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The ability to construct domain specific knowledge graphs (KG) and perform question-answering or hypothesis generation is a transformative capability. Despite their value, automated construction of knowledge graphs remains an expensive technical challenge that is beyond the reach for most enterprises and academic institutions. We propose an end-toend framework for developing custom knowledge graph...
Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the Fiedler vector of centered graph adjacency matrix (graph Laplacian), we propose spectral decomposition approaches to graph PCA...
Data-intensive science simultaneously derives from and creates the need for large quantities of data. As such, scientists increasingly need to discover and analyze new datasets from diverse sources. Beyond the sheer volume of data, issues posed by the resultant data heterogeneity are often overlooked. We postulate that heterogeneity challenges can be solved (at least in part) with the adoption of...
Community detection has become a fundamental operation in numerous graph-theoretic applications. It is used to reveal natural divisions that exist within real world networks without imposing prior size or cardinality constraints on the set of communities. Despite its potential for application, there is only limited support for community detection on large-scale parallel computers, largely owing to...
This article presents SGEM, a full software system for accelerating large-scale graph databases on commodity clusters. Unlike current approaches, GEMS addresses graph databases by primarily employing graph-based methods, which is reflected at all levels of the stack. On the one hand, this allows exploiting the space efficiency of graph data structures and the inherent parallelism of some graph algorithms...
We propose a multiscale approach to modeling cyber networks, with the goal of capturing a view of the network and overall situational awareness with respect to a few key properties — connectivity, distance, and centrality — for a system under an active attack. We focus on theoretical and algorithmic foundations of multiscale graphs, coming from an algorithmic perspective, with the goal of modeling...
Network-of-networks (NoN) is a graph-theoretic model of interdependent networks that have distinct dynamics at each network (layer). By adding special edges to represent relationships between nodes in different layers, NoN provides a unified mechanism to study interdependent systems intertwined in a complex relationship. While NoN based models have been proposed for cyber-physical systems, in this...
Triadic analysis encompasses a useful set of graph mining methods that are centered on the concept of a triad, which is a sub graph of three nodes. Such methods are often applied in the social sciences as well as many other diverse fields. Triadic methods commonly operate on a triad census that counts the number of triads of every possible edge configuration in a graph. Like other graph algorithms,...
Long viewed as a strong statistical inference technique, Bayesian networks have emerged as an important class of applications for high-performance computing. We have applied an architecture-conscious approach to parallelizing the Lauritzen-Spiegelhalter Junction Tree algorithm for exact inferencing of Bayesian networks. In optimizing the Junction Tree algorithm, we have implemented both in-clique...
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