Research Of The Key Node Algorithm In Dynamic Weighted Network Based On Grouping

The measure of key nodes can be boiled down to the their importance measure and sorting algorithm, the solution to this issue can help to conduct the application of finance, electric power, supply chain, internet and so on. Current research on un-weighted and static network is extensive, however, there is very scarce research on weighted and dynamic network, apparently further study is very necessary.Given the limitations of measuring the importance of nodes according to single factors, the concept of equivalent node weight is presented, on one hand, as a basic factor, node weight does reflect the importance of nodes locally; on the other hand, node weight has spreading effect on other nodes, the more the distance, the less the effect; given the effect of one node weight on the center nodes in global network, a importance sorting strategy of nodes based on equivalent node weight is presented to further improve the precision of the measure.Given the calculation structural characteristic of weighted network, a idea based on calculating after grouping is presented. Classic communicative grouping algorithms perform well in un-weighted graphs, while there is disparity from the expected result in weighted graphs, from this point, a grouping concept model based distance-increment matrix is presented which considers the variations of path matrix when two communities are merged, at the same time, the index of distance increment to assess the grouping quality is also presented. Given the huge time cost of directly calculating path matrix, dynamically updating is adopted to shorten calculating time, thus, this algorithm of grouping measure can be applied to data in larger scale.Given the dynamical property of weighted network varies with time, dynamically updating is adopted. According to different variations such as the joining of nodes, deleting of nodes and the changes of edge weights and so on, different updating strategies are used to shorten time cost. To meet the application requirements of dynamic network, a novel method is presented to calculate the shortest paths between nodes.Finally, a dynamic algorithm is presented to calculate the importance of nodes based on the distance-increment matrix grouping which includes the initialization of data grouping, grouping selection, the updating of distance-matrix and the measure of node importance. The feasibility and effectiveness of the algorithm is verified by experiments based on the data of C-DBLP.