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The Surprising Power of Graph Neural Networks with Random Node Initialization

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arxiv 2010.01179 v2 pith:LR5PKUOC submitted 2020-10-02 cs.LG cs.AIstat.ML

Ralph Abboud , İsmail İlkan Ceylan , Martin Grohe , Thomas Lukasiewicz This is my paper
classification cs.LGcs.AIstat.ML
keywords gnnsnodegraphmodelspoweranalyzeexpressivefeatures
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Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot distinguish graphs beyond the capability of the Weisfeiler-Leman graph isomorphism heuristic. In order to break this expressiveness barrier, GNNs have been enhanced with random node initialization (RNI), where the idea is to train and run the models with randomized initial node features. In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties. This universality result holds even with partially randomized initial node features, and preserves the invariance properties of GNNs in expectation. We then empirically analyze the effect of RNI on GNNs, based on carefully constructed datasets. Our empirical findings support the superior performance of GNNs with RNI over standard GNNs.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining

    cs.LG 2025-09 conditional novelty 7.0 of 10

    A contrast-free self-supervised method pretrains molecular graph encoders by predicting subgraph embeddings from complementary ego-net neighborhoods, integrating 2D and 3D, and beats prior SSL baselines on MoleculeNet.

  2. Bridging Theory and Practice in Link Representation with Graph Neural Networks

    cs.LG 2025-06 reject novelty 7.0 of 10

    A new framework classifies message-passing link representation models by neighborhood radius and base expressiveness, yielding a hierarchy in which SEAL is most expressive, plus a synthetic benchmark and symmetry-base...

  3. Universality and Approximation Rates of Graph Neural Networks with Random Features

    cs.LG 2026-07 accept novelty 6.0 of 10

    PENNs with random node features universally approximate measurable perm-invariant/equivariant graph functions in probability, with explicit approximation rates for C^k targets.

  4. Future Link Prediction Without Memory or Aggregation

    cs.LG 2025-05 conditional novelty 6.0 of 10

    CRAFT replaces memory and aggregation with learnable node embeddings and destination-to-source-neighbor cross-attention, improving future link prediction on most of 17 temporal graph benchmarks.

  5. Heat Kernel Goes Topological

    cs.LG 2025-07 reject novelty 5.0 of 10

    TopoHKS defines a weighted combinatorial-complex Laplacian and heat kernel descriptor, claiming maximal expressive power; the supporting uniqueness theorem is incorrect.

  6. Chordless Structure: A Pathway to Simple and Expressive GNNs

    cs.LG 2025-05 reject novelty 4.0 of 10

    CSGNN encodes chordless cycles and paths as structural features and is claimed to strictly outperform k-hop GNNs in expressiveness with polynomial complexity.

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