Recent advancements in experimental high-throughput technologies have expanded the
availability and quantity of molecular data in biology. Given the importance of interactions in …

With the impressive growth of available data and the flexibility of network modelling, the
problem of devising effective quantitative methods for the comparison of networks arises …

Most graph kernels are an instance of the class of R-Convolution kernels, which measure
the similarity of objects by comparing their substructures. Despite their empirical success …

The problem of accurately measuring the similarity between graphs is at the core of many
applications in a variety of disciplines. Graph kernels have recently emerged as a promising …

Negative pairs, especially hard negatives as combined with common negatives (easy to
discriminate), are essential in contrastive learning, which plays a role of avoiding …

We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry.
Gaussian processes have long been a cornerstone of probabilistic machine learning …

Graph-structured data are an integral part of many application domains, including
chemoinformatics, computational biology, neuroimaging, and social network analysis. Over …

Today, there is an increasing concern about malicious attacks on various networks in society
and industry, against which the network robustness is critical. Network connectivity …

The default removal of low-abundance (rare) taxa from microbial community analyses may
lead to an incomplete picture of the taxonomic and functional microbial potential within the …

Techniques from computational topology, in particular persistent homology, are becoming
increasingly relevant for data analysis. Their stable metrics permit the use of many distance …