GIDN: A Lightweight Graph Inception Diffusion Network for High-efficient Link Prediction

Zixiao Wang, Yuluo Guo, Jin Zhao, Yu Zhang, Hui Yu, Xiaofei Liao, Hai Jin, Biao Wang, Ting Yu

Main figure for 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.

Proposed method: GIDN

Benchmark Results

Tested on 1 dataset

Tables show presented results in global context (best 3 papers + GCN + MLP).

OGB

#ModelHits@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)