Microblog similar account detection method based on graph analysis clustering

A detection method and microblogging technology, applied in the information field, can solve problems such as staying in the manual review stage, and achieve the effect of reducing complexity

Active Publication Date: 2018-05-18
BEIJING UNIV OF TECH
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Problems solved by technology

However, at present, the main monitoring method for such accounts is still in the stage of manual review. Therefore, a method f

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  • Microblog similar account detection method based on graph analysis clustering
  • Microblog similar account detection method based on graph analysis clustering

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[0016] Microblog similar account detection method based on graph analysis clustering and multi-dimensional information similarity calculation

[0017] S1. Randomly obtain m through the crawler (m> 100000) Weibo user data as the initial data set, including user background information, blog post information, fans, follow information, comments, and forwarding information;

[0018] S2. Perform experiments on the spark platform, input the information of the target user (u), and perform user portraits through large-scale parallel graph analysis with m users, including user relationship portraits and user behavior portraits, and build a following relationship for each user. Directed graph and Weibo forwarding relationship directed graph;

[0019] S3. Use the directed graph as the data source and use the Spark GraphX ​​graph analysis algorithm Connected Components to cluster users based on user connections in the network. It is generally considered that users classified into one category hav...

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Abstract

The invention discloses a microblog similar account detection method based on graph analysis clustering and multidimensional similarity calculation. The method comprises the following specific contents that: S1: converting a malicious account identification problem into a user similarity calculation problem; S2: constructing a directed graph by user information through graph calculation; S3: usinga graph analysis algorithm to cluster users; S4: importing density weight d, and filtering users with sparse data; S5: imprinting an MDUS (Multi-Dimensional User Similarity) algorithm, and calculating a similarity on the basis of multidimensional information; S6: using an analytic hierarchy process to calculate the weight of each dimension to obtain a weighting similarity; and S7: obtaining the data of m users by a crawler, inputting target user information in a spark experiment, and obtaining a similar account set as a suspicious malicious account, wherein the accuracy of the MDUS algorithmcan be 80%. By use of the method, the graph analysis clustering and the multidimensional similarity calculation are combined to realize a purpose that exceptional accounts are quickly found, and the method has an important meaning for maintaining the stability of a social network site.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to a method for detecting microblog similar accounts based on graph analysis clustering and multidimensional similarity calculation. It is of great significance for social network governance to quickly discover similar accounts on Weibo, perceive malicious group behaviors, and effectively identify network trolls or reincarnated accounts. Background technique [0002] At present, the analysis technology of social network is becoming a hotspot and trend of network technology research. Academia and industry have proposed a large number of research programs, including analyzing user characteristics, user behavior patterns and network structure, for social network security, user privacy protection, network Mass event monitoring, etc. are of great value. Many universities and research institutions at home and abroad have carried out in-depth research in this field. Forei...

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Application Information

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IPC IPC(8): G06F17/30G06Q50/00G06K9/62
CPCG06F16/9535G06F16/958G06Q50/01G06F18/23G06F18/22
Inventor 姜伟田原庄俊玺吴贤达潘邵芹
Owner BEIJING UNIV OF TECH
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