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

Analysis of Swine Movements in a Province in Northern Vietnam and Application in the Design of Surveillance Strategies for Infectious Diseases

While swine production is rapidly growing in South-East Asia, the structure of the swine industry and the dynamic of pig movements have not been well-studied. However, this knowledge is a prerequisite for understanding the dynamic of disease transmission in swine populations and designing cost-effective surveillance strategies for infectious diseases. In this study, we assessed the farming and trading practices in the Vietnamese swine familial farming sector, which accounts for most pigs in Vietnam, and for which disease surveillance is a major challenge. Farmers from two communes of a Red River Delta Province (northern Vietnam) were interviewed, along with traders involved in pig transactions. Major differences in the trade structure were observed between the two communes. One commune had mainly transversal trades, that is between farms of equivalent sizes, whereas the other had pyramidal trades, that is from larger to smaller farms. Companies and large familial farrow-to-finish farms were likely to act as major sources of disease spread through pig sales, demonstrating their importance for disease control. Familial fattening farms with high pig purchases were at greater risk of disease introduction and should be targeted for disease detection as part of a risk-based surveillance. In contrast, many other familial farms were isolated or weakly connected to the swine trade network limiting their relevance for surveillance activities. However, some of these farms used boar hiring for breeding, increasing the risk of disease spread. Most familial farms were slaughtering pigs at the farm or in small local slaughterhouses, making the surveillance at the slaughterhouse inefficient. In terms of spatial distribution of the trades, the results suggested that northern provinces were highly connected and showed some connection with central and southern provinces. These results are useful to develop risk-based surveillance protocols for disease detection in the swine familial sector and to make recommendations for disease control. (Résumé d'auteur

FastSIR Algorithm: A Fast Algorithm for simulation of epidemic spread in large networks by using SIR compartment model

The epidemic spreading on arbitrary complex networks is studied in SIR (Susceptible Infected Recovered) compartment model. We propose our implementation of a Naive SIR algorithm for epidemic simulation spreading on networks that uses data structures efficiently to reduce running time. The Naive SIR algorithm models full epidemic dynamics and can be easily upgraded to parallel version. We also propose novel algorithm for epidemic simulation spreading on networks called the FastSIR algorithm that has better average case running time than the Naive SIR algorithm. The FastSIR algorithm uses novel approach to reduce average case running time by constant factor by using probability distributions of the number of infected nodes. Moreover, the FastSIR algorithm does not follow epidemic dynamics in time, but still captures all infection transfers. Furthermore, we also propose an efficient recursive method for calculating probability distributions of the number of infected nodes. Average case running time of both algorithms has also been derived and experimental analysis was made on five different empirical complex networks.Comment: 8 figure

First anatomical network analysis of fore- and hindlimb musculoskeletal modularity in bonobos, common chimpanzees, and humans

Studies of morphological integration and modularity, and of anatomical complexity in human evolution typically focus on skeletal tissues. Here we provide the first network analysis of the musculoskeletal anatomy of both the fore- and hindlimbs of the two species of chimpanzee and humans. Contra long-accepted ideas, network analysis reveals that the hindlimb displays a pattern opposite to that of the forelimb: Pan big toe is typically seen as more independently mobile, but humans are actually the ones that have a separate module exclusively related to its movements. Different fore- vs hindlimb patterns are also seen for anatomical network complexity (i.e., complexity in the arrangement of bones and muscles). For instance, the human hindlimb is as complex as that of chimpanzees but the human forelimb is less complex than in Pan. Importantly, in contrast to the analysis of morphological integration using morphometric approaches, network analyses do not support the prediction that forelimb and hindlimb are more dissimilar in species with functionally divergent limbs such as bipedal humans

Higher order assortativity in complex networks

Assortativity was first introduced by Newman and has been extensively studied and applied to many real world networked systems since then. Assortativity is a graph metrics and describes the tendency of high degree nodes to be directly connected to high degree nodes and low degree nodes to low degree nodes. It can be interpreted as a first order measure of the connection between nodes, i.e. the first autocorrelation of the degree-degree vector. Even though assortativity has been used so extensively, to the author's knowledge, no attempt has been made to extend it theoretically. This is the scope of our paper. We will introduce higher order assortativity by extending the Newman index based on a suitable choice of the matrix driving the connections. Higher order assortativity will be defined for paths, shortest paths, random walks of a given time length, connecting any couple of nodes. The Newman assortativity is achieved for each of these measures when the matrix is the adjacency matrix, or, in other words, the correlation is of order 1. Our higher order assortativity indexes can be used for describing a variety of real networks, help discriminating networks having the same Newman index and may reveal new topological network features.Comment: 24 pages, 16 figure