Link Prediction via Community Detection in Bipartite Multi-Layer Graphs

International audienceThe growing number of multi-relational networks pose new challenges concerning the development of methods for solving classical graph problems in a multi-layer framework, such as link prediction. In this work, we combine an existing bipartite local models method with approaches for link prediction from communities to address the link prediction problem in multi-layer graphs. To this end, we extend existing community detection-based link prediction measures to the bi-partite multi-layer network setting. We obtain a new generic framework for link prediction in bipartite multi-layer graphs, which can integrate any community detection approach, is capable of handling an arbitrary number of networks, rather inexpensive (depending on the community detection technique), and able to automatically tune its parameters. We test our framework using two of the most common community detection methods, the Louvain algorithm and spectral partitioning, which can be easily applied to bipartite multi-layer graphs. We evaluate our approach on benchmark data sets for solving a common drug-target interaction prediction task in computational drug design and demonstrate experimentally that our approach is competitive with the state-of-the-art

Induction automatique: aspects theoriques, le systeme ARBRE, applications en medecine

SIGLEINIST T 75958 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Extraction et usages de motifs minimaux en fouille de données, contribution au domaine des hypergraphes

CAEN-BU Sciences et STAPS (141182103) / SudocSudocFranceF

Apprentissage supervisé à partir de données séquentielles

CAEN-BU Sciences et STAPS (141182103) / SudocSudocFranceF

Simplest Rules Characterizing Classes Generated by δ-Free Sets

We present a new approach that provides the simplest rules characterizing classes with respect to their left-hand sides. This approach is based on a condensed representation (-free sets) of data which is eciently computed. Produced rules have a minimal body (i.e. any subset of the left-hand side of a rule does not enable to conclude on the same class value). We show a sensible sucient condition that avoids important classi cation conicts. Experiments show that the number of rules characterizing classes drastically decreases. The technique is operational for large data sets and can be used even in the dicult context of highlycorrelated data where other algorithms fail

Minimal Jumping Emerging Patterns: Computation and Practical Assessment

International audienceJumping Emerging Patterns (JEP) are patterns that only occur in objects of a single class, a minimal JEP is a JEP where none of its proper subsets is a JEP. In this paper, an efficient method to mine the whole set of the minimal JEPs is detailed and fully proven. Moreover, our method has a larger scope since it is able to compute the essential JEPs and the top-k minimal JEPs. We also extract minimal JEPs where the absence of attributes is stated, and we show that this leads to the discovery of new valuable pieces of information. A performance study is reported to evaluate our approach and the practical efficiency of minimal JEPs in the design of rules to express correlations is shown

Introduction au numéro thématique double Extraction de motifs dans des bases de données

National audienceIntroduction au numéro thématique double Extraction de motifs dans des bases de donnée

Introduction au numéro thématique double Extraction de motifs dans des bases de données

National audienceIntroduction au numéro thématique double Extraction de motifs dans des bases de donnée

Link Prediction via Community Detection in Bipartite Multi-Layer Graphs

International audienceThe growing number of multi-relational networks pose new challenges concerning the development of methods for solving classical graph problems in a multi-layer framework, such as link prediction. In this work, we combine an existing bipartite local models method with approaches for link prediction from communities to address the link prediction problem in multi-layer graphs. To this end, we extend existing community detection-based link prediction measures to the bi-partite multi-layer network setting. We obtain a new generic framework for link prediction in bipartite multi-layer graphs, which can integrate any community detection approach, is capable of handling an arbitrary number of networks, rather inexpensive (depending on the community detection technique), and able to automatically tune its parameters. We test our framework using two of the most common community detection methods, the Louvain algorithm and spectral partitioning, which can be easily applied to bipartite multi-layer graphs. We evaluate our approach on benchmark data sets for solving a common drug-target interaction prediction task in computational drug design and demonstrate experimentally that our approach is competitive with the state-of-the-art