GRAPH DOMAIN ADAPTATION

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

20 shown of 20 papers with code (40 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Adversarial Deep Network Embedding for Cross-network Node Classification
  18 Feb 2020 2 repositories listed Syntology ran 8 of 11 samples · 3 unverified · 11 pointer-only (licence)
  This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations…
• DANE: Domain Adaptive Network Embedding
  3 Jun 2019 2 repositories listed
  Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data.
• BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
  12 Jun 2025 1 repository listed
  Transferring extensive knowledge from relevant social networks has emerged as a promising solution to overcome label scarcity in detecting social bots and other anomalies with GNN-based models.
• GCAL: Adapting Graph Models to Evolving Domain Shifts
  22 May 2025 1 repository listed Syntology ran 7 of 10 samples · 3 unverified · 10 pointer-only (licence)
  This paper addresses the challenge of graph domain adaptation on evolving, multiple out-of-distribution (OOD) graphs.
• PyGDA: A Python Library for Graph Domain Adaptation
  13 Mar 2025 1 repository listed
  As the first comprehensive library in this area, PyGDA covers more than 20 widely used graph domain adaptation methods together with different types of graph datasets.
• Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation
  16 Dec 2024 1 repository listed
  Given the sensitivity of GNNs to local structural features, even slight discrepancies between source and target graphs could lead to significant shifts in node embeddings, thereby reducing the effectiveness of knowledge…
• GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation
  22 Oct 2024 1 repository listed
  To achieve domain alignment, GALA employs a graph diffusion model to reconstruct source-style graphs from target data.
• DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation
  13 Sep 2024 1 repository listed
  To address the issue, we study the problem of active graph domain adaptation, which selects a small quantitative of informative nodes on the target graph for extra annotation.
• Can Modifying Data Address Graph Domain Adaptation?
  27 Jul 2024 1 repository listed
  UGDA aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph.
• Revisiting, Benchmarking and Understanding Unsupervised Graph Domain Adaptation
  9 Jul 2024 1 repository listed Syntology ran 38 of 47 samples · 9 unverified · 21 pointer-only (licence)
  Unsupervised Graph Domain Adaptation (UGDA) involves the transfer of knowledge from a label-rich source graph to an unlabeled target graph under domain discrepancies.
• Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation
  3 Mar 2024 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph.
• Pairwise Alignment Improves Graph Domain Adaptation
  2 Mar 2024 1 repository listed Syntology ran 5 of 5 samples · 0 unverified
  Graph-based methods, pivotal for label inference over interconnected objects in many real-world applications, often encounter generalization challenges, if the graph used for model training differs significantly from…
• Rethinking Propagation for Unsupervised Graph Domain Adaptation
  8 Feb 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified · 3 pointer-only (licence)
  Motivated by our empirical analysis, we reevaluate the role of GNNs in graph domain adaptation and uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains.
• Graph Domain Adaptation: Challenges, Progress and Prospects
  1 Feb 2024 1 repository listed
  To the best of our knowledge, this paper is the first survey for graph domain adaptation.
• Domain-adaptive Message Passing Graph Neural Network
  31 Aug 2023 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently.
• OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning
  21 Jul 2023 1 repository listed Syntology ran 0 of 1 samples · 1 unverified
  Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark for evaluating graph domain adaptation models.
• Structural Re-weighting Improves Graph Domain Adaptation
  5 Jun 2023 1 repository listed Syntology ran 2 of 7 samples · 5 unverified · 7 pointer-only (licence)
  This work examines different impacts of distribution shifts caused by either graph structure or node attributes and identifies a new type of shift, named conditional structure shift (CSS), which current GDA approaches…
• Non-IID Transfer Learning on Graphs
  15 Dec 2022 1 repository listed
  To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph.
• Source Free Unsupervised Graph Domain Adaptation
  2 Dec 2021 1 repository listed Syntology ran 1 of 9 samples · 8 unverified
  To address the non-trivial adaptation challenges in this practical scenario, we propose a model-agnostic algorithm called SOGA for domain adaptation to fully exploit the discriminative ability of the source model while…
• Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
  4 Sep 2019 1 repository listed
  Existing methods for single network learning cannot solve this problem due to the domain shift across networks.

Syntology lines on 10 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.