Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider a class of opinion dynamics on networks where at each time-step, each node in the network disregards the opinions of a certain number of its most extreme neighbors and updates its own opinion as a ighted average of the remaining opinions. When all nodes disregard the same number of extreme neighbors, previous work has shown that consensus will be reached if and only if the network satisfies certain topological properties. ,  consider the implications of allowing each node to have a personal threshold for the number of extreme neighbors to ignore.  provide graph conditions under which consensus is guaranteed for such dynamics.  then study random networks where each node's threshold is drawn from a certain distribution, and provide conditions on that distribution, together with conditions on the edge formation probability, that guarantee that consensus will be reached asymptotically almost surely.
['Shreyas Sundaram']
I am writing a research paper on a idea Ignoring extreme opinions in complex networks: The impact of heterogeneous thresholds
[{"from": "human", "value": "I am writing a research paper on a idea Ignoring extreme opinions in complex networks: The impact of heterogeneous thresholds"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  consider a class of opinion dynamics on networks where at each time-step, each node in the network disregards the opinions of a certain number of its most extreme neighbors and updates its own opinion as a ighted average of the remaining opinions. When all nodes disregard the same number of extreme neighbors, previous work has shown that consensus will be reached if and only if the network satisfies certain topological properties. ,  consider the implications of allowing each node to have a personal threshold for the number of extreme neighbors to ignore.  provide graph conditions under which consensus is guaranteed for such dynamics.  then study random networks where each node's threshold is drawn from a certain distribution, and provide conditions on that distribution, together with conditions on the edge formation probability, that guarantee that consensus will be reached asymptotically almost surely."}]