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mistyreed63849 / Graph-LLM

GraphLLM: Boosting Graph Reasoning Ability of Large Language Model
https://arxiv.org/abs/2310.05845
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Question about the Meaning of edge_index for Beginners #3

Closed Xueziq closed 10 months ago

Xueziq commented 10 months ago

Hello there,

I am a beginner, and I've been exploring your project recently. I came across the usage of edge_index in the code, but its meaning is not entirely clear to me.

Could you please provide a simple explanation of what edge_index is and its specific purpose in the project? For other datasets, how should I construct the appropriate edge_index? Are there any suggestions or example code that I can refer to? Thank you for your patience in addressing my questions. I hope to better understand and contribute to this project.

Thank you!

mistyreed63849 commented 10 months ago

Hello @Xueziq, Great thanks for your interest in our project!

In Pytorch Geometric, edge_index is a tensor with a shape of (2, num_edges), and each column in the tensor represents a directed edge. For instance,

>>> edge_index
tensor([[0, 1, 2],
        [1, 2, 0]])

here edge_index means that node 0 connects to node 1, node 1 connects to node 2, and node 2 connects to node 0.

edge_index plays a crucial role in PyTorch Geometric, serving as a representation for graph edges and being involved in various graph computations. Many convolutional layers and aggregation operators take edge_index as a part of input. You can refer to PyG document to see more examples about how edge_index is used.

In my experience, it's not difficult to construct edge_index from scratch (eg, from a list and then convert it into a tensor) as its definition. And there are also functions in PyG that turn other formats of edge representation into the format of edge_index, like dense_to_sparse.

I hope the answer can address your question and please feel free to ask more.

Xueziq commented 10 months ago

Hi @mistyreed63849, I wanted to express my sincere gratitude for your assistance and guidance regarding PyTorch Geometric (PyG). Prior to reaching out, I had limited knowledge about PyG, and your insightful responses have been invaluable in helping me understand its intricacies. I hold your project in the highest regard, not only for its innovative ideas and elegant code but also for the supportive community that contributes to its success. Your dedication to advancing the field and fostering a collaborative environment is commendable.