Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding

3 code implementations • NeurIPS 2023 • Jiangyan Ma, Yifei Wang, Yisen Wang

However, from a theoretical perspective, the universal expressive power of spectral embedding comes at the price of losing two important invariance properties of graphs, sign and basis invariance, which also limits its effectiveness on graph data.

Graph Classification Graph Embedding +1