# Graph-Relational Domain Adaptation

arXiv: https://arxiv.org/abs/2202.03628 · HTML: https://mlsanity.com/paper/2202.03628/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 42 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Maximum Classifier Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1712.02560/) (2017)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Bridging Theory and Algorithm for Domain Adaptation](https://mlsanity.com/paper/1904.05801/) (2019)
- [Generate To Adapt: Aligning Domains using Generative Adversarial Networks](https://mlsanity.com/paper/1704.01705/) (2017)
- [A Large-Scale Car Dataset for Fine-Grained Categorization and Verification](https://mlsanity.com/paper/1506.08959/) (2015)
- [SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2012.11460/) (2020)
- [Continuously Indexed Domain Adaptation](https://mlsanity.com/paper/2007.01807/) (2020)
- [Unsupervised Multi-Target Domain Adaptation Through Knowledge Distillation](https://mlsanity.com/paper/2007.07077/) (2020)
- [Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks](https://mlsanity.com/paper/1907.03389/) (2019)
- [AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs](https://mlsanity.com/paper/1903.07062/) (2019)
- [Domain Adaptation with Adversarial Training and Graph Embeddings](https://mlsanity.com/paper/1805.05151/) (2018)
- [Domain Adaptation on Graphs by Learning Aligned Graph Bases](https://mlsanity.com/paper/1803.05288/) (2018)
- [Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2007.09257/) (2020)
- [Unsupervised Domain Adaptation Based on Source-guided Discrepancy](https://mlsanity.com/paper/1809.03839/) (2018)
- [Adaptation Across Extreme Variations using Unlabeled Domain Bridges](https://mlsanity.com/paper/1906.02238/) (2019)

## Built on by (most cited first, 19 of 19)

- [BLoB: Bayesian Low-Rank Adaptation by Backpropagation for Large Language Models](https://mlsanity.com/paper/2406.11675/) (2024)
- [A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm](https://mlsanity.com/paper/2310.12244/) (2023)
- [Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations](https://mlsanity.com/paper/2401.14142/) (2024)
- [On Calibration of LLM-based Guard Models for Reliable Content Moderation](https://mlsanity.com/paper/2410.10414/) (2024)
- [Domain-Indexing Variational Bayes: Interpretable Domain Index for Domain Adaptation](https://mlsanity.com/paper/2302.02561/) (2023)
- [On the Benefits of Attribute-Driven Graph Domain Adaptation](https://mlsanity.com/paper/2502.06808/) (2025)
- [Improving Domain Generalization with Domain Relations](https://mlsanity.com/paper/2302.02609/) (2023)
- [Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation](https://mlsanity.com/paper/2306.10534/) (2023)
- [Homophily Enhanced Graph Domain Adaptation](https://mlsanity.com/paper/2505.20089/) (2025)
- [Topology-aware Robust Optimization for Out-of-distribution Generalization](https://mlsanity.com/paper/2307.13943/) (2023)
- [Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients](https://mlsanity.com/paper/2407.04949/) (2024)
- [Taxonomy-Structured Domain Adaptation](https://mlsanity.com/paper/2306.07874/) (2023)
- [Continuous Invariance Learning](https://mlsanity.com/paper/2310.05348/) (2023)
- [Sheaf HyperNetworks for Personalized Federated Learning](https://mlsanity.com/paper/2405.20882/) (2024)
- [Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy](https://mlsanity.com/paper/2309.07402/) (2023)
- [Concept-Based Unsupervised Domain Adaptation](https://mlsanity.com/paper/2505.05195/) (2025)
- [Towards Domain Adaptive Neural Contextual Bandits](https://mlsanity.com/paper/2406.09564/) (2024)
- [Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation](https://mlsanity.com/paper/2209.10168/) (2022)
- [Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexing](https://mlsanity.com/paper/2405.16233/) (2024)

## Abstract (arXiv)

Existing domain adaptation methods tend to treat every domain equally and align them all perfectly. Such uniform alignment ignores topological structures among different domains; therefore it may be beneficial for nearby domains, but not necessarily for distant domains. In this work, we relax such uniform alignment by using a domain graph to encode domain adjacency, e.g., a graph of states in the US with each state as a domain and each edge indicating adjacency, thereby allowing domains to align flexibly based on the graph structure. We generalize the existing adversarial learning framework with a novel graph discriminator using encoding-conditioned graph embeddings. Theoretical analysis shows that at equilibrium, our method recovers classic domain adaptation when the graph is a clique, and achieves non-trivial alignment for other types of graphs. Empirical results show that our approach successfully generalizes uniform alignment, naturally incorporates domain information represented by graphs, and improves upon existing domain adaptation methods on both synthetic and real-world datasets. Code will soon be available at https://github.com/Wang-ML-Lab/GRDA.