Supervised learning on heterogeneous, attributed entities interacting over time

TL;DR

This paper highlights the need for advanced feature engineering in graph machine learning to effectively model heterogeneous, attributed entities interacting over time, which current methods inadequately address.

Contribution

It proposes a comprehensive feature engineering paradigm in space and time to improve supervised learning on dynamic, heterogeneous entities.

Findings

Current graph ML methods are insufficient for dynamic, attributed entities.

A new feature engineering approach enhances modeling of entity interactions.

Improved classification performance on complex, evolving data.

Abstract

Most physical or social phenomena can be represented by ontologies where the constituent entities are interacting in various ways with each other and with their environment. Furthermore, those entities are likely heterogeneous and attributed with features that evolve dynamically in time as a response to their successive interactions. In order to apply machine learning on such entities, e.g., for classification purposes, one therefore needs to integrate the interactions into the feature engineering in a systematic way. This proposal shows how, to this end, the current state of graph machine learning remains inadequate and needs to be be augmented with a comprehensive feature engineering paradigm in space and time.