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Veridical Network Embedding

[Virtual] Hot Topics: Foundations of Stable, Generalizable and Transferable Statistical Learning March 07, 2022 - March 10, 2022

March 10, 2022 (08:00 AM PST - 08:25 AM PST)
Speaker(s): Tian Zheng (Columbia University)
Location: MSRI: Online/Virtual
  • networks

  • Embedding

  • Representation Learning

Primary Mathematics Subject Classification
Secondary Mathematics Subject Classification No Secondary AMS MSC

Veridical Network Embedding


Embedding nodes of a large network into a metric (e.g., Euclidean) space has become an area of active research in statistical machine learning, which has found applications in natural and social sciences. Generally, a representation of a network object is learned in Euclidean geometry and is then used for subsequent tasks regarding the nodes and/or edges of the network, such as community detection, node classification, and link prediction. Network embedding algorithms have been proposed in multiple disciplines, often with domain-specific notations and details. In addition, different measures and tools have been adopted to evaluate and compare the methods proposed under different settings, often dependent of the downstream tasks. As a result, it is challenging to study these algorithms in the literature systematically. Motivated by the recently proposed Veridical Data Science (VDS) framework, we propose a framework for network embedding algorithms and discuss how the principles of predictability, computability, and stability apply in this context. The utilization of this framework in network embedding holds the potential to motivate and point to new directions for future research.

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Veridical Network Embedding

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