Measuring the Privacy Leakage via Graph Reconstruction Attacks on Simplicial Neural Networks (Student Abstract). (arXiv:2302.04373v1 [cs.LG])

In this paper, we measure the privacy leakage via studying whether graph
representations can be inverted to recover the graph used to generate them via
graph reconstruction attack (GRA). We propose a GRA that recovers a graph's
adjacency matrix from the representations via a graph decoder that minimizes
the reconstruction loss between the partial graph and the reconstructed graph.
We study three types of representations that are trained on the graph, i.e.,
representations output from graph convolutional network (GCN), graph …

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