LFR benchmark graphs for "Comparison of deep and shallow graph representation learning algorithms for detecting modules in molecular networks" pape
In recent years, deep learning has made a significant impact in various fields – helping to push the...
Heterogeneous knowledge graphs are emerging as an abstraction to represent complex data, such as soc...
Network representation learning (NRL) is an effective graph analytics technique and promotes users t...
Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algori...
Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algori...
Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algori...
Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algori...
Datasets used for "Benchmarking graph representation learning algorithms for detecting modules in mo...
The lengthy and expensive process of developing new medicines is a driving force in the development ...
Graphs, a natural and generic data structure, can be seen as the backbone of numerous systems becaus...
We consider feature representation learning problem of molecular graphs. Graph Neural Networks have ...
We present a flexible deep convolutional neural network method for the analysis of arbitrary sized g...
We present a flexible deep convolutional neural network method for the analysis of arbitrary sized g...
International audienceIn recent years, deep neural networks (DNNs) have known an important rise in p...
This electronic version was submitted by the student author. The certified thesis is available in th...
In recent years, deep learning has made a significant impact in various fields – helping to push the...
Heterogeneous knowledge graphs are emerging as an abstraction to represent complex data, such as soc...
Network representation learning (NRL) is an effective graph analytics technique and promotes users t...
Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algori...
Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algori...
Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algori...
Source Code and Supplementary Materials for Paper "Benchmarking graph representation learning algori...
Datasets used for "Benchmarking graph representation learning algorithms for detecting modules in mo...
The lengthy and expensive process of developing new medicines is a driving force in the development ...
Graphs, a natural and generic data structure, can be seen as the backbone of numerous systems becaus...
We consider feature representation learning problem of molecular graphs. Graph Neural Networks have ...
We present a flexible deep convolutional neural network method for the analysis of arbitrary sized g...
We present a flexible deep convolutional neural network method for the analysis of arbitrary sized g...
International audienceIn recent years, deep neural networks (DNNs) have known an important rise in p...
This electronic version was submitted by the student author. The certified thesis is available in th...
In recent years, deep learning has made a significant impact in various fields – helping to push the...
Heterogeneous knowledge graphs are emerging as an abstraction to represent complex data, such as soc...
Network representation learning (NRL) is an effective graph analytics technique and promotes users t...
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