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- W2984343629 abstract "Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges. However, these models focus on undirected graphs and therefore ignore the potential direction of the link, which is limiting for numerous real-life applications. In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs. We present a new gravity-inspired decoder scheme that can effectively reconstruct directed graphs from a node embedding. We empirically evaluate our method on three different directed link prediction tasks, for which standard graph AE and VAE perform poorly. We achieve competitive results on three real-world graphs, outperforming several popular baselines." @default.
- W2984343629 created "2019-11-22" @default.
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- W2984343629 date "2019-11-03" @default.
- W2984343629 modified "2023-10-18" @default.
- W2984343629 title "Gravity-Inspired Graph Autoencoders for Directed Link Prediction" @default.
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- W2984343629 doi "https://doi.org/10.1145/3357384.3358023" @default.
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