Graph pooling that summaries the information in a large graph into a compact form is essential in hierarchical graph representation learning. Existing graph pooling methods either suffer from high computational complexity or cannot capture the global dependencies between graphs before and after pooling. To address the problems of existing graph pooling methods, we propose Coarsened Graph Infomax Pooling (CGIPool) that maximizes the mutual information between the input and the coarsened graph of each pooling layer to preserve graph-level dependencies. To achieve mutual information neural maximization, we apply contrastive learning and propose a self-attention-based algorithm for learning positive and negative samples. Extensive experimental results on seven datasets illustrate the superiority of CGIPool comparing to the state-of-the-art methods.

In 2018 the Reddit community r/deepfakes gained international attention thanks to a piece of investigative journalism by Samantha Cole, deputy editor at VICE. Members of the forum had been using a burgeoning technology to superimpose celebrities' faces onto pornographic videos. For the general public – and no doubt the unwitting stars – it was a shock. Most were unaware this technology existed. Very few believed it was possible to produce such realistic footage.