SIDE: Representation Learning in Signed Directed Networks

Junghwan Kim, Haekyu Park, Ji-Eun Lee, U Kang

WWW 2018

Given a signed directed network, how can we learn node representations which fully encode structural information of the network including sign and direction of edges. Node representation learning or network embedding learns a mapping of each node to a vector. The mapping encodes structural information on network, providing low-dimensional dense node features for general machine learning and data mining frameworks. Since many social networks allow trust (friend) and distrust (enemy) relationships described by signed and directed edges, generalizing network embedding method to learn from sign and direction information in networks is crucial. In addition, social theories are critical tool in signed network analysis. However, none of the existing methods supports all of the desired properties: considering sign, direction, and social theoretical interpretation.

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