Network-based learning through particle competition for data clustering

Complex network provides a general scheme for machine learning. In this paper, we propose a competitive learning mechanism realized on large scale networks, where several particles walk in the network and compete with each other to occupy as many nodes as possible. Each particle can perform a random walk by choosing any neighbor to visit, a deterministic walk by choosing to visit the node with the highest domination, or a combination of them. A computational complexity analysis is developed of the proposed algorithm. Computer simulations performed on several real-world data sets, including a large scale data set, reveal attractive results when the model is applied for data clustering problems.