GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction
Abstract
In this paper, we propose a Graph Inception Diffusion Networks(GIDN) model. This model generalizes graph diffusion in different feature spaces, and uses the inception module to avoid the large amount of computations caused by complex network structures. We evaluate GIDN model on Open Graph Benchmark(OGB) datasets, reached an 11% higher performance than AGDN on ogbl-collab dataset.
Benchmark Results
Tested on 1 dataset
Tables show presented results in global context (best 3 papers + GCN + MLP).
OGB
| # | Model | Hits@50 ↑ | Paper |
|---|---|---|---|
| 1 | PROXI | 0.7650
±0.0027 | |
| 2 | OGB Leader | 0.7129
±0.0018 | |
| 3 | GIDN | 0.7096
±0.0055 | This paper |
| 4 | PLNLP | 0.7059
±0.0029 | This paper |
| 9 | GCN | 0.6621
±0.0033 | |
| 90 | AGDN | 0.4480
±0.0542 | This paper |
| 94 | MLP | 0.4438
±0.0347 |
SOTA performance (not significantly different from the best)
Proposed method (this paper)
Baseline / comparison (this paper)
Global context (other papers)
