The requirement for appropriate ways to measure the similarity between data objects is a
common but vital task in various domains, such as data mining, machine learning and so on …

Graph similarity learning, which measures the similarities between a pair of graph-structured
objects, lies at the core of various machine learning tasks such as graph classification …

Recent efforts adopt interaction-based models to construct the interaction of words between
sentences, which aim to predict whether two sentences are semantically equivalent or not in …

In recent years, multivariate time-series classification (MTSC) has attracted considerable
attention owing to the advancement of sensing technology. Existing deep-learning-based …

Agglomeration-based strategies are important both within adaptive refinement algorithms
and to construct scalable multilevel algebraic solvers. In order to automatically perform …

Graph Neural Networks (GNNs) have demonstrated outstanding results in many graph-
based deep-learning tasks. However, training GNNs on a large graph can be difficult due to …

Graph similarity measurement is a fundamental task in various graph-related applications.
However, recent learning-based approaches lack interpretability as they directly transform …

Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables
diverse transcriptional programs to arise from a single genetic blueprint, whereas its …

Multivariate time series forecasting, which analyzes historical time series to predict future
trends, can effectively help decision-making. Complex relations among variables in MTS …

Graph neural networks (GNNs) have shown great success in various applications. As real-
world graphs are large, training GNNs in distributed systems is desirable. In current training …