ML Research Wiki / Benchmarks / Node Classification / PPI

PPI

Node Classification Benchmark

Performance Over Time

📊 Showing 23 results | 📏 Metric: F1

Top Performing Models

Rank	Model	Paper	F1	Date	Code
1	GCNII*	Simple and Deep Graph Convolutional Networks	99.56	2020-07-04	📦 chennnM/GCNII 📦 chennnM/GCNII 📦 zhanglab-aim/cancer-net 📦 tyxxzjpdez/GCNII-DropGroups
2	GraphSAINT	GraphSAINT: Graph Sampling Based Inductive Learning Method	99.50	2019-07-10	📦 dmlc/dgl 📦 GraphSAINT/GraphSAINT 📦 thudm/graphmae2
3	SGAS	SGAS: Sequential Greedy Architecture Search	99.46	2019-11-30	📦 lightaime/sgas
4	DenseMRGCN-14	DeepGCNs: Making GCNs Go as Deep as CNNs	99.43	2019-10-15	📦 lightaime/deep_gcns_torch 📦 lightaime/deep_gcns_torch 📦 lightaime/deep_gcns_torch 📦 lightaime/deep_gcns
5	ResMRGCN-28	DeepGCNs: Making GCNs Go as Deep as CNNs	99.41	2019-10-15	📦 lightaime/deep_gcns_torch 📦 lightaime/deep_gcns_torch 📦 lightaime/deep_gcns_torch 📦 lightaime/deep_gcns
6	GraphStar	Graph Star Net for Generalized Multi-Task Learning	99.40	2019-06-21	📦 graph-star-team/graph_star
7	Cluster-GCN	Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks	99.36	2019-05-20	📦 google-research/google-research 📦 dmlc/dgl 📦 benedekrozemberczki/ClusterGCN
8	GaAN	GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs	98.70	2018-03-20	📦 jennyzhang0215/GaAN
9	JK-LSTM	Representation Learning on Graphs with Jumping Knowledge Networks	97.60	2018-06-09	📦 dmlc/dgl 📦 shinkyuy/representation_learning_on_graphs_with_jumping_knowledge_networks 📦 mori97/JKNet-dgl 📦 xnuohz/jknet-dgl 📦 willy-b/tiny-GIN-for-ogbg-molhiv
10	VQ-GNN (GAT)	VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization	97.37	2021-10-27	📦 devnkong/VQ-GNN

All Papers (23)

Simple and Deep Graph Convolutional Networks

2020

GCNII*

chennnM/GCNII chennnM/GCNII

GraphSAINT: Graph Sampling Based Inductive Learning Method

2019

GraphSAINT

dmlc/dgl GraphSAINT/GraphSAINT

SGAS: Sequential Greedy Architecture Search

2019

SGAS

lightaime/sgas

DeepGCNs: Making GCNs Go as Deep as CNNs

2019

DenseMRGCN-14

lightaime/deep_gcns_torch lightaime/deep_gcns_torch

DeepGCNs: Making GCNs Go as Deep as CNNs

2019

ResMRGCN-28

lightaime/deep_gcns_torch lightaime/deep_gcns_torch

Graph Star Net for Generalized Multi-Task Learning

2019

GraphStar

graph-star-team/graph_star

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

2019

Cluster-GCN

google-research/google-research dmlc/dgl

GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

2018

GaAN

jennyzhang0215/GaAN

Representation Learning on Graphs with Jumping Knowledge Networks

2018

JK-LSTM

dmlc/dgl shinkyuy/representation_learning_on_graphs_with_jumping_knowledge_networks

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

2021

VQ-GNN (GAT)

