mlsanityData to 7 Oct 2026

Semi-Supervised Graph-to-Graph Translation

arXiv 2103.08827 ↗First posted 16 Mar 2021cs.LGcited by 18

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Abstract (arXiv)

Graph translation is very promising research direction and has a wide range of potential real-world applications. Graph is a natural structure for representing relationship and interactions, and its translation can encode the intrinsic semantic changes of relationships in different scenarios. However, despite its seemingly wide possibilities, usage of graph translation so far is still quite limited. One important reason is the lack of high-quality paired dataset. For example, we can easily build graphs representing peoples' shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain. Therefore, in this work, we seek to provide a graph translation model in the semi-supervised scenario. This task is non-trivial, because graph translation involves changing the semantics in the form of link topology and node attributes, which is difficult to capture due to the combinatory nature and inter-dependencies. Furthermore, due to the high order of freedom in graph's composition, it is difficult to assure the generalization ability of trained models. These difficulties impose a tighter requirement for the exploitation of unpaired samples. Addressing them, we propose to construct a dual representation space, where transformation is performed explicitly to model the semantic transitions. Special encoder/decoder structures are designed, and auxiliary mutual information loss is also adopted to enforce the alignment of unpaired/paired examples. We evaluate the proposed method in three different datasets.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 31 of its 47 references that are in the map. First posted on arXiv 16 Mar 2021.

Builds on

31 of 47 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,974
  7. 2019Simplifying Graph Convolutional Networkscited by 1,470
  8. 2016Variational Graph Auto-Encoderscited by 1,373
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  10. 2016Temporal Ensembling for Semi-Supervised Learningcited by 1,054
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19 more
  1. 2014Semi-Supervised Learning with Deep Generative Modelscited by 494
  2. 2018GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Modelscited by 406
  3. 2018GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoderscited by 375
  4. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  5. 2019Position-aware Graph Neural Networkscited by 203
  6. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  7. 2018Style Transfer Through Back-Translationcited by 124
  8. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 119
  9. 2020Self-supervised Learning on Graphs: Deep Insights and New Directioncited by 108
  10. 2018Learning Multimodal Graph-to-Graph Translation for Molecular Optimizationcited by 87
  11. 2020A Graph to Graphs Framework for Retrosynthesis Predictioncited by 85
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  13. 2019Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Predictioncited by 64
  14. 2018Graph Transformation Policy Network for Chemical Reaction Predictioncited by 56
  15. 2019Understanding the Representation Power of Graph Neural Networks in Learning Graph Topologycited by 52
  16. 2018Unsupervised Statistical Machine Translationcited by 51
  17. 2015A Simple Baseline for Travel Time Estimation using Large-Scale Trip Datacited by 28
  18. 2020Deep Multi-attributed Graph Translation with Node-Edge Co-evolutioncited by 6
  19. 2020Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networkscited by 1

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Citing papers per year: 2021: 5; 2022: 7; 2023: 4; 2024: 2.

18 papers.

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  1. 2021Towards Self-Explainable Graph Neural Networkcited by 75
  2. 2021NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphscited by 73
  3. 2022Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labelscited by 63
  4. 2023Unnoticeable Backdoor Attacks on Graph Neural Networkscited by 45
  5. 2022Exploring Edge Disentanglement for Node Classificationcited by 23
  6. 2021Label-Wise Graph Convolutional Network for Heterophilic Graphscited by 17
  7. 2022Towards Prototype-Based Self-Explainable Graph Neural Networkcited by 14
  8. 2023A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacycited by 14
  9. 2022Towards Faithful and Consistent Explanations for Graph Neural Networkscited by 13
  10. 2022TopoImb: Toward Topology-level Imbalance in Learning from Graphscited by 12
  11. 2021Stable Prediction on Graphs with Agnostic Distribution Shiftcited by 9
  12. 2022Semi-supervised Drifted Stream Learning with Short Lookbackcited by 7
6 more
  1. 2023Faithful and Consistent Graph Neural Network Explanations with Rationale Alignmentcited by 6
  2. 2024Disambiguated Node Classification with Graph Neural Networkscited by 4
  3. 2022Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifierscited by 3
  4. 2023Graph Relation Aware Continual Learningcited by 3
  5. 2021Weakly Supervised Concept Map Generation through Task-Guided Graph Translationcited by 2
  6. 2024EsaCL: Efficient Continual Learning of Sparse Modelscited by 2