14 research outputs found

    Network Archaeology: Uncovering Ancient Networks from Present-day Interactions

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    Often questions arise about old or extinct networks. What proteins interacted in a long-extinct ancestor species of yeast? Who were the central players in the Last.fm social network 3 years ago? Our ability to answer such questions has been limited by the unavailability of past versions of networks. To overcome these limitations, we propose several algorithms for reconstructing a network's history of growth given only the network as it exists today and a generative model by which the network is believed to have evolved. Our likelihood-based method finds a probable previous state of the network by reversing the forward growth model. This approach retains node identities so that the history of individual nodes can be tracked. We apply these algorithms to uncover older, non-extant biological and social networks believed to have grown via several models, including duplication-mutation with complementarity, forest fire, and preferential attachment. Through experiments on both synthetic and real-world data, we find that our algorithms can estimate node arrival times, identify anchor nodes from which new nodes copy links, and can reveal significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure

    The dynamics of animal social networks: Analytical, conceptual, and theoretical advances

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    Social network analysis provides a broad and complex perspective on animal sociality that is widely applicable to almost any species. Recent applications demonstrate the utility of network analysis for advancing our understanding of the dynamics, selection pressures, development, and evolution of complex social systems. However, most studies of animal social networks rely primarily on a descriptive approach. To propel the field of animal social networks beyond exploratory analyses and to facilitate the integration of quantitative methods that allow for the testing of ecologically and evolutionarily relevant hypotheses, we review methodological and conceptual advances in network science, which are underutilized in studies of animal sociality. First, we highlight how the use of statistical model- ing and triadic motifs analysis can advance our understanding of the processes that structure networks. Second, we discuss how the consideration of temporal changes and spatial constraints can shed light on the dynamics of social networks. Third, we consider how the study of variation at multiple scales can potentially transform our understanding of the structure and function of animal networks. We direct readers to analytical tools that facilitate the adoption of these new concepts and methods. Our goal is to provide behavioral ecologists with a toolbox of current methods that can stimulate novel insights into the ecological influences and evolutionary pressures structuring networks and advance our understanding of the proximate and ultimate processes that drive animal sociality

    The dynamics of animal social networks: Analytical, conceptual, and theoretical advances

    Get PDF
    Social network analysis provides a broad and complex perspective on animal sociality that is widely applicable to almost any species. Recent applications demonstrate the utility of network analysis for advancing our understanding of the dynamics, selection pressures, development, and evolution of complex social systems. However, most studies of animal social networks rely primarily on a descriptive approach. To propel the field of animal social networks beyond exploratory analyses and to facilitate the integration of quantitative methods that allow for the testing of ecologically and evolutionarily relevant hypotheses, we review methodological and conceptual advances in network science, which are underutilized in studies of animal sociality. First, we highlight how the use of statistical model- ing and triadic motifs analysis can advance our understanding of the processes that structure networks. Second, we discuss how the consideration of temporal changes and spatial constraints can shed light on the dynamics of social networks. Third, we consider how the study of variation at multiple scales can potentially transform our understanding of the structure and function of animal networks. We direct readers to analytical tools that facilitate the adoption of these new concepts and methods. Our goal is to provide behavioral ecologists with a toolbox of current methods that can stimulate novel insights into the ecological influences and evolutionary pressures structuring networks and advance our understanding of the proximate and ultimate processes that drive animal sociality

    Graph based Anomaly Detection and Description: A Survey

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    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 ‘why’, 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
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