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Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"

Name: Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
Creator: Qiang Huang

BenchTemp contains 19 temporal graph datasets for evaluating temporal graph neural networks, each with temporal graph CSV, edge features, and node features files.

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Machine learning & corporaFree to access

Key facts

InstitutionQiang Huang

Coverage19 temporal graph datasets covering social networks, e-commerce, transportation, etc.

Time spanSee official page

Scale17 files

LicenseLicense statement is still being checked

AccessZenodo

Contents & fields

Each dataset consists of three files: ml_{data_name}.csv (temporal graph CSV), ml_{data_name}.npy (edge features), and ml_{data_name}_node.npy (node features). The CSV file contains 5 columns:

u — user ID
i — item ID
ts — timestamp of interaction
label — label of interaction
idx — index of interaction

Research uses

Suitable for benchmarking and performance evaluation of temporal graph neural networks, supporting research in dynamic graph representation learning and temporal link prediction.

Information comes from the source page. Please check that page for current details and terms.

Keywords

temporal graphgraph neural networkbenchmark datasetdynamic graphBenchTemp

Access & license

License: License statement is still being checked | Free to access

Official site / download →

Why this is hard to get on your own

Lack of unified benchmark datasets for evaluating temporal graph neural networks; BenchTemp provides 19 standardized datasets for fair comparison.

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