Identifying Influential Nodes in complex networks is of great significance in both theory and reality. K-shell decomposition method is a local method which is suitable for increasing scale of complex networks but limited in accuracy because many nodes are partitioned with the same K-shell value. To overcome the coarse result of K-shell, an improved K-shell which considers the number of nodes’ iteration layers and degrees is proposed. Unlike local methods, global methods such as Betweenness Centralities (BC) are accurate but time-consuming. We employed an algorithm framework which combines advantages of both local and global methods where core network is extracted by improved K-shell and then BC is used to quantitatively analyze nodes in the core network. We compare the proposed method with other existing methods on Susceptible-Infective-Removal (SIR) mode. Experiments on three real networks show that the proposed method is more efficient and accurate.
Asia giant hornet is a predator of European honeybees, invading and destroying their nests. Nowadays, the State of Washington is suffering the attack of this hornet. Due to the potential severe impact on local honeybee populations, it will probably cause a great deal of anxiety. The most informative factors we consider are Detection Date,latitude and longitude. We propose a “time-space-state model” to evaluate the information evaluated by these two indicators. The time-space-state model can be divided into two kinds: the general model of time discretization and space continuum; The general model of space discretization and time semi-continuity. In the previous model, we mainly use K-means algorithm to determine the nest position in discrete time. In the latter model, we use the improved K-means algorithm to obtain the distribution model of hornets with seasonal variation. Through research, we have discovered the temporal and spatial laws of hornets’ nests, which are of great significance for maintaining the stability of agricultural production.
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