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CoDCL: Counterfactual Data Augmentation Contrastive Learning for Continuous-Time Dynamic Network Link Prediction

Hantong Feng, Yonggang Wu, Duxin Chen, Wenwu Yu

TL;DR

The paper tackles continuous-time dynamic network link prediction, where temporal evolution and shifting interaction patterns challenge generalization. It introduces CoDCL, a plug-and-play framework that fuses counterfactual data augmentation with contrastive learning, driven by dynamic treatment variables and a neighborhood-based counterfactual link completion strategy. Key contributions include a formal dynamic interaction indicator, a nearest-neighbor k-hop counterfactual sampling method, and a dual-objective loss (factual prediction plus counterfactual contrastive learning) that yield state-of-the-art performance across multiple real-world datasets and backbones. The results demonstrate robust generalization to temporal changes and unseen nodes, highlighting the practical impact of integrating counterfactual reasoning into temporal representation learning.

Abstract

The rapid growth and continuous structural evolution of dynamic networks make effective predictions increasingly challenging. To enable prediction models to adapt to complex temporal environments, they need to be robust to emerging structural changes. We propose a dynamic network learning framework CoDCL, which combines counterfactual data augmentation with contrastive learning to address this deficiency.Furthermore, we devise a comprehensive strategy to generate high-quality counterfactual data, combining a dynamic treatments design with efficient structural neighborhood exploration to quantify the temporal changes in interaction patterns.Crucially, the entire CoDCL is designed as a plug-and-play universal module that can be seamlessly integrated into various existing temporal graph models without requiring architectural modifications.Extensive experiments on multiple real-world datasets demonstrate that CoDCL significantly gains state-of-the-art baseline models in the field of dynamic networks, confirming the critical role of integrating counterfactual data augmentation into dynamic representation learning.

CoDCL: Counterfactual Data Augmentation Contrastive Learning for Continuous-Time Dynamic Network Link Prediction

TL;DR

The paper tackles continuous-time dynamic network link prediction, where temporal evolution and shifting interaction patterns challenge generalization. It introduces CoDCL, a plug-and-play framework that fuses counterfactual data augmentation with contrastive learning, driven by dynamic treatment variables and a neighborhood-based counterfactual link completion strategy. Key contributions include a formal dynamic interaction indicator, a nearest-neighbor k-hop counterfactual sampling method, and a dual-objective loss (factual prediction plus counterfactual contrastive learning) that yield state-of-the-art performance across multiple real-world datasets and backbones. The results demonstrate robust generalization to temporal changes and unseen nodes, highlighting the practical impact of integrating counterfactual reasoning into temporal representation learning.

Abstract

The rapid growth and continuous structural evolution of dynamic networks make effective predictions increasingly challenging. To enable prediction models to adapt to complex temporal environments, they need to be robust to emerging structural changes. We propose a dynamic network learning framework CoDCL, which combines counterfactual data augmentation with contrastive learning to address this deficiency.Furthermore, we devise a comprehensive strategy to generate high-quality counterfactual data, combining a dynamic treatments design with efficient structural neighborhood exploration to quantify the temporal changes in interaction patterns.Crucially, the entire CoDCL is designed as a plug-and-play universal module that can be seamlessly integrated into various existing temporal graph models without requiring architectural modifications.Extensive experiments on multiple real-world datasets demonstrate that CoDCL significantly gains state-of-the-art baseline models in the field of dynamic networks, confirming the critical role of integrating counterfactual data augmentation into dynamic representation learning.
Paper Structure (21 sections, 14 equations, 5 figures, 4 tables)

This paper contains 21 sections, 14 equations, 5 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminary
  4. Proposed Method
  5. Counterfactual Treatment Variables
  6. Dynamic Interaction Indicator
  7. Binary Treatment
  8. Counterfactual Link Completion Strategy
  9. Nearest neighbor breadth search
  10. Counterfactual Pair Selection
  11. Counterfactual Contrastive Learning
  12. Model Architecture
  13. Loss Function
  14. Algorithm Complexity
  15. Experimental Design and Evaluation
  16. ...and 6 more sections

Figures (5)

  • Figure 1: A simple toy example of counterfactual-enhanced link prediction. Causal modeling: Given $Z$ and observed outcomes, find treatment effect of $T$ on $Y$ (Left); Counterfactual model: leverage the estimated treatment effect $(A_{i,j} |T_{i,j})$ to improve the learning of $z_i$ and $z_j$ (Right).
  • Figure 2: The framework of the proposed counterfactual data augmentation dynamic network contrastive learning.
  • Figure 3: Ablation study results under both transductive and inductive settings by removing: counterfactual learning (w/o CL), temporal encoding (w/o TE), contrastive learning (w/o Contrast), and similarity constraints (w/o Similarity). (a) Wikipedia, (b) UCI, (c) Enron, (d) Reddit.
  • Figure 4: Results of hyper-parameters sensitivity study in dynamic link prediction experiments across different datasets. $p$: (a) Wikipedia, (b) UCI, (c) Enron, (d) Reddit.
  • Figure 5: Results of hyper-parameters sensitivity study in dynamic link prediction experiments across different datasets. $k_{\max}$: (a) Wikipedia, (b) UCI, (c) Enron, (d) Reddit.