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Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs.
Controllability Backbone in Networks
Thus, we utilize two lower bounds on the network's SSC based on the zero forcing notion and graph distances.
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