Unsupervised Graph Neural Architecture Search with Disentangled Self-supervision (Appendix)

B.1 Complexity Analysis Denote the number of nodes and edges in the graph as N and E, the number of latent factors as K, the number of operation choices as |O|, the dimensionality of hidden representations as d. The time complexity of the disentangled super-network is O(K|E|d+K|V|d2), where the computation for each factor is fully parallelizable and amenable to GPU acceleration, and K is usually a small constant. The time complexity of the self-supervised training and contrastive search modules is both O(K2d2). As architectures under different factors share the parameters, the number of learnable parameters is the same as classical graph super-network, i.e., O(|O|d2). Therefore, the complexity of our method is comparable to classical GNAS methods.