| 摘要(英) |
The structure and scale of the Internet is tremendous. It’s not easy to do research in the domain of data analysis and data mining. With the rise of the social network, there are more and more research showing how to make use the structure of social network, and to find the most influence nodes to maximize the influence spread. This research is composed of two parts: In the first part, we will cluster the network by SetCover and Label algorithm, and apply the modularity function to determine the length of path. In the second part, we will propose two different methods to measure the influence rank of nodes in social network. For the first method, we consider about that the influence of node for their community and for all communities simultaneously. Different from first method, the second method select the most influence nodes for their communities. Next, we select the most influence node for the other communities as well. By proposing the selection of the influence nodes in the structure of social network, the behavior of social network could be analyzed by experts. It also can support web marketing for enterprises to spend less cost to reach maximum benefits.
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| 參考文獻 |
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