International audience

Given an arbitrary group of computers, how to identify abnormal changes in their communication pattern? How to assess if the absence of some communications is normal or due to a failure? How to distinguish local from global events when communication data are extremely sparse and volatile? Existing approaches for anomaly detection in interaction streams, focusing on edge, nodes or graphs, lack flexibility to monitor arbitrary communication topologies. Moreover, they rely on structural features that are not adapted to highly sparse settings. In this work, we introduce MAD, a novel Multi-scale Anomaly Detection algorithm that (i) allows to query for the normality/abnormality state of an arbitrary group of observed/non-observed communications at a given time; and (ii) handles the highly sparse and uncertain nature of interaction data through a scoring method that is based on a novel probabilistic and multi-scale analysis of sub-graphs. In particular, MAD is (a) flexible: it can assess if any time-stamped subgraph is anomalous, making edge, node and graph anomalies particular instances; (b) interpretable: its multi-scale analysis allows to characterize the scope and nature of the anomalies; (c) efficient: given historical data of length N and M observed/non-observed communications to analyze, MAD produces an anomaly score in O (NM); and (d) effective: it significantly outperforms state-of-the-art alternatives tailored for edge, node or graph anomalies.

International audience

In this paper, we focus on the very specificity of rumors as pieces of information for modeling their process of propagation. We consider a population of pedestrians walking in a city and we assume that a rumor is transmitted by word of mouth from one to another. Although the diffusion of a rumor is of course a multi-dimensional process driven by sociological, economical and psychological elements, in this first step, we emphasize one main dimension of this complex phenomenon only. This dimension is the neighborhood of individuals likely to spread the information. With a confrontation of two antagonistic properties of the neighborhood that are profusion and scarcity of spreaders, we highlight specific characteristics of rumors. This study could lead to the psychological mechanism involved in the decision for a person to become or not a spreader himself/herself. In summary, we study if scarcity could be the silver bullet explaining how a rumor spreads.

Through the development of electronic commerce, social media and collaborative media, the social commerce appeared. Social commerce, a subset of electronic commerce, is based on social interactions in order to buy and sell goods and services. Nowadays, before buying, people give more importance to the experience feedback they found on internet. However, it is difficult to get an overview of this experience feedback since it is scattered in many online resources, and buyers never have time to read many pages of comments. In this paper, we present an approach which grabs and analyzes experience feedback in order to publish a summary of opinions about a product. We develop this approach with a case study on smartphones and publish a dataset of thousands of comments on a wide range of smartphones. To summarize experience feedback, we use a linguistic appraisal model, based on appreciation, affect and judgement, and we set up an approach using methods and tools from the fields of natural language processing, opinion mining and sentiment analysis.

Cancer has recently become the leading cause of death worldwide according to the World Health Organization. As a consequence, health authorities acknowledge the need to implement prevention and screening programs to decrease its incidence. The efficiency of these programs can be increased by targeting higher risk subsets of the population. Efficient tools capable of monitoring the population risk are therefore needed. Constraints to building cancer risk scores and impacts on the tools platform are presented. Major constraints beyond performance of a risk score concern the role of domain experts and their acceptability by end users. Readability is therefore an important criterion. It is shown that a simple k-nearest-neighbor algorithm can achieve good performance with the help of the domain expert. To illustrate this, a risk score made of only four attributes is presented for the French population.