Determinable and interpretable network representation for link prediction.

ABSTRACT: As an intuitive description of complex physical, social, or brain systems, complex networks have fascinated scientists for decades. Recently, to abstract a network's topological and dynamical attributes, network representation has been a prevalent technique, which can map a network or substructures (like nodes) into a low-dimensional vector space. Since its mainstream methods are mostly based on machine learning, a black box of an input-output data fitting mechanism, the learned vector's dimension is indeterminable and the elements are not interpreted. Although massive efforts to cope with this issue have included, say, automated machine learning by computer scientists and learning theory by mathematicians, the root causes still remain unresolved. Consequently, enterprises need to spend en

SUBMITTER: Deng Y 

PROVIDER: S-EPMC9585049 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature