Hyperparameter-free and Explainable Whole Graph Embedding
This work addresses the need for simpler and more interpretable graph embedding methods for researchers and practitioners in fields like bioinformatics and network analysis, though it appears incremental as it builds on existing DHC and entropy concepts.
The paper tackles the problems of tedious parameter tuning and poor explanation in whole graph embedding by introducing DHC-E, a hyperparameter-free and explainable method based on the DHC theorem and Shannon Entropy, which achieves a trade-off between simplicity and quality in supervised classification tasks for molecular, social, and brain networks and performs well in lower-dimensional graph visualization.
Graphs can be used to describe complex systems. Recently, whole graph embedding (graph representation learning) can compress a graph into a compact lower-dimension vector while preserving intrinsic properties, earning much attention. However, most graph embedding methods have problems such as tedious parameter tuning or poor explanation. This paper presents a simple and hyperparameter-free whole graph embedding method based on the DHC (Degree, H-index, and Coreness) theorem and Shannon Entropy (E), abbreviated as DHC-E. The DHC-E can provide a trade-off between simplicity and quality for supervised classification learning tasks involving molecular, social, and brain networks. Moreover, it performs well in lower-dimensional graph visualization. Overall, the DHC-E is simple, hyperparameter-free, and explainable for whole graph embedding with promising potential for exploring graph classification and lower-dimensional graph visualization.