missing relevant patent links into account. The Pareto principle is used to determine the threshold of bibliographic coupling strength, in order to identify the missing relevant patent links. Comparisons betwe...

Graph Isomorphism is one of the classical problems of graph theory for which no deterministic polynomial-time algorithm is currently known, but has been neither proven to be NP-complete. Several heuristic algorithms have been proposed to determine whether or not two graphs are isomorphic (i.e., structurally the same). In this research, we propose to use the sequence (either the non-decreasing o...

We demonstrate that any walk on a directed graph G can be decomposed into an underlying simple path and a nested collection of bare cycles, where simple paths and bare cycles are open and closed walks that are forbidden from visiting any vertex more than once. We define a convention for the nesting structure of the bare cycles that makes this path decomposition unique. In contrast to existing d...

abstractintroduction: cancer is caused by genetic abnormalities, such as mutation of ontogenesis or tumor suppressor genes which alter downstream signaling pathways and protein-protein interactions. comparison of protein interactions in cancerous and normal cells can be of help in mechanisms of disease diagnoses and treatments. methods: we constructed protein interaction networks of cancerous a...

Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step GCNs is the information-passing framework that considers all information from neighbors to central vertex be equally important. Such equal importance, however, inadequate for scale-free networks, where hub vertices propagate more dominant due imba...

In many applications we are required to locate the most prominent group of vertices in a complex network. Group Betweenness Centrality can be used to evaluate the prominence of a group of vertices. Evaluating the Betweenness of every possible group in order to find the most prominent is not computationally feasible for large networks. In this paper we present two algorithms for finding the most...

We introduce an informative labeling algorithm for the vertices of a family of Koch networks. Each of the labels is consisted of two parts, the precise position and the time adding to Koch networks. The shortest path routing between any two vertices is determined only on the basis of their labels, and the routing is calculated only by few computations. The rigorous solutions of betweenness cent...

Graphs (networks) are an important tool to model data in different domains. Realworld graphs usually directed, where the edges have a direction and they not symmetric. Betweenness centrality is index widely used analyze networks. In this paper, first given directed network G vertex r ∈ V (G), we propose exact algorithm compute betweenness score of r. Our pre-computes set ℛ

Betweenness centrality is a graph analytic that states the importance of a vertex based on the number of shortest paths that it is on. As such, betweenness centrality is a building block for graph analysis tools and is used by many applications, including finding bottlenecks in communication networks and community detection. Computing betweenness centrality is computationally demanding, O(V2 + ...