Representative Community Detection Algorithms for Attribute-Free Networks

In the field of network analysis, methods for identifying community structure often involve optimizing a specific objective function to achieve a single optimal allocation from network nodes to communities. However, in practice, we often encounter multiple division schemes with high quality scores that are close to the overall optimum. In fact, an accurate depiction of the community structure is more appropriately achieved by a series of high-quality division schemes rather than relying on a single optimal solution alone. However, such a collection of network divisions may be difficult to interpret, as its size may rapidly expand to hundreds or even thousands. To this end, this paper introduces an innovative strategy to reveal the diversity of network community structures more comprehensively and to provide a set of more globally-visioned delineation results by clustering similar network divisions and then selecting representative divisions from each cluster.