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Programming and Computer Science
Understanding Graph Neural Networks (GNNs) Tutorials & Examples
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[QUOTE="Borg, post: 6597390, member: 185214"] I've been reading various articles on Graph Neural Networks (GNNs) for the last month or so. In particular, I have been focusing on the tutorials in [URL='https://docs.dgl.ai/tutorials/blitz/index.html']A Blitz Introduction to DGL[/URL]. I made it through all of the tutorials OK but I'm having trouble understanding certain aspects of GNNs. I'm also trying to look at some of the examples in the GitHub repository here - [URL]https://github.com/dmlc/dgl/tree/master/examples[/URL] but there a lot of examples to go through. I have a dataset with lots of potential fields that I could use but I am not sure what path to start down. From what I understand, GNN nodes and edges can be defined by multi-dimensional arrays. I could break my data up like that but I've no idea what would make the most sense to put in the nodes vs. the edges. I need a better understanding of when I should use node classification, link prediction or graph classification. From what I understand of it, it seems that you could just rearrange the data for a node classification problem and turn it into a link prediction problem. Also, the first tutorial uses a single graph for the Cora dataset with thousands of nodes. Later in the Graph Classification tutorial it has multiple graphs with just a few nodes in each. Then, when I get to the [URL='https://docs.dgl.ai/tutorials/blitz/6_load_data.html']Make Your Own Dataset[/URL] section, the data that they create doesn't make much sense to me. It seems that I can just manipulate my data into whatever type of problem that I want and just solve it accordingly but I'm not sure . It may be that I just haven't found a good tutorial to understand GNNs well enough. I could really use some help in going beyond the basic examples. [HEADING=2][/HEADING] [/QUOTE]
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Understanding Graph Neural Networks (GNNs) Tutorials & Examples
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