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Signed network representation with novel node proximity evaluation
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AI
DOI：10.1016/j.neunet.2022.01.014.png)
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Abstract
En 中文
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. (C)& nbsp;2022 Elsevier Ltd. All rights reserved.
Keywords:
Signed social network
Network representation
Node proximity
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