mlsanityData to 7 Oct 2026

GLINKX: A Scalable Unified Framework For Homophilous and Heterophilous Graphs

arXiv 2211.00550 ↗First posted 1 Nov 2022cs.LG cs.SIcited by 0

Not on the map. Why?

Abstract (arXiv)

In graph learning, there have been two predominant inductive biases regarding graph-inspired architectures: On the one hand, higher-order interactions and message passing work well on homophilous graphs and are leveraged by GCNs and GATs. Such architectures, however, cannot easily scale to large real-world graphs. On the other hand, shallow (or node-level) models using ego features and adjacency embeddings work well in heterophilous graphs. In this work, we propose a novel scalable shallow method -- GLINKX -- that can work both on homophilous and heterophilous graphs. GLINKX leverages (i) novel monophilous label propagations, (ii) ego/node features, (iii) knowledge graph embeddings as positional embeddings, (iv) node-level training, and (v) low-dimensional message passing. Formally, we prove novel error bounds and justify the components of GLINKX. Experimentally, we show its effectiveness on several homophilous and heterophilous datasets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 38 of its 54 references that are in the map. First posted on arXiv 1 Nov 2022.

Builds on

38 of 54 references.

References · 38

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2018How Powerful are Graph Neural Networks?cited by 4,355
  4. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  5. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  6. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  7. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  8. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  9. 2018Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networkscited by 878
  10. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 821
  11. 2020On the Bottleneck of Graph Neural Networks and its Practical Implicationscited by 624
  12. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 592
26 more
  1. 2019Multi-scale Attributed Node Embeddingcited by 543
  2. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  3. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
  4. 2017Residual Gated Graph ConvNetscited by 341
  5. 2018Invariant and Equivariant Graph Networkscited by 339
  6. 2021Graph Neural Networks with Learnable Structural and Positional Representationscited by 326
  7. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  8. 2020SIGN: Scalable Inception Graph Neural Networkscited by 283
  9. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 248
  10. 2013Fast Computation of Wasserstein Barycenterscited by 216
  11. 2020Graph Neural Networks with Heterophilycited by 202
  12. 2020Combining Label Propagation and Simple Models Out-performs Graph Neural Networkscited by 199
  13. 2022Pure Transformers are Powerful Graph Learnerscited by 188
  14. 2022Graph Neural Networks for Graphs with Heterophily: A Surveycited by 175
  15. 2021Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillationcited by 151
  16. 2021Graph Condensation for Graph Neural Networkscited by 150
  17. 2019PyTorch-BigGraph: A Large-scale Graph Embedding Systemcited by 118
  18. 2021Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification?cited by 82
  19. 2022Graph Attention Multi-Layer Perceptroncited by 82
  20. 2021Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoodscited by 57
  21. 2021Scalable and Adaptive Graph Neural Networks with Self-Label-Enhanced trainingcited by 54
  22. 2021Improving Graph Neural Networks with Simple Architecture Designcited by 41
  23. 2022EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networkscited by 26
  24. 2022TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendationcited by 25
  25. 2022RAW-GNN: RAndom Walk Aggregation based Graph Neural Networkcited by 25
  26. 2022Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagationcited by 7

Built on by

Not cited yet.