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.
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| http://dx.doi.org/10.1016/j.neunet.2022.01.014 | DOI Listing |
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