  We present a framework for learning Node Embeddings from Static Subgraphs
(NESS) using a graph autoencoder (GAE) in a transductive setting. NESS is based
on two key ideas: i) Partitioning the training graph to multiple static, sparse
subgraphs with non-overlapping edges using random edge split during data
pre-processing, ii) Aggregating the node representations learned from each
subgraph to obtain a joint representation of the graph at test time. Moreover,
we propose an optional contrastive learning approach in transductive setting.
We demonstrate that NESS gives a better node representation for link prediction
tasks compared to current autoencoding methods that use either the whole graph
or stochastic subgraphs. Our experiments also show that NESS improves the
performance of a wide range of graph encoders and achieves state-of-the-art
results for link prediction on multiple real-world datasets with edge homophily
ratio ranging from strong heterophily to strong homophily.
