Bayesian anomaly detection methods for social networks

Learning the network structure of a large graph is computationally demanding, and dynamically monitoring the network over time for any changes in structure threatens to be more challenging still. This paper presents a two-stage method for anomaly detection in dynamic graphs: the first stage uses simple, conjugate Bayesian models for discrete time counting processes to track the pairwise links of all nodes in the graph to assess normality of behavior; the second stage applies standard network inference tools on a greatly reduced subset of potentially anomalous nodes. The utility of the method is demonstrated on simulated and real data sets.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS329 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Robot control in a message passing environment: theoretical questions and preliminary experiments

The performance of real-time distributed control systems is shown to depend critically on both communication and computation costs. A taxonomy for distributed system performance measurement is introduced. A roughly accurate method of performance prediction for simple systems is presented. Experimental results demonstrate the effects of communication protocols on real-world system performance

ECOSENSUS: developing collaborative learning systems for stakeholding development in environmental planning

ECOSENSUS *(Electronic/Ecological Collaborative Sensemaking Support System) investigates the socio-technological issues around developing collaboration tools for participatory environmental decision making amongst (a) marginalised natural resource users, (b) professional 'experts' from different countries, and (c) key decision makers associated with managing ecosystems. An integral activity is the production of open content learning resources to support stakeholders in facilitating distributed environmental decision making. This involves the integrated use of three open source software tools: Moodle (online course management), Compendium (dialogue mapping) and uDig (user friendly desktop/internet GIS). In the first ECOSENSUS-1 phase, the pilot collaborative effort has been focused on supporting stakeholders in developing adaptive management plans for the Rupununi Wetlands in southern Guyana, a region rich in flora and fauna but also under intense pressure to expand the exploitation of its natural resources, including timber, gold, and commercially viable fish species. Results of the ECOSENSUS-1 are briefly described along with some preliminary notes on the current ECOSENUS-2 phase of associated research in Guyana supported by an additional grant from DEFRA. The paper prompts questions on how ECOSENSUS can feed into wider open source course development using the LabSpace on the OpenLearn project

Metric Semantics and Full Abstractness for Action Refinement and Probabilistic Choice

This paper provides a case-study in the field of metric semantics for probabilistic programming. Both an operational and a denotational semantics are presented for an abstract process language L_pr, which features action refinement and probabilistic choice. The two models are constructed in the setting of complete ultrametric spaces, here based on probability measures of compact support over sequences of actions. It is shown that the standard toolkit for metric semantics works well in the probabilistic context of L_pr, e.g. in establishing the correctness of the denotational semantics with respect to the operational one. In addition, it is shown how the method of proving full abstraction --as proposed recently by the authors for a nondeterministic language with action refinement-- can be adapted to deal with the probabilistic language L_pr as well

Spreading speeds in reducible multitype branching random walk

This paper gives conditions for the rightmost particle in the $n$th generation of a multitype branching random walk to have a speed, in the sense that its location divided by n converges to a constant as n goes to infinity. Furthermore, a formula for the speed is obtained in terms of the reproduction laws. The case where the collection of types is irreducible was treated long ago. In addition, the asymptotic behavior of the number in the nth generation to the right of na is obtained. The initial motive for considering the reducible case was results for a deterministic spatial population model with several types of individual discussed by Weinberger, Lewis and Li [J. Math. Biol. 55 (2007) 207-222]: the speed identified here for the branching random walk corresponds to an upper bound for the speed identified there for the deterministic model.Comment: Published in at http://dx.doi.org/10.1214/11-AAP813 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Collective Phenomena and Non-Finite State Computation in a Human Social System

We investigate the computational structure of a paradigmatic example of distributed social interaction: that of the open-source Wikipedia community. We examine the statistical properties of its cooperative behavior, and perform model selection to determine whether this aspect of the system can be described by a finite-state process, or whether reference to an effectively unbounded resource allows for a more parsimonious description. We find strong evidence, in a majority of the most-edited pages, in favor of a collective-state model, where the probability of a "revert" action declines as the square root of the number of non-revert actions seen since the last revert. We provide evidence that the emergence of this social counter is driven by collective interaction effects, rather than properties of individual users.Comment: 23 pages, 4 figures, 3 tables; to appear in PLoS ON