Graphs are important data representations for describing objects and their relationships,
which appear in a wide diversity of real-world scenarios. As one of a critical problem in this …

Graph generation has shown great potential in applications like network design and mobility
synthesis and is one of the fastest-growing domains in machine learning for graphs. Despite …

From longitudinal biomedical studies to social networks, graphs have emerged as essential
objects for describing evolving interactions between agents in complex systems. In such …

Quantifying the differences between networks is a challenging and ever-present problem in
network science. In recent years, a multitude of diverse, ad hoc solutions to this problem …

Motivation Single-cell RNA-sequencing enables cell-level investigation of cell differentiation,
which can be modelled using trajectory inference methods. While tremendous effort has …

During the last decade, the evidences on the relationship between neurodevelopmental
disorders and the microbial communities of the intestinal tract have considerably grown …

Technological opportunities are bred in intricate and interactive connections between
science and technology (S&T). To identify these potential opportunities, lexical-or topic …

Over the last two decades, alongside the increased availability of large network datasets, we
have witnessed the rapid rise of network science. For many systems, however, the data we …

Graph, a kind of structured data, is widely used to model complex relationships among
objects, and has been used in various of scientific and engineering fields, such as …

From longitudinal biomedical studies to social networks, graphs have emerged as a
powerful framework for describing evolving interactions between agents in complex …