[HTML][HTML] A survey on graph kernels
Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …
classification tasks on graphs. This survey gives a comprehensive overview of techniques …
Random walk graph neural networks
G Nikolentzos, M Vazirgiannis - Advances in Neural …, 2020 - proceedings.neurips.cc
In recent years, graph neural networks (GNNs) have become the de facto tool for performing
machine learning tasks on graphs. Most GNNs belong to the family of message passing …
machine learning tasks on graphs. Most GNNs belong to the family of message passing …
Matching node embeddings for graph similarity
G Nikolentzos, P Meladianos… - Proceedings of the AAAI …, 2017 - ojs.aaai.org
Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph
kernels focus on local properties of graphs and ignore global structure. In this paper, we …
kernels focus on local properties of graphs and ignore global structure. In this paper, we …
Lovász principle for unsupervised graph representation learning
This paper focuses on graph-level representation learning that aims to represent graphs as
vectors that can be directly utilized in downstream tasks such as graph classification. We …
vectors that can be directly utilized in downstream tasks such as graph classification. We …
Graph kernels: A survey
Graph kernels have attracted a lot of attention during the last decade, and have evolved into
a rapidly developing branch of learning on structured data. During the past 20 years, the …
a rapidly developing branch of learning on structured data. During the past 20 years, the …
Glocalized weisfeiler-lehman graph kernels: Global-local feature maps of graphs
Most state-of-the-art graph kernels only take local graph properties into account, ie, the
kernel is computed with regard to properties of the neighborhood of vertices or other small …
kernel is computed with regard to properties of the neighborhood of vertices or other small …
A large-scale database for graph representation learning
With the rapid emergence of graph representation learning, the construction of new large-
scale datasets is necessary to distinguish model capabilities and accurately assess the …
scale datasets is necessary to distinguish model capabilities and accurately assess the …
Sub-network kernels for measuring similarity of brain connectivity networks in disease diagnosis
As a simple representation of interactions among distributed brain regions, brain networks
have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's …
have been widely applied to automated diagnosis of brain diseases, such as Alzheimer's …
[PDF][PDF] A Degeneracy Framework for Graph Similarity.
The problem of accurately measuring the similarity between graphs is at the core of many
applications in a variety of disciplines. Most existing methods for graph similarity focus either …
applications in a variety of disciplines. Most existing methods for graph similarity focus either …
A survey of graph comparison methods with applications to nondeterminism in high-performance computing
S Bhowmick, P Bell, M Taufer - The International Journal of …, 2023 - journals.sagepub.com
The convergence of extremely high levels of hardware concurrency and the effective overlap
of computation and communication in asynchronous executions has resulted in increasing …
of computation and communication in asynchronous executions has resulted in increasing …