Efficient Learning-based Community-Preserving Graph Generation
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2022, 2022-May, pp. 1982-1994
- Issue Date:
- 2022
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Efficient_Learning-based_Community-Preserving_Graph_Generation.pdf | Published version | 347.79 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Graph generation is beneficial to comprehend the creation of meaningful structures of networks in a broad spec trum of applications such as social networks and biological net works Recent studies tend to leverage deep learning techniques to learn the topology structures in graphs However we notice that the community structure which is one of the most unique and prominent features of the graph cannot be well captured by the existing graph generators Moreover the existing advanced deep learning based graph generators are not efficient and scalable which can only handle small graphs In this paper we propose a novel community preserving generative adversarial network CPGAN for effective and efficient scalable graph simulation We employ graph convolution networks in the encoder and share parameters in the generation process to transmit information about community structures and preserve the permutation invariance in CPGAN We conducted extensive experiments on benchmark datasets including six sets of real life graphs The results demonstrate that CPGAN can achieve a good trade off between efficiency scalability and graph simulation quality for real life graph simulation compared with state of the art baselines
Please use this identifier to cite or link to this item: