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Signed network representation with novel node proximity evaluation.
- Source :
-
Neural Networks . Apr2022, Vol. 148, p142-154. 13p. - Publication Year :
- 2022
-
Abstract
- Currently, signed network representation has been applied to many fields, e.g. , recommendation platforms. A mainstream paradigm of network representation is to map nodes onto a low-dimensional space, such that the node proximity of interest can be preserved. Thus, a key aspect is the node proximity evaluation. Accordingly, three new node proximity metrics were proposed in this study, based on the rigorous theoretical investigation on a new distance metric - signed average first-passage time (SAFT). SAFT derives from a basic random-walk quantity for unsigned networks and can capture high-order network structure and edge signs. We conducted network representation using the proposed proximity metrics and empirically exhibited our advantage in solving two downstream tasks — sign prediction and link prediction. The code is publicly available. [ABSTRACT FROM AUTHOR]
- Subjects :
- *RANDOM walks
*SOCIAL networks
*NEWSVENDOR model
Subjects
Details
- Language :
- English
- ISSN :
- 08936080
- Volume :
- 148
- Database :
- Academic Search Index
- Journal :
- Neural Networks
- Publication Type :
- Academic Journal
- Accession number :
- 155458271
- Full Text :
- https://doi.org/10.1016/j.neunet.2022.01.014