Comparing Graph Clusterings: Set partition measures vs. Graph-aware measures
This work addresses the need for better evaluation metrics in graph analysis, offering a novel approach that could improve clustering assessment in network science, though it appears incremental as it builds on existing partition measures.
The paper tackles the problem of comparing graph clusterings by proposing graph-aware similarity measures that incorporate graph topology, showing they behave oppositely to set partition measures regarding resolution issues and provide complementary information for assessing partition similarity.
In this paper, we propose a family of graph partition similarity measures that take the topology of the graph into account. These graph-aware measures are alternatives to using set partition similarity measures that are not specifically designed for graph partitions. The two types of measures, graph-aware and set partition measures, are shown to have opposite behaviors with respect to resolution issues and provide complementary information necessary to assess that two graph partitions are similar.