Open-world structured sequence learning via dense target encoding

Qin Zhang, Ziqi Liu, Qincai Li, Haolong Xiang, Zhizhi Yu, Junyang Chen, Peng Zhang, Xiaojun Chen

Abstract: Highlights•We represent an initial endeavor in investigating the open-world learning problem within the context of graph streams. To address this challenge, we introduce a novel dense open-world structured sequence learning model, DOSSL, as a proposed solution.•We effectively solve the technical obstacles pertaining to the temporal and structural dynamics, as well as the fluctuating class labels observed in open-world graph streams. DOSSL employs a recurrent neural network based on GCN, which enables the capturing of both temporal and structural dynamics. To enhance the learning process, stochastic states are introduced alongside the conventional deterministic states within the Gaussian distribution. The stochastic components enable the learning of a latent probabilistic model for each node at every time step.•We enhance the proposed DOSSL model with the learning of dense target embedding. It can change the representation of the target classes and better match the known class space. According to the number of different topological spaces enabled by the type of encoding, dense target encoding avoids the limitation of the space complexity represented by the one-hot target encoding.