PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification

Key points

• Its generator draws 100 patterns once and plants the same ones in every split, so each test graph carries a pattern that also appears in 100 training graphs.1
• A matcher that reads these patterns off the training labels and looks them up in each test graph reaches 99.2 class-weighted accuracy with no trained parameters, against 89.8 for the best published model.1
• Inference under the generator without the patterns reaches 86.8.1
• Two published models, CKGConv and GRIT, score lower on patterns they have not seen: on new graphs with fresh patterns both fall below this pattern-blind predictor, and withholding patterns from CKGConv's training lowers its accuracy on the test graphs that carry them.1

Sources (1)

• [1]PATTERN Can Be Solved from Memory: Generator-Aware Baselines for Synthetic Node Classification
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 03:54 PM

  PATTERN, a synthetic node-classification benchmark still used to rank graph networks, plants a random subgraph in a random host graph and asks which nodes belong to it.

  Its generator draws 100 patterns once and plants the same ones in every split, so each test graph carries a pattern that also appears in 100 training graphs.

Extractive summary: sentences quoted from the sources.