Sylvain Lamprier

Learning social network embeddings for predicting information diffusion

Analyzing and modeling the temporal diffusion of information on social media has mainly been treated as a diffusion process on known graphs or proximity structures. The underlying phenomenon results however from the interactions of several actors and media and is more complex than what these models can account for and cannot be explained using such limiting assumptions. We introduce here a new approach to this problem whose goal is to learn a mapping of the observed temporal dynamic onto a continuous space. Nodes participating to diffusion cascades are projected in a latent representation space in such a way that information diffusion can be modeled efficiently using a heat diffusion process. This amounts to learning a diffusion kernel for which the proximity of nodes in the projection space reflects the proximity of their infection time in cascades. The proposed approach possesses several unique characteristics compared to existing ones. Since its parameters are directly learned from cascade samples without requiring any additional information, it does not rely on any pre-existing diffusion structure. Because the solution to the diffusion equation can be expressed in a closed form in the projection space, the inference time for predicting the diffusion of a new piece of information is greatly reduced compared to discrete models. Experiments and comparisons with baselines and alternative models have been performed on both synthetic networks and real datasets. They show the effectiveness of the proposed method both in terms of prediction quality and of inference speed.

CARE: A Platform for Reliable Comparison and Analysis of Reverse-Engineering Techniques

Reverse engineering of behavior models has received a lot of attention over the last few years. However, no standard benchmark exists for the comparison and analysis of published miners. Evaluation is usually performed on few case studies, which fails to demonstrate effectiveness in a broad context. This paper proposes a general, approach-independent, platform for the intensive evaluation of behavior miners. Its goals are essentially: provide a benchmark mechanism for reverse engineering; allow analysis of miners w.r.t. a class of programs and/or behaviors; help users in choosing the best suited approach for their objective.

Exact and Efficient Temporal Steering of Software Behavioral Model Inference

Behavior Model Inference techniques aim at mining behavior models from execution traces. While most of approaches usually ground on local similarities in traces, recent work, referred to as behavior mining with temporal steering, propose to include long term dependencies in the mining process. Such dependencies correspond to temporal implications between events in execution traces, whose consideration allows to ensure a better consistency of the extracted model. Nevertheless, the existing approaches are usually limited by their high computational complexity and the approximations to reduce the cost of temporal rules checking. This paper revisits behavior mining with temporal steering by defining an efficient algorithm that performs an exact consideration of the observed dependencies: in our experiments, greatly reduced processing times (from exponential to quasi-linear) for exact mining with temporal steering have been observed. Furthermore, beyond highlighting the great benefits of considering temporal dependencies, this paper also proposes new key extensions to the existing work that allow to include more complex dependencies in the mining process. Intensive evaluation finally demonstrates the great performances of the proposed approach.

Query-oriented clustering: a multi-objective approach

Document clustering techniques have been widely applied in Information Retrieval to reorganize results furnished as a response to users queries. Following the Cluster Hypothesis which states that relevant documents tend to be more similar one to each other than to non-relevant ones, most of relevant documents are likely to be gathered in a single cluster. Usually, systems organizing search results as a set of clusters consider this tendency as a very advantageous phenomenon, since it allows to filter the results provided by the initial search. Adopting a different point of view, we rather consider the Cluster Hypothesis as a hindrance to the information access since it prevents the emergence of the various aspects of the query. The risk induced is to restrict the perception of the subject to an unique point of view. Therefore, we propose to rather distribute the relevant documents over clusters by orienting the organization of the clusters according to the users topic. The aim is to attract the clusters around the latter in order to highlight the thematic differences between documents which are strongly connected to the query. Rather than modifying the inter-documents similarity computation as it is the case in several studies, we propose to directly act on the organization of the clusters by using a multi-objective evolutionary clustering algorithm which, besides the classical internal cohesion, also optimizes the query proximity of the clusters. First experimental results highlight the great benefit which may be gained by our way of query consideration.

Traveling among clusters: a way to reconsider the benefits of the cluster hypothesis

Relying on the Cluster Hypothesis which states that relevant documents tend to be more similar one to each other than to non-relevant documents, most of information retrieval systems organizing search results as a set of clusters seek to gather all relevant documents in the same cluster. We propose here to reconsider the benefits of the entailed concentration of the relevant information. Contrary to what is commonly admitted, we believe that systems which aim to distribute the relevant documents in different clusters, since being more likely to highlight different aspects of the subject, may be at least as useful for the user as systems gathering all relevant documents in a single group. Since existing evaluation measures tend to greatly favor the latter systems, we first investigate ways to more fairly assess the ability to reach the relevant information from the list of cluster descriptions. At last, we show that systems distributing the relevant information in different clusters may actually provide a better information access than classical systems.

Apprentissage en temps réel pour la collecte d'information dans les réseaux sociaux

Dans cet article nous nous interessons a la collecte d’information dans les reseaux sociaux. Cette tâche, primordiale pour de nombreuses applications, se heurte souvent a diverses contraintes liees aux ressources a disposition ou a des restrictions imposees par les API des medias consideres. Nous formulons cette tâche comme un probleme de selection dynamique de sources, pour lequel nous proposons une methode d’apprentissage pour orienter la collecte vers les donnees les plus pertinentes en fonction d’un besoin specifie. Notre methode est basee sur une extension d’un algorithme de bandit combinatoire recemment propose. Nous fournissons des garanties theoriques sur le comportement de l’algorithme, que nous evaluons ensuite sur differents jeux de donnees Twitter, a la fois hors ligne et en ligne, pour differents besoins de donnees exprimes.

The CARE platform for the analysis of behavior model inference techniques

Context: Finite State Machine (FSM) inference from execution traces has received a lot of attention over the past few years. Various approaches have been explored, each holding different properties for the resulting models, but the lack of standard benchmarks limits the ability of comparing the proposed techniques. Evaluation is usually performed on a few case studies, which is useful for assessing the feasibility of the algorithm on particular cases, but fails to demonstrate effectiveness in a broad context. Consequently, understanding the strengths and weaknesses of inference techniques remains a challenging task. Objective: This paper proposes CARE, a general, approach-independent, platform for the intensive evaluation of FSM inference techniques. Method: Grounded in a program specification scheme that provides a good control on the expected program structures, it allows the production of large benchmarks with well identified properties. Results: The CARE platform demonstrates the following features: (1) providing a benchmarking mechanism for FSM inference techniques, (2) allowing analysis of existing techniques w.r.t. a class of programs and/or behaviors, and (3) helping users in choosing the best suited approach for their objective. Moreover, our extensive experiments on different FSM inference techniques highlight that they do not behave in the same manner on every class of program. Characterizing different classes of programs thus helps understanding the strengths and weaknesses of the studied techniques. Conclusion: Experiments reported in this paper show examples of use cases that demonstrate the ability of the platform to generate large and diverse sets of programs, which allows to carry out meaningful inference techniques analysis. The analysis strategies the CARE platform offers open new opportunities for program behavior learning, particularly in conjunction with model checking techniques. The CARE platform is available at http://care.lip6.fr.