In Spring 2023, we hold seminars in-person on Fridays from 12:10-1:30 PM EST at 707 International Affairs Building (the Lindsay Rogers Room). We also offer a hybrid option.
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Speaker: Tian Zheng (Columbia University)
Date: Friday, April 28, 2023
Time: 12:10 PM to 1:30 PM EST
Location: IAB LRR 707
Zoom Passcode: pmc
Title: Veridical Network Embeddings and Community Detection
Abstract: 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 a 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 on 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. I will also present a recent project on a scalable community detection algorithm for community detection in massive networks as a case study.