Identity-aware Graph Neural Networks


  • Jiaxuan You Stanford University
  • Jonathan M Gomes-Selman Stanford University
  • Rex Ying Stanford University
  • Jure Leskovec Stanford University


Graph-based Machine Learning, Graph Mining, Social Network Analysis & Community, Relational Learning, Representation Learning


Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. However, the expressive power of existing GNNs is upper-bounded by the 1-Weisfeiler-Lehman (1-WL) graph isomorphism test, which means GNNs that are not able to predict node clustering coefficients and shortest path distances, and cannot differentiate between different d-regular graphs. Here we develop a class of message passing GNNs, named Identity-aware Graph Neural Networks (ID-GNNs), with greater expressive power than the 1-WL test. ID-GNN offers a minimal but powerful solution to limitations of existing GNNs. ID-GNN extends existing GNN architectures by inductively considering nodes’ identities during message passing. To embed a given node, ID-GNN first extracts the ego network centered at the node, then conducts rounds of heterogeneous message passing, where different sets of parameters are applied to the center node than to other surrounding nodes in the ego network. We further propose a simplified but faster version of ID-GNN that injects node identity information as augmented node features. Alto- gether, both versions of ID-GNN represent general extensions of message passing GNNs, where experiments show that transforming existing GNNs to ID-GNNs yields on average 40% accuracy improvement on challenging node, edge, and graph property prediction tasks; 3% accuracy improvement on node and graph classification benchmarks; and 15% ROC AUC improvement on real-world link prediction tasks. Additionally, ID-GNNs demonstrate improved or comparable performance over other task-specific graph networks.




How to Cite

You, J., Gomes-Selman, J. M., Ying, R., & Leskovec, J. (2021). Identity-aware Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), 10737-10745. Retrieved from



AAAI Technical Track on Machine Learning V