Research On Graph Query Algorithm Based On Multi-source Data Fusion

In the new era of intelligence,facing many new problems such as false news,hidden telecom fraud groups,and other intelligence analysis,the research team has constructed a complex network algorithm for the perception of people(counterfeiters),objects,organizations,true and false events,and mutual relationships,Understanding and predicting intelligence analysis framework IAF,from a large-scale social association network based on human electronic footprints in social / physical /cyberspace,quickly retrieves the function of analyzing community members and groups in accordance with a specific pattern.Key functions of the module.This paper focuses on the research of fast search calculation methods for specific pattern groups of graph query algorithms,and carries out the following work:First of all,this paper proposes a method model for rapid group retrieval of specific patterns.This model is based on the research on graph query theory.It integrates cyberspace,social space,and physical space information data,builds a multi-layer complex network with human nodes,and applies query algorithms to quickly retrieve communities that meet specific patterns.This method can quickly find communities with similar substructures in large-scale networks.Secondly,an empirical analysis of the document data collaboration network is performed based on the above method model.A collaborator network with document collaborators as the research object was constructed.Then,based on the community detection method of a specific pattern,the topological characteristics of the network are analyzed.According to the characteristics,the changing rules of research hotspots in the field of bibliometrics and the methods for quickly discovering new research hotspots are analyzed.Finally,the empirical analysis of the user transaction network of commercial banks is carried out according to the method model proposed in this paper.A user-touser transfer network was constructed with user transfer events in commercial banks as the research object.The process of community-specific retrieval in this network is given,then the characteristics of the transfer network are analyzed,and the possible moneylaundering structure is retrieved according to the characteristics of the network structure.This paper proposes a model-specific fast retrieval method model.Two empirical analyses were performed around the model.Based on the constructed network,the topological structure characteristics of the network are analyzed.According to the topological substructure in the network,changes in research hotspots in the field of bibliometrics can be discovered,and abnormal transfer models and fraud risks can be discovered in commercial banks.The model has very good scalability.Through two demonstrations,it can be applied not only in the field of bibliometrics and commercial banks,but also in social networks for community analysis.It can apply fraud group detection and marketing strategies for specific model groups.Has a good application prospect.