mlsanityData to 7 Oct 2026

iN2V: Bringing Transductive Node Embeddings to Inductive Graphs

arXiv 2506.05039 ↗First posted 5 Jun 2025cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Shallow node embeddings like node2vec (N2V) can be used for nodes without features or to supplement existing features with structure-based information. Embedding methods like N2V are limited in their application on new nodes, which restricts them to the transductive setting where the entire graph, including the test nodes, is available during training. We propose inductive node2vec (iN2V), which combines a post-hoc procedure to compute embeddings for nodes unseen during training and modifications to the original N2V training procedure to prepare the embeddings for this post-hoc procedure. We conduct experiments on several benchmark datasets and demonstrate that iN2V is an effective approach to bringing transductive embeddings to an inductive setting. Using iN2V embeddings improves node classification by 1 point on average, with up to 6 points of improvement depending on the dataset and the number of unseen nodes. Our iN2V is a plug-in approach to create new or enrich existing embeddings. It can also be combined with other embedding methods, making it a versatile approach for inductive node representation learning. Code to reproduce the results is available at https://github.com/Foisunt/iN2V .

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 50 references that are in the map. First posted on arXiv 5 Jun 2025.

Builds on

36 of 50 references.

References · 36

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2018How Powerful are Graph Neural Networks?cited by 4,355
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  7. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  8. 2019Simplifying Graph Convolutional Networkscited by 1,470
  9. 2018Pitfalls of Graph Neural Network Evaluationcited by 994
  10. 2016Complex Embeddings for Simple Link Predictioncited by 897
  11. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  12. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
24 more
  1. 2020Simple and Deep Graph Convolutional Networkscited by 857
  2. 2020Geom-GCN: Geometric Graph Convolutional Networkscited by 821
  3. 2014Distributed Representations of Sentences and Documentscited by 677
  4. 2020Adaptive Universal Generalized PageRank Graph Neural Networkcited by 592
  5. 2019MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixingcited by 442
  6. 2023A critical look at the evaluation of GNNs under heterophily: Are we really making progress?cited by 332
  7. 2021Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methodscited by 309
  8. 2020Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networkscited by 253
  9. 2017graph2vec: Learning Distributed Representations of Graphscited by 228
  10. 2017struc2vec: Learning Node Representations from Structural Identitycited by 214
  11. 2022Revisiting Heterophily For Graph Neural Networkscited by 169
  12. 2021Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillationcited by 151
  13. 2018Canonical Tensor Decomposition for Knowledge Base Completioncited by 150
  14. 2017Learning Structural Node Embeddings Via Diffusion Waveletscited by 97
  15. 2021Graph-MLP: Node Classification without Message Passing in Graphcited by 73
  16. 2021On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Featurescited by 62
  17. 2022Characterizing Graph Datasets for Node Classification: Homophily-Heterophily Dichotomy and Beyondcited by 60
  18. 2023Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunitiescited by 60
  19. 2020FedE: Embedding Knowledge Graphs in Federated Settingcited by 46
  20. 2016subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphscited by 19
  21. 2021A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinationscited by 18
  22. 2022ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspectivecited by 12
  23. 2024Edge-Splitting MLP: Node Classification on Homophilic and Heterophilic Graphs without Message Passingcited by 2
  24. 2024HyperAggregation: Aggregating over Graph Edges with Hypernetworkscited by 1

Built on by

Not cited yet.