Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

On the Benefits of Attribute-Driven Graph Domain Adaptation

Ruiyi Fang, Bingheng Li, Zhao Kang, Qiuhao Zeng, Nima Hosseini Dashtbayaz, Ruizhi Pu, Boyu Wang, Charles Ling

TL;DR

This work addresses the challenge of graph domain adaptation by highlighting that node attribute shifts can dominate topology shifts in cross-network tasks. It presents GAA, a cross-channel framework that jointly aligns attribute and topology views through a feature-graph based attribute module, cross-view similarity refinement, and an adversarial training objective with a PAC-Bayes-inspired bound. Theoretical analysis connects the target risk to both topology and attribute divergences, while extensive experiments on diverse datasets show GAA achieving notable improvements over state-of-the-art baselines. The findings suggest that incorporating and aligning node attributes is crucial for robust GDA in real-world graphs and opens avenues for broader cross-network learning tasks.

Abstract

Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Recent studies attempted to learn domain invariant representations by eliminating structural shifts between graphs. In this work, we show that existing methodologies have overlooked the significance of the graph node attribute, a pivotal factor for graph domain alignment. Specifically, we first reveal the impact of node attributes for GDA by theoretically proving that in addition to the graph structural divergence between the domains, the node attribute discrepancy also plays a critical role in GDA. Moreover, we also empirically show that the attribute shift is more substantial than the topology shift, which further underscores the importance of node attribute alignment in GDA. Inspired by this finding, a novel cross-channel module is developed to fuse and align both views between the source and target graphs for GDA. Experimental results on a variety of benchmarks verify the effectiveness of our method.

On the Benefits of Attribute-Driven Graph Domain Adaptation

TL;DR

This work addresses the challenge of graph domain adaptation by highlighting that node attribute shifts can dominate topology shifts in cross-network tasks. It presents GAA, a cross-channel framework that jointly aligns attribute and topology views through a feature-graph based attribute module, cross-view similarity refinement, and an adversarial training objective with a PAC-Bayes-inspired bound. Theoretical analysis connects the target risk to both topology and attribute divergences, while extensive experiments on diverse datasets show GAA achieving notable improvements over state-of-the-art baselines. The findings suggest that incorporating and aligning node attributes is crucial for robust GDA in real-world graphs and opens avenues for broader cross-network learning tasks.

Abstract

Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Recent studies attempted to learn domain invariant representations by eliminating structural shifts between graphs. In this work, we show that existing methodologies have overlooked the significance of the graph node attribute, a pivotal factor for graph domain alignment. Specifically, we first reveal the impact of node attributes for GDA by theoretically proving that in addition to the graph structural divergence between the domains, the node attribute discrepancy also plays a critical role in GDA. Moreover, we also empirically show that the attribute shift is more substantial than the topology shift, which further underscores the importance of node attribute alignment in GDA. Inspired by this finding, a novel cross-channel module is developed to fuse and align both views between the source and target graphs for GDA. Experimental results on a variety of benchmarks verify the effectiveness of our method.

Paper Structure

This paper contains 27 sections, 4 theorems, 28 equations, 7 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Theoretical Analysis
  4. The proposed methodology
  5. Specific Attribute Convolution Module
  6. Source Classifier Loss
  7. Attribute-Driven Alignment
  8. Target Node Classification
  9. EXPERIMENT
  10. Datasets
  11. Baselines
  12. Experimental Setup
  13. Cross-network Node Classification Results
  14. Ablation Study
  15. Parameter Analysis
  16. ...and 12 more sections

Key Result

Theorem 1

Let $\mathcal{H}$ be a family of classification functions. For any classifier $h$ in $\mathcal{H}$, and for any parameters $\lambda > 0$ and $\gamma \geq 0$, consider any prior distribution $P$ over $\mathcal{H}$ that is independent of the training data $\mathcal{V}^S$. With a probability of at leas

Figures (7)

  • Figure 1: This represents feature value in two groups of datasets. This shows the feature value distribution gap in the attribute is larger than in the topology.
  • Figure 2: (a) An overview of our method. GAA gives attribute and topology graph representation, where minimizing source and target distribution shift through two views. (b)(i) Distribution shifts exist in both topology and attribute views before alignment. (ii) Existing GDA algorithms can only address graph topology shifts but not attribute shifts. (iii) GAA can address GDA attribute shifts.
  • Figure 3: The classification accuracy of GAA and its variants on citation datasets and airport dataset.
  • Figure 4: The influence of parameters $\alpha$, $\beta$, $\tau$ and $k$ on Citation and Airport dataset.
  • Figure 5: The influence of parameters $\alpha$, $\beta$, $\tau$ and $k$ on two social datasets.
  • ...and 2 more figures

Theorems & Definitions (8)

  • Definition 1: Expected Loss Discrepancy
  • Theorem 1: Domain Adaptation Bound for Deterministic Classifiers
  • Proposition 1: Bound for $D^{\gamma}_{S, T}(P;\lambda)$
  • Definition 1
  • Theorem 2
  • proof
  • Proposition 2: Bound for $D^{\gamma}_{S, T}(P;\lambda)$
  • proof