In many domains where data are represented as graphs, learning a similarity metric among
graphs is considered a key problem, which can further facilitate various learning tasks, such …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

In this paper, we propose a flexible notion of characteristic functions defined on graph
vertices to describe the distribution of vertex features at multiple scales. We introduce …

Graphs encode important structural properties of complex systems. Machine learning on
graphs has therefore emerged as an important technique in research and applications. We …

The prevalence of graph structures in real-world scenarios enables important tasks such as
node classification and link prediction. Graphs in many domains follow a long-tailed …

Conventional graph neural networks (GNNs) are often confronted with fairness issues that
may stem from their input, including node attributes and neighbors surrounding a node …

Graph Neural Networks (GNNs) have attracted much attention due to their superior learning
capability. Despite the successful applications of GNNs in many areas, their performance …

We consider the vector embedding problem. We are given a finite set of items, with the goal
of assigning a representative vector to each one, possibly under some constraints (such as …

In recent years, multiplex network embedding has received great attention from researchers.
However, existing multiplex network embedding methods neglect structural role information …

Proximity preserving and structural role-based node embeddings have become a prime
workhorse of applied graph mining. Novel node embedding techniques are often tested on a …