Section: Research Program

Introduction

The main objective of Magnet is to develop original machine learning methods for networked data. We consider information networks in which the data are vectorial data and texts. We model such information networks as (multiple) (hyper)graphs wherein nodes correspond to entities (documents, spans of text, users, ...) and edges correspond to relations between entities (similarity, answer, co-authoring, friendship, ...). Our main research goal is to propose new learning algorithms to build applications like browsing, monitoring and recommender systems, and more broadly information extraction in information networks. Hence, we will investigate new learning algorithms for node clustering and node classification, link classification and link prediction. Also, we will search for the best hidden graph structure to be generated for solving a given learning task. We will base our research on generative models for graphs, on machine learning for graphs and on machine learning for texts. The challenges are the dimensionality of the input space, possibly the dimensionality of the output space, the high level of dependencies between the data, the inherent ambiguity of textual data and the limited amount of human labeling. An additional challenge will be to design scalable methods for large information networks. Hence, we will explore how sampling and randomization can be used in new machine learning algorithms. Also, active machine learning algorithms for graphs will be investigated.

On the first hand we want to design machine learning algorithms on graphs to solve problems in networks of texts and documents in natural language. The main originality of this research is to consider and take advantage of the setting of networked data exploiting the relationships between different data entities and, overall, the graph topology. On the second hand, in a concomitant way, we want to develop prediction models for graph-like data. This includes prediction, ranking and classification of links and nodes in an on-line or batch setting. The two objectives are intertwined, enrich each other and raise important scientific questions we want to focus on. Our research proposal is organized according to the following questions:

1. How to go beyond vectorial classification models in natural language oriented tasks?

2. How to adaptively build graphs with respect to the given tasks? How to create network from observations of information diffusion processes?

3. How to design methods able to achieve very good predictive accuracy without giving up on scalability?

4. How to go beyond strict node homophilic/similarity assumptions in graph-based learning methods?