The invention discloses a
heterogeneous network embedding method for reserving
label information based on node signature. During
network embedding, high-dimensional graph data is mapped to low-dimensional vectors so that that a
machine learning
algorithm is adopted to effectively the high-dimensional graph data. The method comprises the following steps: carrying out prime number dictionary mappingon all
label types in a network; extracting a neighborhood
label set of network nodes; constructing a node
signature vector; and building network node representations. The
network structure,
semantics and label information of heterogeneous graphs are comprehensively utilized. The idea of
digital signature and the characteristics of prime numbers are used. A
network representation learning framework of the heterogeneous graphs is constructed, reservation of network node and edge label information on the heterogeneous graph is realized, subsequent
node clustering, classification, link prediction and other
machine learning tasks are performed according to learned
heterogeneous network node representations, and the existing homogeneous and
heterogeneous network embedding method can be universally expanded and improved.