Representative Community Detection Algorithms for Attribute Networks

In network analysis, methods for identifying a community structure of ten involve optimizing a specific objective function to achieve a single optimal allocation from network nodes to communities. In practice, however, we often encounter multiple division schemes with high-quality scores close to the overall optimum. An accurate depiction of the community structure is more appropriately achieved by a series of high-quality division schemes rather than relying solely on a single optimal solution. However, such a collection of network divisions may be challenging to interpret, as its size may rapidly expand to hundreds or even thousands. To this end, we propose a representative community detection algorithm for attribute networks. By clustering similar network partitions and selecting representative partitions from each cluster, we can comprehensively reveal the diversity of network community structures and provide partition results with a more global perspective. Network partitioning experiments on natural and artificial datasets demonstrate that our proposed method performs better than advanced methods.