Graphlet-based self-supervised model for topological network alignment

Network alignment seeks to identify corresponding nodes across graphs, a task that is challenging when structural signals are weak or noisy. Existing optimization-based methods offer strong accuracy but do not scale well, while embedding-based methods are efficient yet often less precise. We propose SST-Align, a hybrid alignment framework that combines the strengths of both paradigms: a topology-driven initialization followed by self-supervised representation learning. Across six real-world datasets, SST-Align achieves competitive or superior node-mapping accuracy compared to strong graph alignment baselines. Under increasing structural noise, SST-Align remains robust and typically matches the performance of non-pretrained methods. We further demonstrate that SST-Align generalizes to heterogeneous networks through a simple extension incorporating node-type awareness.