Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (5): 96-102.doi: 10.11896/jsjkx.190400072

• Databωe & Big Data & Data Science • Previous Articles     Next Articles

Event Detection Method Based on Node Evolution Staged Optimization

FU Kun, QIU Qian, ZHAO Xiao-meng, GAO Jin-hui   

  1. School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China
    Hebei Province Key Laboratory of Big Data Calculation,Tianjin 300401,China
  • Received:2019-04-11 Online:2020-05-15 Published:2020-05-19
  • About author:FU Kun,born in 1979,Ph.D,associate professor.Her main research interests include social network analysis,community detection and reconfigurable calculation.
  • Supported by:
    This work was supported by the Young Scientists Fund of the National Natural Science Foundation of China (61806072)

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Abstract: Link prediction technology is an effective method to analyze network evolution,it also provides a new idea to detect social events.Now,most of the event detections using link prediction start from the macroscopic overall network evolution.Although there are a few detection methods that combine node evolution,the stability of them are not good,and the sensitivity to the event are not high enough to accurately detect the occurrence of the event.Therefore,an event detection method based on node evolution staged optimization (NESO_ED) was proposed.Firstly,the stability of event detection is enhanced by a staged optimization method,and an array of node index weights is obtained.Then,according to different rules,the optimal similarity calculation index of the node is selected,so that the node can better quantify the network evolution and improve the sensitivity of event detection.In addition,the changes of indicators that selected by nodes in the process of network evolution were also analyzed.It reveals different effects of events on the evolution of nodes.On real social network VAST,the event detection sensibility of NESO_ED is increased by 227% compared with LinkEvent and 63% compared with NodeED.The stability of NESO_ED is also increased by 66% compared with NodeED,which shows that NESO_ED can detect events more accurately and stably.

Key words: Dynamic network, Event detection, Link prediction, Node evolution, Social network

CLC Number: 

  • TP181
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