Next Waves in Veridical Network Embedding

TL;DR

This paper introduces a new framework for network embedding algorithms based on the Veridical Data Science principles, aiming to standardize evaluation and guide future research in the field.

Contribution

It proposes a systematic framework for network embedding that incorporates predictability, computability, and stability, addressing current evaluation challenges.

Findings

Framework aligns with VDS principles for network embedding

Facilitates systematic comparison of embedding methods

Suggests new research directions in network representation learning

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 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.