The modern science of networks has brought significant advances to our understanding of
complex systems. One of the most relevant features of graphs representing real systems is …

Measuring distances between pairs of points on a 3D surface is a fundamental problem in
computer graphics and geometric processing. For most applications, the important …

This paper presents a survey as well as an empirical comparison and evaluation of seven
kernels on graphs and two related similarity matrices, that we globally refer to as “kernels on …

There is a dire need to rebuild existing infrastructure with strategic and efficient methods.
Design-build (DB) becomes a potential solution that provides fast-tracked delivery as a more …

This supplement is devoted to the proof of our main results: Theorems 2 and 3 of the main
paper. For convenience, we formulate all proofs in terms of the effective resistance between …

This work introduces a new family of link-based dissimilarity measures between nodes of a
weighted directed graph. This measure, called the randomized shortest-path (RSP) …

As a graph data mining task, graph classification has high academic value and wide
practical application. Among them, the graph neural network-based method is one of the …

Similarity-based embedding methods have introduced a new perspective on graph
embedding by conforming the similarity distribution of latent vectors in the embedding space …

This work addresses the problem of detecting clusters in a weighted, undirected, graph by
using kernel-based clustering methods, directly partitioning the graph according to a well …

We present GEGNN, a learning-based method for computing the approximate geodesic
distance between two arbitrary points on discrete polyhedra surfaces with constant time …