Fast Bayesian inference in large Gaussian graphical models

Despite major methodological developments, Bayesian inference in Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a method to infer the marginal and conditional independence structures between variables by multiple testing, which bypasses the exploration of the model space. Specifically, we introduce closed-form Bayes factors under the Gaussian conjugate model to evaluate the null hypotheses of marginal and conditional independence between variables. Their computation for all pairs of variables is shown to be extremely efficient, thereby allowing us to address large problems with thousands of nodes as required by modern applications. Moreover, we derive exact tail probabilities from the null distributions of the Bayes factors. These allow the use of any multiplicity correction procedure to control error rates for incorrect edge inclusion. We demonstrate the proposed approach on various simulated examples as well as on a large gene expression data set from The Cancer Genome Atlas.This research was supported by the Medical Research Council core funding number MRC MC UP 0801/and grant number MR/M004421

Graph-based Features for Automatic Online Abuse Detection

While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance , with results comparable to those previously obtained with a content-based approach

Conedy: a scientific tool to investigate Complex Network Dynamics

We present Conedy, a performant scientific tool to numerically investigate dynamics on complex networks. Conedy allows to create networks and provides automatic code generation and compilation to ensure performant treatment of arbitrary node dynamics. Conedy can be interfaced via an internal script interpreter or via a Python module

Social Experience of Captive Livingstone’s Fruit Bats (Pteropus livingstonii)

Social network analysis has been highlighted as a powerful tool to enhance the evidence-based management of captive-housed species through its ability to quantify the social experience of individuals. We apply this technique to explore the social structure and social roles of 50 Livingstone’s fruit bats (Pteropus livingstonii) housed at Jersey Zoo, Channel Islands, through the observation of associative, affiliative, and aggressive interactions over two data collection periods. We implement binomial mixture modelling and characteristic-based assortment quantification to describe the complexity and organisation of social networks, as well as a multiple regression quadratic assignment procedural (MRQAP) test to analyse the relationship between network types. We examine the effects of individual characteristics (i.e., sex, age, and dominance rank) on social role by fitting models to explain the magnitude of node metrics. Additionally, we utilize a quadratic assignment procedural (QAP) test to assess the temporal stability of social roles over two seasons. Our results indicate that P. livingstonii display a non-random network structure. Observed social networks are positively assorted by age, as well as dominance rank. The frequency of association between individuals correlates with a higher frequency of behavioural interactions, both affiliative and aggressive. Individual social roles remain consistent over ten months. We recommend that, to improve welfare and captive breeding success, relationships between individuals of similar ages and dominance levels should be allowed to persist in this group where possible, and separating individuals that interact frequently in an affiliative context should be avoided