The surge in prompt-based learning within Natural Language Processing (NLP) suggests the potential of adapting a "pre-train, prompt" paradigm to graphs as an alternative.
While formal product documentation often provides example data plots with diagnostic suggestions, the sheer diversity of attributes, critical thresholds, and data interactions can be overwhelming to non-experts who subsequently seek help from discussion forums to interpret their data logs.
Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning.
Learning effective recipe representations is essential in food studies.
In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence.