[ESWA 119775] A novel network core structure extraction algorithm utilized variational autoencoder for community detection
Published: 7 March 2023| Version 1 | DOI: 10.17632/4xr6ftymvp.1
Contributors:
, Description
We use two example datasets, karate and dolphins, where the csv file with "adj" represents the adjacency matrix and the csv file with "real" represents the real community division of each node, which can be directly used as input to the CSEA algorithm.
Files
Steps to reproduce
Using the "adj" and "real" files of the same dataset as input to the CSEA algorithm, follow the steps in https://github.com/PeterWana/CSEA to obtain the algorithm's output results.
Institutions
Xi'an University of Technology
Categories
Machine Learning, Cluster Analysis
Funding
National Natural Science Foundation of China
62120106011
Natural Science Basic Research Program of Shaanxi Province
2021JM-347
Shaanxi Provincial Department of education Special project
No. 21JC026