The Multiscale Laplacian Graph Kernel
Abstract
Many real world graphs, such as the graphs of molecules, exhibit structure at multiple different scales, but most existing kernels between graphs are either purely local or purely global in character. In contrast, by building a hierarchy of nested subgraphs, the Multiscale Laplacian Graph kernels (MLG kernels) that we define in this paper can account for structure at a range of different scales. At the heart of the MLG construction is another new graph kernel, called the Feature Space Laplacian Graph kernel (FLG kernel), which has the property that it can lift a base kernel defined on the vertices of two graphs to a kernel between the graphs. The MLG kernel applies such FLG kernels to subgraphs recursively. To make the MLG kernel computationally feasible, we also introduce a randomized projection procedure, similar to the Nystro m method, but for RKHS operators.
Benchmark Results
Tested on 3 datasets 2 baseline values reported too low
Tables show presented results in global context (best 3 papers + GCN + MLP).
TU Dortmund
| # | Model | Accuracy ↑ | Paper |
|---|---|---|---|
| 1 | MSH-GNN | 0.9910
±0.0030 | |
| 2 | ID-GNN+BEC | 0.9830
±0.0360 | |
| 3 | SupCosine | 0.9830
±0.0250 | |
| 39 | GIN | 0.9330
±0.0290 | |
| 159 | WL | 0.8450
±0.0216 | prev best: Propagation Kernels (2014) Improvement within reported uncertainty; not highlighted. |
| 332 | MLG | 0.8794
±0.0161 | This paper |
| 403 | MLP | 0.8660
±0.0495 | |
| 426 | SP | 0.8580
±0.0020 best reported | best at pub: Propagation Kernels (2014) Difference within reported uncertainty; not counted as underperformance. |
| 526 | WL-Edge | 0.8294
±0.0233 | This paper |
| 550 | Graphlet | 0.8244
±0.0129 | This paper |
| 611 | p--RW | 0.8033
±0.0135 | This paper |
| # | Model | Accuracy ↑ | Paper |
|---|---|---|---|
| 1 | WL-MLP | 0.9954 | |
| 2 | GIN | 0.9934 | |
| 3 | GCN | 0.9727 | |
| 12 | WL | 0.8590
±0.0010 best reported | best at pub: Propagation Kernels (2014) |
| 27 | WL-Edge | 0.8465
±0.0025 | This paper |
| 131 | GraphSAGE | 0.8200
±0.0100 | |
| 145 | MLG | 0.8175
±0.0024 | This paper |
| 421 | SP | 0.7450
±0.0030 best reported | best at pub: Propagation Kernels (2014) best today: On Valid Optimal Assignment Kernels and Applications to Graph Classification (NeurIPS 2016) |
| 533 | Graphlet | 0.6430
±0.0220 best reported | best today: GraphCrop: Subgraph Cropping for Graph Classification (2020) Difference within reported uncertainty; not counted as underperformance. |
| # | Model | Accuracy ↑ | Paper |
|---|---|---|---|
| 1 | GMN | 0.7866 | |
| 2 | MemGNN | 0.7550 | |
| 3 | TFGW SP | 0.7510
±0.0500 | |
| 17 | GatedGCN | 0.7000
±0.0494 | |
| 18 | WL | 0.6950
±0.0320 best reported | best today: Template based Graph Neural Network with Optimal Transport Distances (NeurIPS 2022) |
| 26 | GraphSAGE | 0.6817
±0.0545 | |
| 76 | MLG | 0.6181
±0.0099 | This paper |
| 124 | MLP | 0.5583
±0.0352 | |
| 128 | Graphlet | 0.5530
±0.0378 best reported | best today: Contrastive Graph Few-Shot Learning (2022) |
| 151 | WL-Edge | 0.5200
±0.0072 | This paper |
| 179 | SP | 0.4260
±0.0160 best reported | best today: On Valid Optimal Assignment Kernels and Applications to Graph Classification (NeurIPS 2016) Difference within reported uncertainty; not counted as underperformance. |
| 240 | p--RW | 0.2817
±0.0076 | This paper |
