vgatm-main.zip (31.74 MB)

Data and code for: Variational Graph Author Topic Modeling

dataset

posted on 2022-10-25, 04:48 authored by ZHANG, CE (SMU)ZHANG, CE (SMU), Hady Wirawan LAUWHady Wirawan LAUW

This is the tensorflow implementation of KDD-2022 paper "Variational Graph Author Topic Modeling" by Delvin Ce Zhang and Hady W. Lauw.

VGATM is a Graph Neural Network model that extracts interpretable topics for documents with authors and venues. Topics of documents then fulfill document classification, citation prediction, etc.

History

References

https://github.com/cezhang01/vgatm

Confidential or personally identifiable information

I confirm that the uploaded data has no confidential or personally identifiable information.

Usage metrics

Read the peer-reviewed publication

Variational Graph Author Topic Modeling

School of Computing and Information Systems

Keywords

topic modeling text mining graph neural networks variational graph auto-encoder

Licence

CC BY 4.0