Learning Graph Representation via Formal Concept Analysis
This addresses the need for hierarchical clustering in data analysis, but appears incremental as it applies an existing mathematical framework to graph representation learning.
The paper tackles the problem of learning hierarchical graph representations from multivariate data by using formal concept analysis to extract subset-superset relationships between overlapping clusters, and empirically shows it effectively extracts hierarchical structures compared to a baseline.
We present a novel method that can learn a graph representation from multivariate data. In our representation, each node represents a cluster of data points and each edge represents the subset-superset relationship between clusters, which can be mutually overlapped. The key to our method is to use formal concept analysis (FCA), which can extract hierarchical relationships between clusters based on the algebraic closedness property. We empirically show that our method can effectively extract hierarchical structures of clusters compared to the baseline method.