14 research outputs found
Network Archaeology: Uncovering Ancient Networks from Present-day Interactions
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
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
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
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Education in the Wild: Contextual and Location-Based Mobile Learning in Action. A Report from the STELLAR Alpine Rendez-Vous Workshop Series
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Introduction to location-based mobile learning
[About the book]
The report follows on from a 2-day workshop funded by the STELLAR Network of Excellence as part of their 2009 Alpine Rendez-Vous workshop series and is edited by Elizabeth Brown with a foreword from Mike Sharples. Contributors have provided examples of innovative and exciting research projects and practical applications for mobile learning in a location-sensitive setting, including the sharing of good practice and the key findings that have resulted from this work. There is also a debate about whether location-based and contextual learning results in shallower learning strategies and a section detailing the future challenges for location-based learning
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Augmenting the field experience: a student-led comparison of techniques and technologies
In this study we report on our experiences of creating and running a student fieldtrip exercise which allowed students to compare a range of approaches to the design of technologies for augmenting landscape scenes. The main study site is around Keswick in the English Lake District, Cumbria, UK, an attractive upland environment popular with tourists and walkers. The aim of the exercise for the students was to assess the effectiveness of various forms of geographic information in augmenting real landscape scenes, as mediated through a range of techniques and technologies. These techniques were: computer-generated acetate overlays showing annotated wireframe views from certain key points; a custom-designed application running on a PDA; a mediascape running on the mScape software on a GPS-enabled mobile phone; Google Earth on a tablet PC; and a head-mounted in-field Virtual Reality system. Each group of students had all five techniques available to them, and were tasked with comparing them in the context of creating a visitor guide to the area centred on the field centre. Here we summarise their findings and reflect upon some of the broader research questions emerging from the project
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
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Reflecting back, looking forward: the challenges for location-based learning
This final section of the report has been reproduced from âD3.1 The STELLAR Rendez-Vous I report and white papersâ, published in 2009 by the STELLAR Network of Excellence. It is included here for completeness; we, as co-authors, felt that it was important to look back at the main contributions to theworkshop and also where the challenges lie for the future.
This chapter addresses two critical questions:
- What has been learned from this workshop, especially in respect to the STELLAR Grand Challenges (âConnecting learnersâ, âOrchestrationâ and âContextualisationâ)?
- What are the new research questions and issues for location-based learning, with respect to the Grand Challenges (âConnecting learnersâ, âOrchestrationâand âContextualisationâ)