Authors :
Girish L; Raviprakash M L
Volume/Issue :
Volume 8 - 2023, Issue 3 - March
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3JaLgtC
DOI :
https://doi.org/10.5281/zenodo.7735576
Abstract :
Graph neural network are a part of deep
learning methods created to perform presumption on
data described by graphs. Graph neural network is a
neutral network that can straight away be applied to
graphs. It provides a agreeable way for node level,
edge level and graph level prediction tasks. Moreover,
most GNN models do not account for long distance
relationships in graphs and instead simply aggregate
data from short distances (e.g., 1-hop neighbours) in
each round. In this paper work, we carry out node
classification using graphs which can be put into large
graphs comprise of labelled and unlabelled nodes. Here
we can predict the node embeddings of the unlabelled
node by using an approach called message passing. For
executingthis, we took Cora dataset, provided a overview
of it, builded ourgraph neural network, splitted the data
to test and train data, trained it and finally visualised
the output.
I
Graph neural network are a part of deep
learning methods created to perform presumption on
data described by graphs. Graph neural network is a
neutral network that can straight away be applied to
graphs. It provides a agreeable way for node level,
edge level and graph level prediction tasks. Moreover,
most GNN models do not account for long distance
relationships in graphs and instead simply aggregate
data from short distances (e.g., 1-hop neighbours) in
each round. In this paper work, we carry out node
classification using graphs which can be put into large
graphs comprise of labelled and unlabelled nodes. Here
we can predict the node embeddings of the unlabelled
node by using an approach called message passing. For
executingthis, we took Cora dataset, provided a overview
of it, builded ourgraph neural network, splitted the data
to test and train data, trained it and finally visualised
the output.
I
