Generalization of Probabilistic Latent Semantic Analysis to k-partite Graphs

Yohann Salomon, Pietro Pinoli

2022

Abstract

Many data can be easily modelled as bipartite or k-partite graphs. Among the many computational analyses that can be run on such graphs, link prediction, i.e., the inference of novel links between nodes, is one of the most valuable and has many applications on real world data. While for bipartite graphs many methods exist for this task, only few algorithms are able to perform link prediction on k-partite graphs. The Probabilistic Latent Semantic Analysis (PLSA) is an algorithm based on latent variables, named topics, designed to perform matrix factorisation. As such, it is straightforward to apply PLSA to the task of link prediction on bipartite graphs, simply by decomposing the association matrix. In this work we extend PLSA to k-partite graphs; in particular we designed an algorithm able to perform link prediction on k-partite graphs, by exploiting the information in all the layers of the target graph. Our experiments confirm the capability of the proposed method to effectively perform link prediction on k-partite graphs.

Paper Citation

in Harvard Style

Salomon Y. and Pinoli P. (2022). Generalization of Probabilistic Latent Semantic Analysis to k-partite Graphs. In Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR; ISBN 978-989-758-614-9, SciTePress, pages 127-137. DOI: 10.5220/0011586600003335

in Bibtex Style

@conference{kdir22,
author={Yohann Salomon and Pietro Pinoli},
title={Generalization of Probabilistic Latent Semantic Analysis to k-partite Graphs},
booktitle={Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR},
year={2022},
pages={127-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011586600003335},
isbn={978-989-758-614-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2022) - Volume 1: KDIR
TI - Generalization of Probabilistic Latent Semantic Analysis to k-partite Graphs
SN - 978-989-758-614-9
AU - Salomon Y.
AU - Pinoli P.
PY - 2022
SP - 127
EP - 137
DO - 10.5220/0011586600003335
PB - SciTePress