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

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

AccessFree to access
LicenceLicense statement is still being checked
ProducerQiang Huang

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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

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 and licence

Licence: License statement is still being checked · Source page
Rights details not yet verified | 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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Same domain

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