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stat.ML2020★ 1 cited
The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version
Abram Magner, Mayank Baranwal, Alfred O. Hero
Graph convolutional networks (GCNs) are a widely used method for graph representation learning. We investigate the power of GCNs, as a function of their number of layers, to distin…
stat.ML2019
Fundamental Limits of Deep Graph Convolutional Networks
Abram Magner, Mayank Baranwal, Alfred O. Hero
Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate the capabilities and limitations of GCNs, we investigate their power, a…
stat.ML2019
Toward Universal Testing of Dynamic Network Models
Abram Magner, Wojciech Szpankowski
Numerous networks in the real world change over time, in the sense that nodes and edges enter and leave the networks. Various dynamic random graph models have been proposed to expl…