As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …

The quantum approximate optimization algorithm (QAOA) is one of the most promising
candidates for achieving quantum advantage through quantum-enhanced combinatorial …

With the rapid emergence of graph representation learning, the construction of new large-
scale datasets is necessary to distinguish model capabilities and accurately assess the …

What is the value of an individual model in an ensemble of binary classifiers? We answer
this question by introducing a class of transferable utility cooperative games called …

We propose the molecular omics network (MOOMIN) a multimodal graph neural network
used by AstraZeneca oncologists to predict the synergy of drug combinations for cancer …

Over the past few years, skeleton-based action recognition has attracted great success
because the skeleton data is immune to illumination variation, view-point variation …

The ever-increasing importance of structured data in different applications, especially in the
biomedical field, has driven the need for reducing its complexity through projections into a …

The study of vertex centrality measures is a key aspect of network analysis. Naturally, such
centrality measures have been generalized to groups of vertices; for popular measures it …

Networks provide a suitable model for many scientific and technological problems that
require the representation of complex entities and their relations. Life sciences applications …

Learning the problem structure at multiple levels of coarseness to inform the decomposition-
based hybrid quantum-classical combinatorial optimization solvers is a promising approach …