Multi-Label Network Classification via Weighted Personalized Factorizations

Ahmed Rashed, Josif Grabocka, Lars Schmidt-Thieme

2019

Abstract

Multi-label network classification is a well-known task that is being used in a wide variety of web-based and non-web-based domains. It can be formalized as a multi-relational learning task for predicting nodes labels based on their relations within the network. In sparse networks, this prediction task can be very challenging when only implicit feedback information is available such as in predicting user interests in social networks. Current approaches rely on learning per-node latent representations by utilizing the network structure, however, implicit feedback relations are naturally sparse and contain only positive observed feedbacks which mean that these approaches will treat all observed relations as equally important. This is not necessarily the case in real-world scenarios as implicit relations might have semantic weights which reflect the strength of those relations. If those weights can be approximated, the models can be trained to differentiate between strong and weak relations. In this paper, we propose a weighted personalized two-stage multi-relational matrix factorization model with Bayesian personalized ranking loss for network classification that utilizes basic transitive node similarity function for weighting implicit feedback relations. Experiments show that the proposed model significantly outperforms the state-of-art models on three different real-world web-based datasets and a biology-based dataset.

Paper Citation

in Harvard Style

Rashed A., Grabocka J. and Schmidt-Thieme L. (2019). Multi-Label Network Classification via Weighted Personalized Factorizations.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 357-366. DOI: 10.5220/0007681203570366

in Bibtex Style

@conference{icaart19,
author={Ahmed Rashed and Josif Grabocka and Lars Schmidt-Thieme},
title={Multi-Label Network Classification via Weighted Personalized Factorizations},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={357-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007681203570366},
isbn={978-989-758-350-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Multi-Label Network Classification via Weighted Personalized Factorizations
SN - 978-989-758-350-6
AU - Rashed A.
AU - Grabocka J.
AU - Schmidt-Thieme L.
PY - 2019
SP - 357
EP - 366
DO - 10.5220/0007681203570366