Data clustering with edge domination in complex networks
This work addresses community detection and data clustering in complex networks, but it appears incremental as it builds on existing dynamical system models.
The paper tackles the problem of data clustering by modeling a dynamical system where particles dominate edges in complex networks, and shows that the proposed algorithm performs well when the number of clusters is known in advance, as demonstrated through simulations on 10 datasets.
This paper presents a model for a dynamical system where particles dominate edges in a complex network. The proposed dynamical system is then extended to an application on the problem of community detection and data clustering. In the case of the data clustering problem, 6 different techniques were simulated on 10 different datasets in order to compare with the proposed technique. The results show that the proposed algorithm performs well when prior knowledge of the number of clusters is known to the algorithm.