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TFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Prediction

Hantong Feng, Yonggang Wu, Duxin Chen, Wenwu Yu

TL;DR

The superior performance of TFWaveFormer validates the effectiveness of combining temporal-frequency analysis with wavelet decomposition in capturing complex temporal dynamics for dynamic link prediction tasks.

Abstract

Dynamic link prediction plays a crucial role in diverse applications including social network analysis, communication forecasting, and financial modeling. While recent Transformer-based approaches have demonstrated promising results in temporal graph learning, their performance remains limited when capturing complex multi-scale temporal dynamics. In this paper, we propose TFWaveFormer, a novel Transformer architecture that integrates temporal-frequency analysis with multi-resolution wavelet decomposition to enhance dynamic link prediction. Our framework comprises three key components: (i) a temporal-frequency coordination mechanism that jointly models temporal and spectral representations, (ii) a learnable multi-resolution wavelet decomposition module that adaptively extracts multi-scale temporal patterns through parallel convolutions, replacing traditional iterative wavelet transforms, and (iii) a hybrid Transformer module that effectively fuses local wavelet features with global temporal dependencies. Extensive experiments on benchmark datasets demonstrate that TFWaveFormer achieves state-of-the-art performance, outperforming existing Transformer-based and hybrid models by significant margins across multiple metrics. The superior performance of TFWaveFormer validates the effectiveness of combining temporal-frequency analysis with wavelet decomposition in capturing complex temporal dynamics for dynamic link prediction tasks.

TFWaveFormer: Temporal-Frequency Collaborative Multi-level Wavelet Transformer for Dynamic Link Prediction

TL;DR

The superior performance of TFWaveFormer validates the effectiveness of combining temporal-frequency analysis with wavelet decomposition in capturing complex temporal dynamics for dynamic link prediction tasks.

Abstract

Dynamic link prediction plays a crucial role in diverse applications including social network analysis, communication forecasting, and financial modeling. While recent Transformer-based approaches have demonstrated promising results in temporal graph learning, their performance remains limited when capturing complex multi-scale temporal dynamics. In this paper, we propose TFWaveFormer, a novel Transformer architecture that integrates temporal-frequency analysis with multi-resolution wavelet decomposition to enhance dynamic link prediction. Our framework comprises three key components: (i) a temporal-frequency coordination mechanism that jointly models temporal and spectral representations, (ii) a learnable multi-resolution wavelet decomposition module that adaptively extracts multi-scale temporal patterns through parallel convolutions, replacing traditional iterative wavelet transforms, and (iii) a hybrid Transformer module that effectively fuses local wavelet features with global temporal dependencies. Extensive experiments on benchmark datasets demonstrate that TFWaveFormer achieves state-of-the-art performance, outperforming existing Transformer-based and hybrid models by significant margins across multiple metrics. The superior performance of TFWaveFormer validates the effectiveness of combining temporal-frequency analysis with wavelet decomposition in capturing complex temporal dynamics for dynamic link prediction tasks.
Paper Structure (22 sections, 22 equations, 5 figures, 8 tables, 1 algorithm)

This paper contains 22 sections, 22 equations, 5 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Preliminary
  4. METHODOLOGY
  5. Feature Extraction
  6. Multi-Level Wavelet
  7. Decomposition:
  8. Parallel Representation:
  9. Reconstruction:
  10. Temporal-Frequency Hybrid Transformer
  11. Dynamic Link Prediction
  12. Experimental Design and Evaluation
  13. Datasets and benchmarks
  14. Performance Comparison Analysis
  15. Ablation analysis
  16. ...and 7 more sections

Figures (5)

  • Figure 1: The proposed TFWaveFormer framework. The architecture consists of three key components: (1) feature integration, (2) multi-level wavelet transformation, and (3) temporal-frequency hybrid transformer for generating final representations.
  • Figure 2: Comparison performance and training time per epoch on Wikipedia and Reddit.(a) Wikipedia, (b) Reddit.
  • Figure 3: Results of hyper-parameters sensitivity to the number of wavelet convolution kernels $m$ in dynamic link prediction experiments across different datasets. (a) Wikipedia, (b) Reddit, (c) MOOC, (d) LastFM, (e) Enron, (f) Social Evolution, (g) UCI, (h) Flights.
  • Figure 4: Results on ablation study for dynamic link prediction Under differrent settings. (a) Transductive setting, (b) Inductive setting.
  • Figure 5: Results of hyper-parameters sensitivity study to the number of wavelet convolution kernels $m$ in dynamic link prediction experiments across different datasets. (a) UN Trade, (b) Contact.