Papers › Fully-inductive Node Classification on Arbitrary Graphs

Fully-inductive Node Classification on Arbitrary Graphs

30 May 2024arXiv:2405.20445archive 2025-07-28

Jianan Zhao, Zhaocheng Zhu, Mikhail Galkin, Hesham Mostafa, Michael Bronstein, Jian Tang

One fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can generalize to test graphs with new structures, but assuming the feature and label spaces remain the same as the training ones. This paper introduces a fully-inductive setup, where models should perform inference on arbitrary test graphs with new structures, feature and label spaces. We propose GraphAny as the first attempt at this challenging setup. GraphAny models inference on a new graph as an analytical solution to a LinearGNN, which can be naturally applied to graphs with any feature and label spaces. To further build a stronger model with learning capacity, we fuse multiple LinearGNN predictions with learned inductive attention scores. Specifically, the attention module is carefully parameterized as a function of the entropy-normalized distance features between pairs of LinearGNN predictions to ensure generalization to new graphs. Empirically, GraphAny trained on a single Wisconsin dataset with only 120 labeled nodes can generalize to 30 new graphs with an average accuracy of 67.26%, surpassing not only all inductive baselines, but also strong transductive methods trained separately on each of the 30 test graphs.

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generate_unique_id deepgraphlearning/graphany/graphany/utils/experiment.py official repository ran MIT (permissive) · 137836e0a3e43732 · report
get_entropy_normed_cond_gaussian_prob deepgraphlearning/graphany/graphany/data.py official repository ran MIT (permissive) · 14ba19def90c1d4f · report
rename_alias deepgraphlearning/graphany/graphany/utils/config.py official repository ran fingerprinted MIT (permissive) · f49c1f79064000d2 · report
sample_k_nodes_per_label deepgraphlearning/graphany/graphany/data.py official repository ran MIT (permissive) · 22e47b12db07fbcd · report
save_config deepgraphlearning/graphany/graphany/utils/config.py official repository ran MIT (permissive) · c4de46acf34700f5 · report
get_cur_time deepgraphlearning/graphany/graphany/utils/logging.py official repository unverified MIT (permissive) · dfdeecadef37dc2c · report
get_data_split_masks deepgraphlearning/graphany/graphany/data.py official repository unverified MIT (permissive) · 40f63ba517d4f559 · report
init_experiment deepgraphlearning/graphany/graphany/utils/experiment.py official repository unverified MIT (permissive) · 48759ff44d6d216b · report

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