HGT_SimpleHGN

Hetergenour Graph Transformers

• authors: Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun
• link: https://arxiv.org/abs/2003.01332

Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks

• authors: Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang
• link: https://arxiv.org/abs/2112.14936

---

• file structure:

├── cresci-2015
│   ├── HGT.py  # train HGT on cresci-2015
│   ├── SimpleHGN.py # train SimpleHGN on cresci-2015
│   ├── Dataset.py
│   └── layer.py
├── Twibot-20    
│   ├── HGT.py  # train HGT on Twibot-20
│   ├── SimpleHGN.py # train SimpleHGN on cresci-2015
│   ├── Dataset.py
│   └── layer.py
└── Twibot-22
    ├── HGT_sample.py  # train HGT on Twibot-22
    ├── SimpleHGN_sample.py # train SimpleHGN on Twibot-22
    └── layer.py

• implement details:

1. We just consider the graph with user as node and their follow relations (e.g. follower & following) as edge due to the limitation of computation resources.

2. We fix HGT and SimpleHGN's attention head to 1 for simplicity.

3. To enable the training on large-scale graph such as Twibot-22, we adopt the NeighborLoader in torch geometric with num_neighbors set to 20 and sample for 2 iterations.

4. Input features for user node is taken from BotRGCN, more details please refer to BotRGCN imlementation

• Requirements

CUDA==10.2
pytorch-lightning==1.4.9
torch==1.9.1
torch-geometric==2.0.2
torch-cluster==1.5.9
torch-scatter==2.0.9
torch-sparse==0.6.12
torch-spline-conv==1.2.1
sklearn
argparse

How to reproduce:

Please change the path argument into your own path to preprocess files

• reproduce HGT on cresci-2015 & Twibot-20

CUDA_VISIBLE_DEVICES=0 python HGT.py --batch_size 128 --epochs 200 --path /path/to/preprocess/file

• reproduce HGT on Twibot-22

CUDA_VISIBLE_DEVICES=0 python HGT.py --batch_size 128 --epochs 200 --path /path/to/preprocess/file

• reproduce SimpleHGN on cresci-2015 & Twibot-2015

CUDA_VISIBLE_DEVICES=0 python SimpleHGN.py --batch_size 128 --epochs 200 --path /path/to/preprocess/file

• reproduce SimpleHGN on Twibot-22 dataset

CUDA_VISIBLE_DEVICES=0 python SimpleHGN.py --batch_size 128 --epochs 200 --path /path/to/preprocess/file

Result:

HGT

dataset		acc	precison	recall	f1
Cresci-2015	mean	0.9603	0.9480	0.9911	0.9693
Cresci-2015	std	0.0032	0.0049	0.0012	0.0024
Twibot-20	mean	0.8691	0.8555	0.9100	0.8819
Twibot-20	std	0.0024	0.0031	0.0057	0.0019
Twibot-22	mean	0.7491	0.6822	0.2803	0.3960
Twibot-22	std	0.0013	0.0271	0.0260	0.0211

SimpleHGN

dataset		acc	precison	recall	f1
Cresci-2015	mean	0.9671	0.9568	0.9929	0.9745
Cresci-2015	std	0.0054	0.0090	0.0040	0.0040
Twibot-20	mean	0.8674	0.8476	0.9206	0.8825
Twibot-20	std	0.0022	0.0046	0.0051	0.0018
Twibot-22	mean	0.7672	0.7257	0.3290	0.4544
Twibot-22	std	0.0027	0.0279	0.0164	0.0042

baseline	acc on Twibot-22	f1 on Twibot-22	type	tags
HGT	0.7491	0.3960	F T G	Graph Neural Networks
SimpleHGN	0.7672	0.4544	F T G	Graph Neural Networks