Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
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 …
A comprehensive survey of graph embedding: Problems, techniques, and applications
Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …
scenarios. Effective graph analytics provides users a deeper understanding of what is …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Weisfeiler and leman go neural: Higher-order graph neural networks
In recent years, graph neural networks (GNNs) have emerged as a powerful neural
architecture to learn vector representations of nodes and graphs in a supervised, end-to-end …
architecture to learn vector representations of nodes and graphs in a supervised, end-to-end …
Weisfeiler and leman go machine learning: The story so far
In recent years, algorithms and neural architectures based on the Weisfeiler-Leman
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …
algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a …
Weisfeiler and leman go sparse: Towards scalable higher-order graph embeddings
Graph kernels based on the $1 $-dimensional Weisfeiler-Leman algorithm and
corresponding neural architectures recently emerged as powerful tools for (supervised) …
corresponding neural architectures recently emerged as powerful tools for (supervised) …
Matching node embeddings for graph similarity
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 …
On valid optimal assignment kernels and applications to graph classification
The success of kernel methods has initiated the design of novel positive semidefinite
functions, in particular for structured data. A leading design paradigm for this is the …
functions, in particular for structured data. A leading design paradigm for this is the …
The multiscale laplacian graph kernel
Many real world graphs, such as the graphs of molecules, exhibit structure at multiple
different scales, but most existing kernels between graphs are either purely local or purely …
different scales, but most existing kernels between graphs are either purely local or purely …
Speqnets: Sparsity-aware permutation-equivariant graph networks
While message-passing graph neural networks have clear limitations in approximating
permutation-equivariant functions over graphs or general relational data, more expressive …
permutation-equivariant functions over graphs or general relational data, more expressive …