Hrl Laboratories, LLC

Daniel K Xie

Jiejun Xu

Tsai-Ching Lu

This disclosure provides a system for pairwise network alignment. In operation, the system receives datasets from two networks, each network having a plurality of nodes. The two networks are embedded based on multi-layer graph convolution to generate network embeddings. An inner product similarity score is generated between the two networks based on an inner product of the network embeddings. Next, a node correspondence is estimated between the two networks using a SoftMax function on the inner product similarity score. Finally, the two networks are aligned on the node correspondence.

System and method for pairwise network alignment

Described is a system for estimating long-term causal effects of interventions on various entities. A synthetic control is generated from selected donor series such that parameters involved in the generation of the synthetic control can be tuned automatically, or manually by a user. Post-intervention synthetic control values are generated from the synthetic control, and estimates of long-term causal effects of an intervention onto aggregate units similar to the donor series are determined. A device is controlled based on the determined estimates.

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Sugar Land, TX, United States of America

Daniel K Xie