Authors:
Yohann Salomon
1
and
Pietro Pinoli
2
Affiliations:
1
ENSTA, Palaiseau, France
;
2
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
Keyword(s):
PLSA, EM-algorithm, k-partite Graphs, Link Prediction.
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 per
form link prediction on k-partite graphs.
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