cszhangzhen / mvpool

Hierarchical Multi-View Graph Pooling with Structure Learning (TKDE-2021)

MVPool

Hierarchical Multi-View Graph Pooling with Structure Learning (paper).

This is a PyTorch implementation of the MVPool algorithm, which is accepted by TKDE. The proposed MVPool conducts pooling operation via mulit-view information. Then, a structure learning layer is stacked on the pooling operation, which aims to learn a refined graph structure that can best preserve the essential topological information. It's a general operator that can be used in various architectures, including node-level representation learning and graph-level representation learning.

Requirements

• python3.6
• pytorch==1.3.0
• torch-scatter==1.4.0
• torch-sparse==0.4.3
• torch-cluster==1.4.5
• torch-geometric==1.3.2

Note: An older version of torch-sparse is needed, lower than 0.4.4. This code repository is heavily built on pytorch_geometric, which is a Geometric Deep Learning Extension Library for PyTorch. Please refer here for how to install and utilize the library.

Node Classification Datasets

The input contains:

• x, the feature vectors of the labeled training instances
• y, the one-hot labels of the labeled training instances
• allx, the feature vectors of both labeled and unlabeled training instances (a superset of x)
• graph, a dict in the format {index: [index_of_neighbor_nodes]}.

Let n be the number of both labeled and unlabeled training instances. These n instances should be indexed from 0 to n - 1 in graph with the same order as in allx.

In addition to x, y, allx, and graph as described above, the preprocessed datasets also include:

• tx, the feature vectors of the test instances
• ty, the one-hot labels of the test instances
• test.index, the indices of test instances in graph, for the inductive setting
• ally, the labels for instances in allx.

The indices of test instances in graph for the transductive setting are from #x to #x + #tx - 1, with the same order as in tx.

You can use cPickle.load(open(filename)) to load the numpy/scipy objects x, y, tx, ty, allx, ally, and graph. test.index is stored as a text file. More details can be found at here.

Node Classification

Just execuate the following command for node classification task:

python main_node_classification.py

Parameter settings for node classification

Datasets	lr	weight_decay	batch_size	pool_ratio	lambda	net_layers
Cora	0.01	0.01	Full	0.5/0.5/0.8/0.5	0.9	4
Citeseer	0.005	0.1	Full	0.7	0.0	1
Pubmed	0.01	0.001	Full	0.05/0.6/0.5/0.9	1.0	4
CS	0.01	0.01	Full	0.05/0.5/0.5/0.5	0.0	4
Physics	0.01	0.01	Full	0.05/0.8/0.8/0.8	0.0	4

Graph Classification Datasets

Graph classification benchmarks are publicly available at here.

This folder contains the following comma separated text files (replace DS by the name of the dataset):

n = total number of nodes

m = total number of edges

N = number of graphs

(1) DS_A.txt (m lines)

sparse (block diagonal) adjacency matrix for all graphs, each line corresponds to (row, col) resp. (node_id, node_id)

(2) DS_graph_indicator.txt (n lines)

column vector of graph identifiers for all nodes of all graphs, the value in the i-th line is the graph_id of the node with node_id i

(3) DS_graph_labels.txt (N lines)

class labels for all graphs in the dataset, the value in the i-th line is the class label of the graph with graph_id i

(4) DS_node_labels.txt (n lines)

column vector of node labels, the value in the i-th line corresponds to the node with node_id i

There are OPTIONAL files if the respective information is available:

(5) DS_edge_labels.txt (m lines; same size as DS_A_sparse.txt)

labels for the edges in DS_A_sparse.txt

(6) DS_edge_attributes.txt (m lines; same size as DS_A.txt)

attributes for the edges in DS_A.txt

(7) DS_node_attributes.txt (n lines)

matrix of node attributes, the comma seperated values in the i-th line is the attribute vector of the node with node_id i

(8) DS_graph_attributes.txt (N lines)

regression values for all graphs in the dataset, the value in the i-th line is the attribute of the graph with graph_id i

Run Graph Classification

Just execuate the following command for graph classification task:

python main_graph_classification.py

Citing

If you find MVPool useful for your research, please consider citing the following paper:

@article{zhang2021hierarchical,
  title={Hierarchical Multi-View Graph Pooling with Structure Learning},
  author={Zhang, Zhen and Bu, Jiajun and Ester, Martin and Zhang, Jianfeng and Li, Zhao and Yao, Chengwei and Huifen, Dai and Yu, Zhi and Wang, Can},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2021},
  publisher={IEEE}
}