52 citations · 128 across the 17 of their papers we have counts for
Showing 2022 · cs.LGShow all
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cs.LG2022
Handling Missing Data via Max-Entropy Regularized Graph Autoencoder
Ziqi Gao, Yifan Niu, Jiashun Cheng +6
Graph neural networks (GNNs) are popular weapons for modeling relational data. Existing GNNs are not specified for attribute-incomplete graphs, making missing attribute imputation…
cs.LG2022★ 52 cited
Rethinking Graph Neural Networks for Anomaly Detection
Jianheng Tang, Jiajin Li, Ziqi Gao +1
Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first…
cs.LG2022★ 2 cited
Fast and Provably Convergent Algorithms for Gromov-Wasserstein in Graph Data
Jiajin Li, Jianheng Tang, Lemin Kong +4
In this paper, we study the design and analysis of a class of efficient algorithms for computing the Gromov-Wasserstein (GW) distance tailored to large-scale graph learning tasks.…