devnkong/VQ-GNN

Graph Attention Networks

2017

GAT

labmlai/annotated_deep_learning_paper_implementations dmlc/dgl

SIGN: Scalable Inception Graph Neural Networks

2020

SIGN

dmlc/dgl twitter-research/sign

Graph Representation Learning Beyond Node and Homophily

2022

PairE

syvail/PairE-Graph-Representation-Learning-Beyond-Node-and-Homophily

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

2019

ClusterGCN

google-research/google-research dmlc/dgl

Large-Scale Learnable Graph Convolutional Networks

2018

LGCN

divelab/lgcn

Deep Graph Contrastive Representation Learning

2020

GRACE

dmlc/dgl CRIPAC-DIG/GRACE ycremar/DIG-SSL

Inductive Representation Learning on Large Graphs

2017

GraphSAGE

pyg-team/pytorch_geometric dmlc/dgl

node2vec: Scalable Feature Learning for Networks

2016

node2vec

dmlc/dgl shenweichen/GraphEmbedding

node2vec: Scalable Feature Learning for Networks

2016

DeepWalk

dmlc/dgl shenweichen/GraphEmbedding

The Split Matters: Flat Minima Methods for Improving the Performance of GNNs

2023

GCN + SAF

foisunt/fmms-in-gnns

The Split Matters: Flat Minima Methods for Improving the Performance of GNNs

2023

GAT + PGN

foisunt/fmms-in-gnns

Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks

2020

DSGCN

balcilar/Spectral-Designed-Graph-Convolutions sidneyarcidiacono/UnderstandingGCNs

GraphNAS: Graph Neural Architecture Search with Reinforcement Learning

2019

GraphNAS

GraphNAS/GraphNAS-simple

PPI

Performance Over Time

Edit Benchmark Results

Edit Result

Top Performing Models

All Papers (23)

Simple and Deep Graph Convolutional Networks

GraphSAINT: Graph Sampling Based Inductive Learning Method

SGAS: Sequential Greedy Architecture Search

DeepGCNs: Making GCNs Go as Deep as CNNs

DeepGCNs: Making GCNs Go as Deep as CNNs

Graph Star Net for Generalized Multi-Task Learning

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs

Representation Learning on Graphs with Jumping Knowledge Networks

VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization

Graph Attention Networks

SIGN: Scalable Inception Graph Neural Networks

Graph Representation Learning Beyond Node and Homophily

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

Large-Scale Learnable Graph Convolutional Networks

Deep Graph Contrastive Representation Learning

Inductive Representation Learning on Large Graphs

node2vec: Scalable Feature Learning for Networks

node2vec: Scalable Feature Learning for Networks

The Split Matters: Flat Minima Methods for Improving the Performance of GNNs

The Split Matters: Flat Minima Methods for Improving the Performance of GNNs

Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks

GraphNAS: Graph Neural Architecture Search with Reinforcement Learning

Model	Paper	F1	Date
GCNII*	Simple and Deep Graph Convolutional Networks	99.56	2020-07-04
GraphSAINT	GraphSAINT: Graph Sampling Based Inductive Learni…	99.50	2019-07-10
SGAS	SGAS: Sequential Greedy Architecture Search	99.46	2019-11-30
DenseMRGCN-14	DeepGCNs: Making GCNs Go as Deep as CNNs	99.43	2019-10-15
ResMRGCN-28	DeepGCNs: Making GCNs Go as Deep as CNNs	99.41	2019-10-15
GraphStar	Graph Star Net for Generalized Multi-Task Learning	99.40	2019-06-21
Cluster-GCN	Cluster-GCN: An Efficient Algorithm for Training …	99.36	2019-05-20
GaAN	GaAN: Gated Attention Networks for Learning on La…	98.70	2018-03-20
JK-LSTM	Representation Learning on Graphs with Jumping Kn…	97.60	2018-06-09
VQ-GNN (GAT)	VQ-GNN: A Universal Framework to Scale up Graph N…	97.37	2021-10-27
GAT	Graph Attention Networks	97.30	2017-10-30
SIGN	SIGN: Scalable Inception Graph Neural Networks	96.50	2020-04-23
PairE	Graph Representation Learning Beyond Node and Hom…	94.83	2022-03-03
ClusterGCN	Cluster-GCN: An Efficient Algorithm for Training …	92.90	2019-05-20
LGCN	Large-Scale Learnable Graph Convolutional Networks	77.20	2018-08-12
GRACE	Deep Graph Contrastive Representation Learning	66.20	2020-06-07
GraphSAGE	Inductive Representation Learning on Large Graphs	61.20	2017-06-07
node2vec	node2vec: Scalable Feature Learning for Networks	0.18	2016-07-03
DeepWalk	node2vec: Scalable Feature Learning for Networks	0.18	2016-07-03
GCN + SAF	The Split Matters: Flat Minima Methods for Improv…		2023-06-15
GAT + PGN	The Split Matters: Flat Minima Methods for Improv…		2023-06-15
DSGCN	Bridging the Gap Between Spectral and Spatial Dom…		2020-03-26
GraphNAS	GraphNAS: Graph Neural Architecture Search with R…		2019-04-22