Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dynamic community detection algorithms tryto solve problems that identify communities of dynamicnetwork which consists of a series of network snapshots. Toaddress this issue, here  propose a new dynamiccommunity detection algorithm based on incrementalidentification according to a vertex-based metric calledpermanence.  incrementally analyze the communityownership of partial vertices, so as to avoid the reassignmentof all the vertices in the network to their respectivecommunities. In addition,  propose a new metrics calledevolution strength to measure the error probably caused byincrementally assigning the community ownership or theabrupt change of network structure. The experiment resultsshow that our proposed algorithm is able to identify thecommunity structure in a network with a higher efficiency. Meanwhile, due to the lack of dynamic network data withground-truth structure and limitation of existing syntheticmethods,  propose a novel method for generatingsynthetic data of dynamic network with ground-truthstructure, which defines evolution events and evolution rateof events, so as to get more realistic synthetic data.
['Xiaoming Li', 'Bin Wu', 'Qian Guo', 'Xuelin Zeng', 'Chuan Shi']
I am writing a research paper on a idea Dynamic Community Detection Algorithm Based on Incremental Identification
[{"from": "human", "value": "I am writing a research paper on a idea Dynamic Community Detection Algorithm Based on Incremental Identification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Dynamic community detection algorithms tryto solve problems that identify communities of dynamicnetwork which consists of a series of network snapshots. Toaddress this issue, here  propose a new dynamiccommunity detection algorithm based on incrementalidentification according to a vertex-based metric calledpermanence.  incrementally analyze the communityownership of partial vertices, so as to avoid the reassignmentof all the vertices in the network to their respectivecommunities. In addition,  propose a new metrics calledevolution strength to measure the error probably caused byincrementally assigning the community ownership or theabrupt change of network structure. The experiment resultsshow that our proposed algorithm is able to identify thecommunity structure in a network with a higher efficiency. Meanwhile, due to the lack of dynamic network data withground-truth structure and limitation of existing syntheticmethods,  propose a novel method for generatingsynthetic data of dynamic network with ground-truthstructure, which defines evolution events and evolution rateof events, so as to get more realistic synthetic data."}]