文献记录(part106)--GRAPH AUTO-ENCODER VIA NEIGHBORHOOD WASSERSTEIN RECONSTRUCTION

学习笔记，仅供参考，有错必纠
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GRAPH AUTO-ENCODER VIA NEIGHBORHOOD WASSERSTEIN RECONSTRUCTION

ABSTRACT

Graph neural networks (GNNs) have drawn significant research attention recently, mostly under the setting of semi-supervised learning. When task-agnostic representations are preferred or supervision is simply unavailable, the auto-encoder framework comes in handy with a natural graph reconstruction objective for unsupervised GNN training.

However, existing graph auto-encoders are designed to reconstruct the direct links, so GNNs trained in this way are only optimized towards proximity-oriented graph mining tasks, and will fall short when the topological structures matter.

In this work, we revisit the graph encoding process of GNNs which essentiall