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20162025
most citedThe Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version

1 citations · 1 across the 7 of their papers we have counts for

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Showing 2019Show all

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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…