Classification of Message Spreading in a Heterogeneous Social Network

Nowadays, social networks such as Twitter, Facebook and LinkedIn become increasingly popular. In fact, they introduced new habits, new ways of communication and they collect every day several information that have different sources. Most existing research works fo-cus on the analysis of homogeneous social networks, i.e. we have a single type of node and link in the network. However, in the real world, social networks offer several types of nodes and links. Hence, with a view to preserve as much information as possible, it is important to consider so-cial networks as heterogeneous and uncertain. The goal of our paper is to classify the social message based on its spreading in the network and the theory of belief functions. The proposed classifier interprets the spread of messages on the network, crossed paths and types of links. We tested our classifier on a real word network that we collected from Twitter, and our experiments show the performance of our belief classifier

An interval-valued dissimilarity measure for belief functions based on credal semantics

Abstract Evidence theory extends Bayesian probability theory by allowing for a more expressive model of subjective uncertainty. Besides standard interpretation of belief functions, where uncertainty corresponds to probability masses which might refer to whole subsets of the possibility space, credal semantics can be also considered. Accordingly, a belief function can be identified with the whole set of probability mass functions consistent with the beliefs induced by the masses. Following this interpretation, a novel, set-valued, dissimilarity measure with a clear behavioral interpretation can be defined. We describe the main features of this new measure and comment the relation with other measures proposed in the literature.

Geometric Interpretations of Conflict: A Viewpoint

International audienc

Comment les jeux (sérieux) peuvent-ils aider à tracer le processus d'expertise liée aux avalanches de neige ? Une méthodologie innovante et une application à la gestion des risques routiers

ISSW 2018 International Snow Science Workshop, Innsbruck, AUT, 07-/10/2018 - 12/10/2018International audienceManagement of mountain roads exposed to snow avalanches involves several actors local who have often to take difficult and quick decisions under social, political, economic pressures in a context of imperfect (lacking, incomplete, conflicting or uncertain) information. An innovative serious gaming concept is proposed to both aid, improve and trace decision processes considering influence of information quality on decisions

A framework for dynamic context exploitation

While the benefits of exploiting Contextual Information (CI) are starting being recognized by the Information Fusion (IF) community [1], most current approaches for CI inclusion lead to stove-piped solutions that hardly scale or adapt to new input or situations. This paper makes a step in the direction of better CI exploitation by presenting some results of an international collaboration investigating the role of CI in IF and proposing an adaptive framework that dynamically takes into consideration CI to better support mission goals. In particular, we discuss some architecture concepts to be considered in the development of fusion systems including CI and we present how context can be dynamically exploited at different levels of a fusion engine. The concepts are illustrated in a maritime use-case. \ua9 2015 IEEE

Uncertainty in Ontology Matching: A Decision Rule-Based Approach

International audienceConsidering the high heterogeneity of the ontologies pub-lished on the web, ontology matching is a crucial issue whose aim is to establish links between an entity of a source ontology and one or several entities from a target ontology. Perfectible similarity measures, consid-ered as sources of information, are combined to establish these links. The theory of belief functions is a powerful mathematical tool for combining such uncertain information. In this paper, we introduce a decision pro-cess based on a distance measure to identify the best possible matching entities for a given source entity