Etude de la Maximisation de l'Influence dans les Réseaux Sociaux

National audienceInfluence maximization is a NP-hard problem depending on the diffusion of information in social networks. The Greedy hill climbing algorithm have been proved a good approximation if the influence fonction we try to optimize is submodular, which is the case for standard diffusion models.We present a diffusion model not equivalent to standard models for which the influence function is not submodular. Then we propose, using toy graphs and a real social network, a study of different influence maximization algorithms on this model and on the standard model IC: some basic heuristics, the greedy hill climbing method, a generalization of the greedy method and an optimization method for submodular functions. We show that even if the influence function is not submodular, the greedy algorithm obtain good results while being able to scale efficiently

Learning Information Spread in Content Networks

We introduce a model for predicting the diffusion of content information on social media. When propagation is usually modeled on discrete graph structures, we introduce here a continuous diffusion model, where nodes in a diffusion cascade are projected onto a latent space with the property that their proximity in this space reflects the temporal diffusion process. We focus on the task of predicting contaminated users for an initial initial information source and provide preliminary results on differents datasets.Comment: 4 page

Predicting Information Diffusion in Social Networks using Content and User's Profiles

International audiencePredicting the diffusion of information on social networks is a key problem for applications like Opinion Leader Detection, Buzz Detection or Viral Marketing. Many recent diffusion models are direct extensions of the Cascade and Threshold models, initially proposed for epidemiology and social studies. In such models, the diffusion process is based on the dynamics of interactions between neighbor nodes in the network (the social pressure), and largely ignores important dimensions as the content of the piece of information diffused. We propose here a new family of probabilistic models that aims at predicting how a con- tent diffuses in a network by making use of additional dimensions: the content of the piece of information diffused, user's profile and willing- ness to diffuse. These models are illustrated and compared with other approaches on two blog datasets. The experimental results obtained on these datasets show that taking into account the content of the piece of information diffused is important to accurately model the diffusion process

Un Modèle de Diffusion de l'Information dans les Réseaux Sociaux

Numéro spécial sur les journées AAFD (Apprentissage Artificiel et Fouille de Données, avril 2010)National audienceLes réseaux sociaux sont un outil que les gens utilisent de plus en plus pour communiquer et partager de l'information. Un certain nombre d'études ont été effectuées, sur les réseaux sociaux, la propagation de l'innovation et les maladies afin de comprendre et de modéliser la diffusion dans des graphes d'utilisateurs. Dans un premier temps, nous présentons ici un modèle de diffusion de l'information dans les graphes de contenu alliant aussi bien l'influence des voisins que celle de la proximité avec le contenu, avant d'illustrer notre modèle par des exemples de diffusion sur des réseaux générés manuellement et des réseaux réels. Puis, dans une seconde partie, nous introduisons une dynamique de groupe afin de considérer ensemble les utilisateurs similaires au sein du réseau social

Modéliser l'utilisateur pour la diffusion de l'information dans les réseaux sociaux

Numéro spécial sur "Systèmes d'information : impact des réseaux sociaux"National audiencePredicting information diffusion in social networks is a hard task which can lead to interesting applications: recommending relevant information for users, choosing the best entry points in the network for the best diffusion of a given piece of information, etc. We present new models which take into account three main characteristics: the number of neighbors who have disclosed the information, the relevance of the information for each user and the willingness of users to diffuse information. After this presentation, we propose to estimate the parameters of our models and illustrate their behavior through a comparison with standard information diffusion models on a real dataset. We also propose a study of the influence maximization problem associated with these new models

Valuation of Startups: A Machine Learning Perspective

International audienceWe address the problem of startup valuation from a machine learning perspective with a focus on European startups. More precisely, we aim to infer the valuation of startups corresponding to the funding rounds for which only the raised amount was announced. To this end, we mine Crunchbase, a well-established source of information on companies. We study the discrepancy between the properties of the funding rounds with and without the startup’s valuation announcement and show that the Domain Adaptation framework is suitable for this task. Finally, we propose a method that outperforms, by a large margin, the approaches proposed previously in the literature

Assessing the Factors Related to a Start-Up’s Valuation Using Prediction and Causal Discovery

Research indicates that investors rely on various criteria to evaluate early-stage companies. However, past research in this area has focused on subsets of factors and does not distinguish between the predictors and causal determinants of start-up valuation. In our study, we applied machine learning and causal discovery to analyze a comprehensive dataset with 57 independent variables and 2,366 valuations of start-ups in the United Kingdom. The results show a strong relationship between good predictors and causal determinants of valuation. However, noncausal variables may still be useful for prediction, and inversely, some observed causes may not help in the prediction task