GraphZoom

GraphZoom is a framework that aims to improve both performance and scalability of graph embedding techniques. As shown in the following figure, GraphZoom consists of 4 kernels: Graph Fusion, Spectral Coarsening, Graph Embedding, and Embedding Refinement. GraphZoom More details are available in our paper: https://openreview.net/forum?id=r1lGO0EKDH

Citation

If you use GraphZoom in your research, please cite our preliminary work published in ICLR'20.

@inproceedings{deng2020graphzoom,
title={GraphZoom: A Multi-level Spectral Approach for Accurate and Scalable Graph Embedding},
author={Chenhui Deng and Zhiqiang Zhao and Yongyu Wang and Zhiru Zhang and Zhuo Feng},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=r1lGO0EKDH}
}

Spectral Coarsening Options

• lamg-based coarsening: This is the spectral coarsening algorithm used in the original paper, but it requires you to download Matlab Compiler Runtime (MCR).
• simple coarsening: This is a simpler spectral coarsening implemented via python and you do not need to download MCR. This algorithm adopts a similar idea to coarsen the graph (spectrum-preserving), while it may compromise the performance compared to lamg-based coarsening (especially for run-time speedup).

Requirements

• Matlab Compiler Runtime (MCR) 2018a(Linux), which is a standalone set of shared libraries that enables the execution of compiled MATLAB applications and does not require license to install (only required if you run lamg-based coarsening).
• python 3.5/3.6/3.7 (We suggest Conda to manage package dependencies.)
• numpy
• networkx
• scipy
• scikit-learn
• gensim, only required by deepwalk, node2vec
• tensorflow, only required by graphsage
• torch, ogb, pytorch_geometric, only required by Open Graph Benchmark (OGB) examples

Installation

• install matlab compiler runtime 2018a(Linux) (only required if you run lamg-based coarsening)

1. wget https://ssd.mathworks.com/supportfiles/downloads/R2018a/deployment_files/R2018a/installers/glnxa64/MCR_R2018a_glnxa64_installer.zip`
2. unzip MCR_R2018a_glnxa64_installer.zip -d YOUR_SAVE_PATH
3. cd YOUR_SAVE_PATH
4. ./install -mode silent -agreeToLicense yes -destinationFolder YOUR_MCR_PATH

• install PyTorch Geometric (only required if you run OGB examples)
• create virtual environment (skip if you do not want)

1. conda create -n graphzoom python=3.6
2. conda activate graphzoom

• install packages for graphzoom

pip install -r requirements.txt

Directory Stucture

GraphZoom/
│   README.md
│   requirements.txt
│   ... 
│
└───graphzoom/
│   │   graphzoom.py
│   │   cora.sh
│   │   ...  
│   │ 
│   └───dataset/
│   │   │    cora
│   │   │    citeseer
│   │   │    pubmed
│   │  
│   └───embed_methods/
│       │    DeepWalk
│       │    node2vec
│       │    GraphSAGE
│ 
└───mat_coarsen/
│   │   make.m
│   │   LamgSetup.m
│   │   ...  
│
└───ogb/
│   │   ...
│   └───ogbn-arxiv/ 
│   │    │   main.py
│   │    │   mlp.py
│   │    │   arxiv.sh   
│   │    │   ...  
│   │    
│   └───ogbn-products/ 
│        │   main.py
│        │   mlp.py
│        │   products.sh  
│        │   ...
│

Usage

Note: If you run lamg-based coarsening, you have to pass the root directory of matlab compiler runtime to the argument--mcr_dir when running graphzoom.py

Example Usage

1. cd graphzoom

2. python graphzoom.py --mcr_dir YOUR_MCR_PATH --dataset citeseer --search_ratio 12 --num_neighs 10 --embed_method deepwalk --coarse lamg

--coarse: choose a specific algorithm for coarsening, [lamg, simple]

--reduce_ratio: the reduction ratio when choosing lamg-based coarsening method

--level: the coarsening level when choosing simple coarsening method

--mcr_dir: root directory of matlab compiler runtime

--dataset: input dataset, currently supports "json" format

--embed_method: choose a specific basic embedding algorithm

--search_ratio: control the search space of graph fusion

--num_neighs: control number of edges in feature graph

Full Command List The full list of command line options is available with python graphzoom.py --help

Highlight in Flexibility

You can easily plug a new unsupervised graph embedding model into GraphZoom, just implement a new function, which takes a graph as input and outputs an embedding matrix, in graphzoom/embed_methods.

The current version of GraphZoom can support the following basic models:

• DeepWalk
• node2vec
• GraphSAGE

Dataset

• Cora
• Citeseer
• Pubmed

You can add your own dataset following the json format in graphzoom/dataset

Experimental Results

Here we evaluate GraphZoom on Cora dataset with DeepWalk as basic embedding model, with lamg-based coarsening method. GraphZoom-i denotes applying GraphZoom with i-th coarsening level.

Method	Accuracy	Speedup	Graph_Size
DeepWalk	71.4	1x	2708
GraphZoom-1	76.9	2.5x	1169
GraphZoom-2	77.3	6.3x	519
GraphZoom-3	75.1	40.8x	218

We also evaluate Graphzoom on ogbn-arxiv and ogbn-products dataset with lamg-based coarsening method, and GraphZoom-1 has better performance and much fewer parameters than the Node2vec baseline.

ogbn-arxiv

Method	Accuracy	#Params
Node2vec	70.07 ± 0.13	21,818,792
GraphZoom-1	71.18 ± 0.18	8,963,624

ogbn-products

Method	Accuracy	#Params
Node2vec	72.49 ± 0.10	313,612,207
GraphZoom-1	74.06 ± 0.26	120,251,183

LAMG Coarsening Code

The matlab version of lamg-based spectral coarsening code is available in mat_coarsen/