NeurIPS 2016

The Multiscale Laplacian Graph Kernel

R. Kondor, Horace Pan

Main figure for 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.

Proposed method: MLG

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

#ModelAccuracy ↑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
actual #159 vs paper #466 307 places worse
0.8450 ±0.0216
actual #159 vs paper #466 307 places worse
paper 0.8450 ±0.0216
prev best: Propagation Kernels (2014)

Improvement within reported uncertainty; not highlighted.

paper 0.8450 ±0.0216 Δ at pub 0.0050
332
MLG
0.8794 ±0.0161
This paper
403
MLP
0.8660 ±0.0495
426
SP
actual #426 vs paper #437 11 places worse
0.8580 ±0.0020
best reported
actual #426 vs paper #437 11 places worse
paper 0.8550 ±0.0250
best at pub: Propagation Kernels (2014)

Difference within reported uncertainty; not counted as underperformance.

paper 0.8550 ±0.0250 Δ at pub 0.0030
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
#ModelAccuracy ↑Paper
1
WL-MLP
0.9954
2
GIN
0.9934
3
GCN
0.9727
12
WL
actual #12 vs paper #26 14 places worse
0.8590 ±0.0010
best reported
actual #12 vs paper #26 14 places worse
paper 0.8476 ±0.0032
best at pub: Propagation Kernels (2014)
paper 0.8476 ±0.0032 Δ at pub 0.0114
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
actual #421 vs paper #442 21 places worse
0.7450 ±0.0030
best reported
actual #421 vs paper #442 21 places worse
paper 0.7361 ±0.0036
best at pub: Propagation Kernels (2014)
paper 0.7361 ±0.0036 Δ at pub 0.0079 Δ today 0.0089
533
Graphlet
actual #533 vs paper #542 9 places worse
0.6430 ±0.0220
best reported
actual #533 vs paper #542 9 places worse
paper 0.6240 ±0.0027

Difference within reported uncertainty; not counted as underperformance.

paper 0.6240 ±0.0027 Δ today 0.0190
#ModelAccuracy ↑Paper
1
GMN
0.7866
2
MemGNN
0.7550
3
TFGW SP
0.7510 ±0.0500
17
GatedGCN
0.7000 ±0.0494
18
WL
actual #18 vs paper #141 123 places worse
0.6950 ±0.0320
best reported
actual #18 vs paper #141 123 places worse
paper 0.5375 ±0.0137
paper 0.5375 ±0.0137 Δ today 0.1575
26
GraphSAGE
0.6817 ±0.0545
76
MLG
0.6181 ±0.0099
This paper
124
MLP
0.5583 ±0.0352
128
Graphlet
actual #128 vs paper #303 175 places worse
0.5530 ±0.0378
best reported
actual #128 vs paper #303 175 places worse
paper 0.1095 ±0.0069
paper 0.1095 ±0.0069 Δ today 0.4435
151
WL-Edge
0.5200 ±0.0072
This paper
179
SP
actual #179 vs paper #182 3 places worse
0.4260 ±0.0160
best reported
actual #179 vs paper #182 3 places worse
paper 0.4231 ±0.0137

Difference within reported uncertainty; not counted as underperformance.

paper 0.4231 ±0.0137 Δ today 0.0029
240
p--RW
0.2817 ±0.0076
This paper
SOTA performance (not significantly different from the best) Proposed method (this paper) Baseline / comparison (this paper) Global context (other papers) Best at pub — reported result worse than the best known when published (best reported in green) Best today — OK at publication; today's leaderboard best is elsewhere