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Add FiGraph dataset #9630
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Add FiGraph dataset #9630
XiaoguangWang23
wants to merge
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commits into
pyg-team:master
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XiaoguangWang23:add-figraph-dataset
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It's not possible for me to merge 50 files and 9000 LOC. Do we need the model implementations for this PR? |
Sorry, there may be some errors here. We will modify the PR following other builtin datasets.
2024-09-03 16:26:59 "Matthias Fey" ***@***.***> 写道:
It's not possible for me to merge 50 files and 9000 LOC. Do we need the model implementations for this PR?
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Overview
This Pull Request (PR) introduces a real-world dataset, FiGraph dataset, to the PyTorch Geometric (PyG) library. FiGraph is a dynamic heterogeneous graph dataset that captures the evolving relationships within financial networks over a span of nine years. This dataset is particularly useful for node classification tasks where both the temporal dynamics and heterogeneous nature of the graph are crucial.
Dataset Details
Dynamic Heterogeneous Graph
FiGraph is structured as a dynamic heterogeneous graph, meaning it not only evolves over time but also contains multiple types of nodes and edges. Each year from 2014 to 2022 is represented as a distinct graph snapshot within the dataset.
Time Span: 2014 to 2022
Graph Snapshots: 9 snapshots, one for each year
Node Types: 5 distinct types of nodes, labeled as:
L
: Listed companiesU
: Unlisted companiesH
: Holding companiesA
: AuditorsR
: Regulatory bodiesEdge Types: 4 types of edges, representing different types of relationships:
Related-party transaction
Investment
Audit
Supply chain
Yearly Snapshots
Each year's data is stored as a separate snapshot, capturing the state of the financial network at that time. The nodes' features and labels, as well as the graph structure, are allowed to change from year to year, making this dataset particularly suitable for studying temporal dynamics in graph-based learning tasks.
L
(Listed companies) have features, which include financial attributes such as profit and liabilities. These features can vary annually.L
type nodes have labels, which indicate whether a company's financial report for that year is fraudulent (Label = 1
) or normal (Label = 0
).Code Structure
torch_geometric/datasets/figraph.py
.torch_geometric/datasets/figraph/data/
.Example Usage
Researchers can load the FiGraph dataset as follows: