Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  have recently seen a surge of work on distributed graph filters, extending classical results to the graph setting. State of the art filters have hover only been examined from a deterministic standpoint, ignoring the impact of stochasticity in the computation (e.g., temporal fluctuation of links) and input (e.g., the value of each node is a random process). Initiating the study of stochastic graph signal processing, this paper shows that a prominent class of graph filters, namely autoregressive moving average (ARMA) filters, are suitable for the stochastic setting. In particular,  prove that an ARMA filter that operates on a stochastic signal over a stochastic graph is equivalent, in the mean, to the same filter operating on the expected signal over the expected graph.  also characterize the variance of the output and  provide an upper bound for its average value among different nodes. Our results are validated by numerical simulations.
['Elvin Isufi', 'Andrea Simonetto', 'Andreas Loukas', 'Geert Leus']
I am writing a research paper on a idea Stochastic graph filtering on time-varying graphs
[{"from": "human", "value": "I am writing a research paper on a idea Stochastic graph filtering on time-varying graphs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  have recently seen a surge of work on distributed graph filters, extending classical results to the graph setting. State of the art filters have hover only been examined from a deterministic standpoint, ignoring the impact of stochasticity in the computation (e.g., temporal fluctuation of links) and input (e.g., the value of each node is a random process). Initiating the study of stochastic graph signal processing, this paper shows that a prominent class of graph filters, namely autoregressive moving average (ARMA) filters, are suitable for the stochastic setting. In particular,  prove that an ARMA filter that operates on a stochastic signal over a stochastic graph is equivalent, in the mean, to the same filter operating on the expected signal over the expected graph.  also characterize the variance of the output and  provide an upper bound for its average value among different nodes. Our results are validated by numerical simulations."}]