Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Measuring the relative importance of each vertex in a network is one of the most fundamental building blocks in network analysis. Among several importance measures, betenness centrality, in particular, plays key roles in many real applications. Considerable effort has been made for developing algorithms for static settings. Hover, real networks today are highly dynamic and are evolving rapidly, and scalable dynamic methods that can instantly reflect graph changes into centrality values are required.#R##N##R##N#,  present the first fully dynamic method for managing betenness centrality of all vertices in a large dynamic network. Its main data structure is the ighted hyperedge representation of shortest paths called hypergraph sketch.  carefully design dynamic update procedure with theoretical accuracy guarantee. To accelerate updates,  further propose two auxiliary data structures called two-ball index and special-purpose reachability index. Experimental results using real networks demonstrate its high scalability and efficiency. In particular, it can reflect a graph change in less than a millisecond on average for a large-scale b graph with 106M vertices and 3.7B edges, which is several orders of magnitude larger than the limits of previous dynamic methods.
['Takanori Hayashi', 'Takuya Akiba', 'Yuichi Yoshida']
I am writing a research paper on a idea Fully dynamic betweenness centrality maintenance on massive networks
[{"from": "human", "value": "I am writing a research paper on a idea Fully dynamic betweenness centrality maintenance on massive networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Measuring the relative importance of each vertex in a network is one of the most fundamental building blocks in network analysis. Among several importance measures, betenness centrality, in particular, plays key roles in many real applications. Considerable effort has been made for developing algorithms for static settings. Hover, real networks today are highly dynamic and are evolving rapidly, and scalable dynamic methods that can instantly reflect graph changes into centrality values are required.#R##N##R##N#,  present the first fully dynamic method for managing betenness centrality of all vertices in a large dynamic network. Its main data structure is the ighted hyperedge representation of shortest paths called hypergraph sketch.  carefully design dynamic update procedure with theoretical accuracy guarantee. To accelerate updates,  further propose two auxiliary data structures called two-ball index and special-purpose reachability index. Experimental results using real networks demonstrate its high scalability and efficiency. In particular, it can reflect a graph change in less than a millisecond on average for a large-scale b graph with 106M vertices and 3.7B edges, which is several orders of magnitude larger than the limits of previous dynamic methods."}]