Analyzing Disturbed Diffusion on Networks

This work provides the first detailed investigation of the disturbed diffusion scheme FOS/C introduced in [17] as a type of diffusion distance measure within a graph partitioning framework related to Lloyd's k-means algorithm [14]. After outlining connections to distance measures proposed in machine learning, we show that FOS/C can be related to random walks despite its disturbance. Its convergence properties regarding load distribution and edge flow characterization are examined on two different graph classes, namely torus graphs and distance-transitive graphs (including hypercubes), representatives of which are frequently used as interconnection networks.

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