9,649 research outputs found
Investigative Simulation: Towards Utilizing Graph Pattern Matching for Investigative Search
This paper proposes the use of graph pattern matching for investigative graph
search, which is the process of searching for and prioritizing persons of
interest who may exhibit part or all of a pattern of suspicious behaviors or
connections. While there are a variety of applications, our principal
motivation is to aid law enforcement in the detection of homegrown violent
extremists. We introduce investigative simulation, which consists of several
necessary extensions to the existing dual simulation graph pattern matching
scheme in order to make it appropriate for intelligence analysts and law
enforcement officials. Specifically, we impose a categorical label structure on
nodes consistent with the nature of indicators in investigations, as well as
prune or complete search results to ensure sensibility and usefulness of
partial matches to analysts. Lastly, we introduce a natural top-k ranking
scheme that can help analysts prioritize investigative efforts. We demonstrate
performance of investigative simulation on a real-world large dataset.Comment: 8 pages, 6 figures. Paper to appear in the Fosint-SI 2016 conference
proceedings in conjunction with the 2016 IEEE/ACM International Conference on
Advances in Social Networks Analysis and Mining ASONAM 201
Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling
The animation of network visualizations poses technical and theoretical
challenges. Rather stable patterns are required before the mental map enables a
user to make inferences over time. In order to enhance stability, we developed
an extension of stress-minimization with developments over time. This dynamic
layouter is no longer based on linear interpolation between independent static
visualizations, but change over time is used as a parameter in the
optimization. Because of our focus on structural change versus stability the
attention is shifted from the relational graph to the latent eigenvectors of
matrices. The approach is illustrated with animations for the journal citation
environments of Social Networks, the (co-)author networks in the carrying
community of this journal, and the topical development using relations among
its title words. Our results are also compared with animations based on
PajekToSVGAnim and SoNIA
Graph based Anomaly Detection and Description: A Survey
Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the âwhyâ, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field
The Public Health Exposome: A Population-Based, Exposure Science Approach to Health Disparities Research
The lack of progress in reducing health disparities suggests that new approaches are needed if we are to achieve meaningful, equitable, and lasting reductions. Current scientific paradigms do not adequately capture the complexity of the relationships between environment, personal health and population level disparities. The public health exposome is presented as a universal exposure tracking framework for integrating complex relationships between exogenous and endogenous exposures across the lifespan from conception to death. It uses a social-ecological framework that builds on the exposome paradigm for conceptualizing how exogenous exposures âget under the skinâ. The public health exposome approach has led our team to develop a taxonomy and bioinformatics infrastructure to integrate health outcomes data with thousands of sources of exogenous exposure, organized in four broad domains: natural, built, social, and policy environments. With the input of a transdisciplinary team, we have borrowed and applied the methods, tools and terms from various disciplines to measure the effects of environmental exposures on personal and population health outcomes and disparities, many of which may not manifest until many years later. As is customary with a paradigm shift, this approach has far reaching implications for research methods and design, analytics, community engagement strategies, and research training
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