Distributed algorithms to determine eigenvectors of matrices on spatially distributed networks

Eigenvectors of matrices on a network have been used for understanding influence vertex and spectral clustering. For with small geodesic-width their given eigenvalues, we propose preconditioned gradient descent algorithms in this paper to find eigenvectors. We also consider synchronous implementation the proposed at vertex/agent level spatially distributed which each agent has limited data processing capability confined communication range.