\r\naxiomatically designates the graph adjacency matrix as the shift

\r\noperator. In this setup, we often encounter a problem wherein we

\r\nknow the filtered output and the filter coefficients, and need to

\r\nfind out the input graph signal. Solution to this problem using

\r\ndirect approach requires O(N3) operations, where N is the number

\r\nof vertices in graph. In this paper, we adapt the spectral graph

\r\npartitioning method for partitioning of graphs and use it to reduce

\r\nthe computational cost of the filtering problem. We use the example

\r\nof denoising of the temperature data to illustrate the efficacy of the

\r\napproach.","references":null,"publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 123, 2017"}