mlsanityData to 7 Oct 2026

Network Representation Learning: From Traditional Feature Learning to Deep Learning

arXiv 2103.04339 ↗First posted 7 Mar 2021cs.SI cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been successfully applied in many real-world tasks related to network science, such as social network data processing, biological information processing, and recommender systems. Deep Learning is a powerful tool to learn data features. However, it is non-trivial to generalize deep learning to graph-structured data since it is different from the regular data such as pictures having spatial information and sounds having temporal information. Recently, researchers proposed many deep learning-based methods in the area of NRL. In this survey, we investigate classical NRL from traditional feature learning method to the deep learning-based model, analyze relationships between them, and summarize the latest progress. Finally, we discuss open issues considering NRL and point out the future directions in this field.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 65 of its 143 references that are in the map. First posted on arXiv 7 Mar 2021.

Builds on

65 of 143 references.

References · 65

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  4. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  5. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  8. 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
  9. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  10. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  11. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  12. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
38 more
  1. 2016Revisiting Semi-Supervised Learning with Graph Embeddingscited by 970
  2. 2016Complex Embeddings for Simple Link Predictioncited by 897
  3. 2015Gated Graph Sequence Neural Networkscited by 792
  4. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  5. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  6. 2019Heterogeneous Graph Attention Networkcited by 732
  7. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  8. 2015LINE: Large-scale Information Network Embeddingcited by 582
  9. 2019DeepGCNs: Can GCNs Go as Deep as CNNs?cited by 527
  10. 2017Representation Learning on Graphs: Methods and Applicationscited by 456
  11. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  12. 2019Self-Attention Graph Poolingcited by 421
  13. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 395
  14. 2018Deep Learning on Graphs: A Surveycited by 393
  15. 2016Learning Convolutional Neural Networks for Graphscited by 314
  16. 2016Structured Sequence Modeling with Graph Convolutional Recurrent Networkscited by 273
  17. 2015A Review of Relational Machine Learning for Knowledge Graphscited by 264
  18. 2017Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2veccited by 197
  19. 2017Graph Embedding Techniques, Applications, and Performance: A Surveycited by 188
  20. 2017A Survey on Network Embeddingcited by 142
  21. 2017Predicting multicellular function through multi-layer tissue networkscited by 138
  22. 2017Heterogeneous Information Network Embedding for Recommendationcited by 133
  23. 2017GraphGAN: Graph Representation Learning with Generative Adversarial Netscited by 121
  24. 2018Graphite: Iterative Generative Modeling of Graphscited by 112
  25. 2014word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding methodcited by 109
  26. 2018Adversarial Attacks on Node Embeddings via Graph Poisoningcited by 109
  27. 2019A Gentle Introduction to Deep Learning for Graphscited by 107
  28. 2017Network Representation Learning: A Surveycited by 101
  29. 2019Graph Adversarial Training: Dynamically Regularizing Based on Graph Structurecited by 93
  30. 2018Adversarially Regularized Graph Autoencoder for Graph Embeddingcited by 84
  31. 2019Hierarchical Graph Pooling with Structure Learningcited by 81
  32. 2017Knowledge Graph Completion via Complex Tensor Factorizationcited by 80
  33. 2017Neural Embeddings of Graphs in Hyperbolic Spacecited by 58
  34. 2014word2vec Parameter Learning Explainedcited by 53
  35. 2018Logic Attention Based Neighborhood Aggregation for Inductive Knowledge Graph Embeddingcited by 53
  36. 2020Random Walks: A Review of Algorithms and Applicationscited by 38
  37. 2018Spectral Inference Networks: Unifying Deep and Spectral Learningcited by 34
  38. 2017Adversarial Network Embeddingcited by 25

50 most cited of 65 All 65 (JSON)

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 2.

3 papers.

  1. 2021Graph Learning: A Surveycited by 140
  2. 2022Physics-Informed Graph Learningcited by 2
  3. 2022TPM: Transition Probability Matrix -- Graph Structural Feature based Embeddingcited by 1