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Benchmarking Parallel Eigen Decomposition for Residuals Analysis of Very Large Graphs
 Proceedings of the IEEE High Performance Extreme Computing Conference, 2012, available online at ieeehpec.org/2012/index_htm_files/Rutledge.pdf
"... Abstract—Graph analysis is used in many domains, from the social sciences to physics and engineering. The computational driver for one important class of graph analysis algorithms is the computation of leading eigenvectors of matrix representations of a graph. This paper explores the computational i ..."
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Abstract—Graph analysis is used in many domains, from the social sciences to physics and engineering. The computational driver for one important class of graph analysis algorithms is the computation of leading eigenvectors of matrix representations of a graph. This paper explores the computational implications of performing an eigen decomposition of a directed graph’s symmetrized modularity matrix using commodity cluster hardware and freely available eigensolver software, for graphs with 1 million to 1 billion vertices, and 8 million to 8 billion edges. Working with graphs of these sizes, parallel eigensolvers are of particular interest. Our results suggest that graph analysis approaches based on eigen space analysis of graph residuals are feasible even for graphs of these sizes. I.
Anomaly detection in dynamic networks: a survey
 Wiley Interdisciplinary Reviews: Computational Statistics
, 2015
"... Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressivene ..."
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Anomaly detection is an important problem with multiple applications, and thus has been studied for decades in various research domains. In the past decade there has been a growing interest in anomaly detection in data represented as networks, or graphs, largely because of their robust expressiveness and their natural ability to represent complex relationships. Originally, techniques focused on anomaly detection in static graphs, which do not change and are capable of representing only a single snapshot of data. As realworld networks are constantly changing, there has been a shift in focus to dynamic graphs, which evolve over time. In this survey, we aim to provide a comprehensive overview of anomaly detection in dynamic networks, concentrating on the stateoftheart methods. We first describe four types of anomalies that arise in dynamic networks, providing an intuitive explanation, applications, and a concrete example for each. Having established an idea for what constitutes an anomaly, a general twostage approach to anomaly detection in dynamic networks that is common among the methods is presented. We then construct a twotiered taxonomy, first partitioning the methods based on the intuition behind their approach, and subsequently subdividing them based on the types of anomalies they detect. Within each of the tier one categoriescommunity, compression, decomposition, distance, and probabilistic model basedwe highlight the major similarities and differences, showing the wealth of techniques derived from similar conceptual approaches. © 2015 The Authors. financial systems connecting banks across the world, electric power grids connecting geographically distributed areas, and social networks that connect users, businesses, or customers using relationships such as friendship, collaboration, or transactional interactions. These are examples of dynamic networks, which, unlike static networks, are constantly undergoing changes to their structure or attributes. Possible changes include insertion and deletion of vertices (objects), insertion and deletion of edges (relationships), and modification of attributes (e.g., vertex or edge labels). WIREs Computational Statistics An important problem over dynamic networks is anomaly detectionfinding objects, relationships, or
TAMING BIOLOGICAL BIG DATA WITH D4M Taming Biological Big Data with D4M
"... The supercomputing community has taken up the challenge of “taming the beast ” spawned by the massive amount of data available in the bioinformatics domain: How can these data be exploited faster and better? MIT Lincoln Laboratory computer scientists demonstrated how a new Laboratorydeveloped techn ..."
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The supercomputing community has taken up the challenge of “taming the beast ” spawned by the massive amount of data available in the bioinformatics domain: How can these data be exploited faster and better? MIT Lincoln Laboratory computer scientists demonstrated how a new Laboratorydeveloped technology, the Dynamic Distributed Dimensional Data Model (D4M), can be used to accelerate DNA sequence comparison, a core operation in bioinformatics. The growth of large, unstructured datasets is driving the development of new technologies for finding items of interest in these data. Because of the tremendous expansion of data from DNA sequencing, bioinformatics has become an active area of research in the supercomputing community [1, 2]. The Dynamic Distributed Dimensional Data Model (D4M) developed at Lincoln Laboratory, and available at