116 research outputs found
A data-driven analysis to question epidemic models for citation cascades on the blogosphere
Citation cascades in blog networks are often considered as traces of
information spreading on this social medium. In this work, we question this
point of view using both a structural and semantic analysis of five months
activity of the most representative blogs of the french-speaking
community.Statistical measures reveal that our dataset shares many features
with those that can be found in the literature, suggesting the existence of an
identical underlying process. However, a closer analysis of the post content
indicates that the popular epidemic-like descriptions of cascades are
misleading in this context.A basic model, taking only into account the behavior
of bloggers and their restricted social network, accounts for several important
statistical features of the data.These arguments support the idea that
citations primary goal may not be information spreading on the blogosphere.Comment: 18 pages, 9 figures, to be published in ICWSM-13 proceeding
Visual Analysis of Complex Networks for Business Intelligence with Gephi
International audiencePlatforms which combine data mining algorithms and interactive visualizations play a key role in the discovery process from complex networks data, e.g. Web and Online Social Networks data. Here we illustrate the use of Gephi, an open source software for networks visual exploration, for the visual analysis of Business Intelligence data modeled as complex networks
Suivi de la Dynamique Intrinsèque des Interactions entre Utilisateur et SI
National audienceMonitoring the evolution of user-system interactions is of high importance for complex systems and for information systems in particular, especially to raise alerts automatically when abnormal behaviors occur. However current methods fail at capturing the intrinsic dynamics of the system, and focus on evolution due to exogenous factors like day-night patterns. In order to capture the intrinsic dynamics of user-system interactions, we propose an innovative graph-based approach relying on a novel concept of time. We apply our method on a large real-world system (the Github.com social network) to automatically detect statistically signi cant events in a real-time fashion. We nally validate our results with the successful interpretation of the detected events
Towards an Automatic Extraction of Smartphone Users' Contextual Behaviors
International audienceThis paper presents a new method for automatically extracting smartphone users' contextual behaviors from the digital traces collected during their interactions with their devices. Our goal is in particular to understand the impact of users' context (e.g., location, time, environment, etc.) on the applications they run on their smartphones. We propose a methodology to analyze digital traces and to automatically identify the significant information that characterizes users' behaviors. In earlier work, we have used Formal Concept Analysis and Galois lattices to extract relevant knowledge from heterogeneous and complex contextual data; however, the interpretation of the obtained Galois lattices was performed manually. In this article, we aim at automating this interpretation process, through the provision of original metrics. Therefore our methodology returns relevant information without requiring any expertise in data analysis. We illustrate our contribution on real data collected from volunteer users
Reducing Offline Evaluation Bias in Recommendation Systems
Recommendation systems have been integrated into the majority of large online
systems. They tailor those systems to individual users by filtering and ranking
information according to user profiles. This adaptation process influences the
way users interact with the system and, as a consequence, increases the
difficulty of evaluating a recommendation algorithm with historical data (via
offline evaluation). This paper analyses this evaluation bias and proposes a
simple item weighting solution that reduces its impact. The efficiency of the
proposed solution is evaluated on real world data extracted from Viadeo
professional social network.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn
2014), Bruxelles : Belgium (2014
Using the Mean Absolute Percentage Error for Regression Models
We study in this paper the consequences of using the Mean Absolute Percentage
Error (MAPE) as a measure of quality for regression models. We show that
finding the best model under the MAPE is equivalent to doing weighted Mean
Absolute Error (MAE) regression. We show that universal consistency of
Empirical Risk Minimization remains possible using the MAPE instead of the MAE.Comment: European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. 2015,
Proceedings of the 23-th European Symposium on Artificial Neural Networks,
Computational Intelligence and Machine Learning (ESANN 2015
Reducing offline evaluation bias of collaborative filtering algorithms
Recommendation systems have been integrated into the majority of large online
systems to filter and rank information according to user profiles. It thus
influences the way users interact with the system and, as a consequence, bias
the evaluation of the performance of a recommendation algorithm computed using
historical data (via offline evaluation). This paper presents a new application
of a weighted offline evaluation to reduce this bias for collaborative
filtering algorithms.Comment: European Symposium on Artificial Neural Networks, Computational
Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium.
pp.137-142, 2015, Proceedings of the 23-th European Symposium on Artificial
Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015
Semantics of Higraphs for Process Modeling and Analysis
International audienceKnowledge and experience of a case manager remains a key success factor for Case Management Processes (CMPs). When a number of influential parameters is high, a number of possible scenarios grows significantly. Automated guidance in scenario evaluation and activity planning would be of a great help. In our previous work, we defined the statecharts semantics for visualisation and simulation of CMP scenarios. In this work, we formalise the state-oriented models with higraphs: higraphs provide mathematical foundation for statecharts and eventually enable a wide panoply of algorithms for process analysis and optimisation. We show how a statecharts diagram can be transformed into higraph and analysed at run-time with graph algorithms. In particular, we take an example of the Shortest Path algorithm and show how this algorithm can be used in order to guide the case manager suggesting her the best process scenario. Compared to BPM approaches, a state-oriented process scenario does not specify concrete activities but only the objectives and constraints to be met. Thus, our approach does not prescribe but describe an activity to be executed next. The manager can define an activity that fit the description " on the fly " , based on her experience and intuition
Restitution aux enseignants de l'Ă©valuation des apprentissages dans des EIAH
International audienceLes environnements informatiques sont de plus en plus utilisés dans les processus d'apprentissage, que ce soit pour l'acquisition, la consolidation ou l'évaluation de compétences. Dans cet article, nous nous intéressons spécifiquement aux EIAH utilisés en complément d'un apprentissage classique en présentiel. Notre objectif est d'utiliser les résultats des évaluations des apprenants afin de restituer des connaissances actionnables aux enseignants. Notre objectif est aussi de réfléchir aux besoins spécifiques des disciplines non scientifiques, en particulier l'anglais de spécialité. Nous décrivons tout d'abord les modalités d'évaluation proposées dans une sélection de plateformes existantes. Nous évoquons les limites des traditionnels QCM. Nous dressons ensuite un panorama des techniques de fouille de données, de graphes et de processus qui peuvent être mises en oeuvre pour l'analyse et la restitution des résultats d'évaluation aux enseignants. Nous fournissons quelques exemples concrets issus de l'analyse de données réelles et nous présentons nos perspectives de recherche sur ce sujet. Mots-clés. Evaluation des apprentissages ; analyse de traces ; fouille de données ; fouille de processus ; anglais de spécialit
Consistance de la minimisation du risque empirique pour l'optimisation de l'erreur relative moyenne
National audienceWe study in this paper the consequences of using the Mean Absolute Percentage Error (MAPE) as a measure of quality for regression models. We show that finding the best model under the MAPE is equivalent to doing weighted Mean Absolute Error (MAE) regression. We also show that, under some asumptions, universal consistency of Empirical Risk Minimization remains possible using the MAPE.Nous nous intéressons au problème de la minimisation de l'erreur relative moyenne dans le cadre des modèles de régression. Nous montrons que l'optimisation de ce critère est équivalente à la minimisation de l'erreur absolue par régressions pondérées et que l'approche par minimisation du risque empirique est, sous certaines hypothèses, consistante pour la minimisation de ce critère
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