Supervised learning on heterogeneous, attributed entities interacting over time
This addresses the problem of applying machine learning to complex real-world systems like social or physical phenomena for researchers and practitioners, but it is incremental as it builds on existing graph methods.
The paper identifies that current graph machine learning methods are inadequate for classifying heterogeneous, attributed entities with dynamic interactions over time, and proposes augmenting them with a comprehensive feature engineering paradigm in space and time.
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.