A method for maximizing information dissemination in online social networks based on community structure mining algorithm
A technology of information dissemination and algorithm, applied in computing, instrumentation, data processing applications, etc., can solve problems such as different sizes of communities, and achieve the effect of improving efficiency and improving feasibility
Active Publication Date: 2018-06-22
NANJING UNIV
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However, the sizes of the mined communities are often different, so how many seed node indicators should be assigned to the subgraph corresponding to a community for mining has become a difficult problem
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[0041] In order to understand the technical content of the present invention more specifically, specific embodiments are given and described as follows in conjunction with the accompanying drawings.
[0042] Such as figure 1 As shown, the method goes through three stages, namely, complex network generation stage (step 1), model training stage (step 2) and seed node mining stage (step 3). Once the model in the model training phase is trained, it can be used repeatedly in the seed node mining phase. This model answers the question, after a large graph is divided into several different subgraphs, how many seed nodes are mined from each subgraph.
[0043] A method for maximizing information dissemination in online social networks based on community structure mining includes the following steps:
[0044] 1) Complex network generation stage:
[0045] a) Crawl the link relationship between users (that is, an undirected graph) and the user's operation log from the online social net...
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An information transmission maximization method based on a complex network community discovery algorithm includes the following steps: 1) a complex network generation stage in which data crawled from an online social network is preprocessed to be turned to a directed weighted graph; a model training stage in which a) the community discovery algorithm is used to perform community discovery on a complex network, b) a corresponding subgraph is output for each community, c) different seed node selection experiments are performed for each subgraph, and d) generated data is used to train a model, and the obtained model is stored in a designated area for use; 2) a seed node selection stage in which a) an original complex network that needs mining is divided into different communities, and corresponding subgraphs are generated, b) predictions trained in the model training stage are imported, and c) a prediction model is used to calculate the number of seed nodes which needs to be allocated to each subgraph for mining. Time efficiency in a node having the highest transmission capacity is selected in complex network data. The method has the advantages of excellent selection result, fast data processing speed and the like.
Description
technical field [0001] The invention relates to a method for maximizing information dissemination in an online social network, which introduces a learning mechanism on the basis of a communication maximization algorithm based on a community structure. Background technique [0002] With the popularity and size of online social networks, people began to pay attention to the problem of how to effectively disseminate information in online social networks. And put forward the related algorithm. But the efficiency of these algorithms can not meet the actual needs. In order to achieve an efficient algorithm, this method introduces a learning mechanism into the traditional information diffusion maximization algorithm based on community structure mining, thereby improving the efficiency of the original algorithm. The principle of the traditional community structure-based information dissemination maximization algorithm is as follows. The community discovery algorithm is used to div...
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IPC IPC(8): G06Q50/00
Inventor 吴骏海军彭岳李宁吴和生谢俊元
Owner NANJING UNIV