Denoising of network graphs using topology diffusion
Partial Differential Equation (PDE) based diffusion has been utilized for image denoising for more than two decades. It is known that the process of diffusion preserves the edges and object boundaries making it a suitable preprocessing step for edge detection. Synergetic to these efforts, in this work, we apply diffusion to network graphs leading to an efficient algorithm for removing anomalous structures from the network topology. The driving force for the diffusion process in our work is the clustering propensity that exists in real social networks. The proposed diffusion enhances the boundaries of communities of which makes it a suitable step prior to community detection.