A reliability-based approach for influence maximization using the evidence theory

The influence maximization is the problem of finding a set of social network users, called influencers, that can trigger a large cascade of propagation. Influencers are very beneficial to make a marketing campaign goes viral through social networks for example. In this paper, we propose an influence measure that combines many influence indicators. Besides, we consider the reliability of each influence indicator and we present a distance-based process that allows to estimate the reliability of each indicator. The proposed measure is defined under the framework of the theory of belief functions. Furthermore, the reliability-based influence measure is used with an influence maximization model to select a set of users that are able to maximize the influence in the network. Finally, we present a set of experiments on a dataset collected from Twitter. These experiments show the performance of the proposed solution in detecting social influencers with good quality.Comment: 14 pages, 8 figures, DaWak 2017 conferenc

Generalised max entropy classifiers

In this paper we propose a generalised maximum-entropy classification framework, in which the empirical expectation of the feature functions is bounded by the lower and upper expectations associated with the lower and upper probabilities associated with a belief measure. This generalised setting permits a more cautious appreciation of the information content of a training set. We analytically derive the KarushKuhn-Tucker conditions for the generalised max-entropy classifier in the case in which a Shannon-like entropy is adopted

Uncertainty representation and evaluation for modelling and decision-making in information fusion

In this paper, the uncertainties that enter through the life-cycle of an information fusion system are exhaustively and explicitly considered and defined. Addressing the factors that influence a fusion system is an essential step required before uncertainty representation and reasoning processes within a fusion system can be evaluated according to the Uncertainty Representation and Reasoning Evaluation Framework (URREF) ontology. The life cycle of a fusion system consists primarily of two stages, namely inception and design, as well as routine operation and assessment. During the inception and design stage, the primary flow is that of abstraction, through modelling and representation of real-world phenomena. This stage is mainly characterised by epistemic uncertainty. During the routine operation and assessment stage, aleatory uncertainty combines with epistemic uncertainty from the design phase as well as uncertainty about the effect of actions on the mission in a feedback loop (another form of epistemic uncertainty). Explicit and accurate internal modelling of these uncertainties, and the evaluation of how these uncertainties are represented and reasoned about in the fusion system using the URREF ontology, are the main contributions of this paper for the information fusion community. This paper is an extension of previous works by the authors, where all uncertainties pertaining to the complete fusion life cycle are now jointly and comprehensively considered. Also, uncertainties pertaining to the decision process are further detailed

A clustering model for uncertain preferences based on belief functions

International audienceCommunity detection is a popular topic in network science field. In social network analysis, preference is often applied as an attribute for individuals' representation. In some cases, uncertain and imprecise preferences may appear in some cases. Moreover, conflicting preferences can arise from multiple sources. From a model for imperfect preferences we proposed earlier, we study the clustering quality in case of perfect preferences as well as imperfect ones based on weak orders (orders that are complete, reflexive and transitive). The model for uncertain preferences is based on the theory of belief functions with an appropriate dissimilarity measure when performing the clustering steps. To evaluate the quality of clustering results, we used Adjusted Rand Index (ARI) and silhouette score on synthetic data as well as on Sushi preference data set collected from real world. The results show that our model has an equivalent quality with traditional preference representations for certain cases while it has better quality confronting imperfect cases

CEC-Model: A new competence model for CBR systems based on the belief function theory

International audienceThe high influence of case bases quality on Case-Based Reasoning success gives birth to an important study on cases competence for problems resolution. The competence of a case base (CB), which presents the range of problems that it can successfully solve, depends on various factors such as the CB size and density. Besides, it is not obvious to specify the exactly relationship between the individual and the overall cases competence. Hence, numerous Competence Models have been proposed to evaluate CBs and predict their actual coverage and competence on problem-solving. However, to the best of our knowledge, all of them are totally neglecting the uncertain aspect of information which is widely presented in cases since they involve real world situations. Therefore, this paper presents a new competence model called CEC-Model (Coverage & Evidential Clustering based Model) which manages uncertainty during both of cases clustering and similarity measurement using a powerful tool called the belief function theory