Newton’s gravitational law for link prediction in social networks
Authors: Wahid-Ul-Ashraf, A., Budka, M. and Musial-Gabrys, K.
Journal: Studies in Computational Intelligence
Volume: 689
Pages: 93-104
ISBN: 9783319721491
ISSN: 1860-949X
DOI: 10.1007/978-3-319-72150-7_8
Abstract:Link prediction is an important research area in network science due to a wide range of real-world application. There are a number of link prediction methods. In the area of social networks, these methods are mostly inspired by social theory, such as having more mutual friends between two people in a social network platform entails higher probability of those two people becoming friends in the future. In this paper we take our inspiration from a different area, which is Newton’s law of universal gravitation. Although this law deals with physical bodies, based on our intuition and empirical results we found that this could also work in networks, and especially in social networks. In order to apply this law, we had to endow nodes with the notion of mass and distance. While node importance could be considered as mass, the shortest path, path count, or inverse similarity (AdamicAdar, Katz score etc.) could be considered as distance. In our analysis, we have primarily used degree centrality to denote the mass of the nodes, while the lengths of shortest paths between them have been used as distances. In this study we compare the proposed link prediction approach to 7 other methods on 4 datasets from various domains. To this end, we use the ROC curves and the AUC measure to compare the methods. As the results show that our approach outperforms the other 7 methods on 2 out of the 4 datasets, we also discuss the potential reasons of the observed behaviour.
https://eprints.bournemouth.ac.uk/30084/
Source: Scopus
Newton's Gravitational Law for Link Prediction in Social Networks
Authors: Ul-Ashraf, A.W., Budka, M. and Musial-Gabrys, K.
Journal: COMPLEX NETWORKS & THEIR APPLICATIONS VI
Volume: 689
Pages: 93-104
eISSN: 1860-9503
ISBN: 978-3-319-72149-1
ISSN: 1860-949X
DOI: 10.1007/978-3-319-72150-7_8
https://eprints.bournemouth.ac.uk/30084/
Source: Web of Science (Lite)
Newton’s Gravitational Law for Link Prediction in Social Networks
Authors: Wahid-Ul-Ashraf, A., Budka, M. and Musial-Gabrys, K.
Conference: Complex Networks 2017
Dates: 29 November-1 December 2017
https://eprints.bournemouth.ac.uk/30084/
Source: Manual
Newton's Gravitational Law for Link Prediction in Social Networks.
Authors: Wahid-Ul-Ashraf, A., Budka, M. and Musial-Gabrys, K.
Editors: Cherifi, C., Cherifi, H., Karsai, M. and Musolesi, M.
Journal: COMPLEX NETWORKS
Volume: 689
Pages: 93-104
Publisher: Springer
ISBN: 978-3-319-72149-1
https://eprints.bournemouth.ac.uk/30084/
https://doi.org/10.1007/978-3-319-72150-7
Source: DBLP
Newton’s Gravitational Law for Link Prediction in Social Networks
Authors: Wahid-Ul-Ashraf, A., Budka, M. and Musial-Gabrys, K.
Conference: Complex Networks 2017
Volume: 689
Pages: 93-104
Publisher: Studies in Computational Intelligence 689
ISSN: 1860-949X
Abstract:Link prediction is an important research area in network science due to a wide range of real-world application. There are a number of link prediction meth- ods. In the area of social networks, these methods are mostly inspired by social theory, such as having more mutual friends between two people in a social network platform entails higher probability of those two people becoming friends in the fu- ture. In this paper we take our inspiration from a different area, which is Newton’s law of universal gravitation. Although this law deals with physical bodies, based on our intuition and empirical results we found that this could also work in networks, and especially in social networks. In order to apply this law, we had to endow nodes with the notion of mass and distance. While node importance could be considered as mass, the shortest path, path count, or inverse similarity (AdamicAdar, Katz score etc.) could be considered as distance. In our analysis, we have primarily used de- gree centrality to denote the mass of the nodes, while the lengths of shortest paths between them have been used as distances. In this study we compare the proposed link prediction approach to 7 other methods on 4 datasets from various domains. To this end, we use the ROC curves and the AUC measure to compare the methods. As the results show that our approach outperforms the other 7 methods on 2 out of the 4 datasets, we also discuss the potential reasons of the observed behaviour.
https://eprints.bournemouth.ac.uk/30084/
Source: BURO EPrints