Research On Self-organizing Architecture And Resource Discovery Mechanisms For Distributed Social Networks

In recent years,with the development of computer network technology and intelligent terminal,online social networks have gradually become important platforms for instant messaging,resource sharing and business activities in people's daily life.At the same time,they are also Internet applications with wide user coverage,great influence and high commercial value.However,at present,the development of mainstream social networks based on the central architecture mode are facing the problems of security and privacy brought by centralized storage of user data,single point of failure caused by central server downtime,and low network scalability.In addition,due to the huge volume of social users,social activities produce a large number of social resources,and it becomes difficult to query useful resources from social communication big data.The new distributed self-organizing social network model based on human social structure naturally adapts to the peer-to-peer social model,and each peer-to-peer individual has a personalized information network.Therefore,it is urgent to study the new network architecture model and social resource discovery algorithm,which is of great research significance,can solve the defects of the current social network architecture,and can accurately query,identify and mine valuable resources in social communication big data.The research on distributed network architecture model,resource discovery method under the new model and resource discovery optimized algorithm are the key components of distributed social network architecture and resource discovery research.The research results will lay a theoretical foundation for the construction and wide application of the next generation social network.Therefore,carrying out this research work has great theoretical significance and practical value.To address the difficulties faced by the development of mainstream social networks,combined with the research status of decentralized networks at home and abroad,this study analyzes some key problems to be solved in the research of distributed social network architecture and resource discovery methods.According to the theory of human sociology,based on the existing research results,this study explores and studies the self-organizing architecture model of next-generation distributed social networks and the resource discovery method based on this model.The specific research contents and innovations are as follows:(1)Aiming at the problems and challenges faced by the development of existing peer-to-peer social networks,such as "the singleness of topology construction method,difficult to well adapt to high dynamic distributed social network environment,paying more attention to the mapping between physical social networks and their logical overlay networks,and paying less attention to the characteristics of social networks themselves",a self-organizing distributed social network architecture model,named SDSN,is proposed.Firstly,based on the social self-organization theory in human sociology and peer-to-peer network technology,the overall framework of decentralized distributed self-organization network is constructed.Constructing specific social network node index structure,including local social resource index structure,node interest index structure and node knowledge base structure,respectively to solve the problems of rapid identification and retrieval of local social resources,interest vector retrieval of nodes and localized storage of friends' information.Design network routing structure and self-organizing social relations.Secondly,a node interest fingerprint generating algorithm based on information fingerprint technology is proposed to provide a consistent solution for heterogeneous data scattered in network nodes.Then,according to the social relationship theory and complex network topology generation theory in sociology,an adaptive method of generating network topology is designed.Hamming distance is used to calculate the similarity of interest fingerprint of network nodes to measure the interest similarity of network nodes.According to the scale-free characteristics of social networks and the transitivity characteristics of social relations,a preferred connection algorithm based on the maximum probability of the product of interest similarity and connection degree is proposed to solve the topology connection problem of new nodes.According to the small world network phenomenon,an interest similarity neighbor topology connection algorithm is proposed to solve the topology mismatch caused by network disturbance.Furthermore,self-organizing network query and feedback messages are defined and designed,including random networking query and feedback messages,new node interest preference connection query and feedback messages,interest similarity node topology connection detection query and feedback messages,resource search and location query and feedback messages.Finally,a simulation and verification system for distributed peer-to-peer social networks is developed to verify the distributed self-organizing social network model proposed in this study with real data sets,and compared with the typical complex network topology construction model to verify that the network structure index and resource search positioning index of the SDSN model proposed in this study have achieved better performance.(2)To solve the low efficiency of resource discovery when the existing peer-topeer network resource discovery model is applied to decentralized distributed social networks,this study proposes an interest-aware social-like P2 P model,named IASLP,for resource discovery in distributed social networks.Firstly,a social resource matching and recognition algorithm is designed to retrieve and identify resources with the help of the node interest domain and query topic.Secondly,a self-organizing community generating method based on social interest perception is proposed.The interest community characterized by resource sharing is defined according to the homogeneity theory in sociology and the theory of strong relationships and weak relationships.The knowledge updating algorithm of the interest community is designed according to the theory of memory and forgetting.Thirdly,a social interest-aware adaptive routing algorithm is proposed,including interest-ware routing query and adaptive routing forwarding strategy.When routing forwarding,an olfactory adaptive forwarding node recommendation algorithm is proposed to predict the forwarding path and improve the query forwarding efficiency.Finally,the IASLP model is verified on the SDSN network topology using real data sets,and compared with the existing classical peer-to-peer network resource discovery model to verify that the IASLP model proposed in this study has achieved good performance in terms of recall,number of found resource query overhead,query performance and other indicators.(3)To address the failure of query routing caused by network disturbance and dynamic topology change,and the low efficiency of resource discovery in the "cold start" stage of the network,this study optimizes the IASLP model and proposes an adaptive routing query algorithm model based on node resource value(ARQARV).This algorithm model uses the friend relationship in the personal information network to improve the efficiency of resource discovery.Firstly,the "rich" resource neighbor node in the personal information network is abstractly represented,and the calculation method of "rich" neighbor resource value is given.Secondly,in order to solve the problem of low resource discovery efficiency caused by more "zero knowledge" nodes in the network "cold start" stage,an "expected high-quality" neighbor discovery method is proposed,and the "zero knowledge" nodes give priority to the neighbors with more expected resources.Thirdly,a query optimization method based on a probabilistic model is proposed,which can adaptively select the query method according to the realtime situation of the number of friend nodes in the local knowledge base,so as to improve the resource discovery efficiency in the "low knowledge" state of nodes.Then,based on the sociological theory,the expert node in the network is defined,the valuable neighbors are obtained through the "expert" node optimization strategy,and the "shortcut" of resource query is pointed out.Finally,a comparative experiment with the classical resource discovery model on real data sets is carried out to verify that the ARQARV model proposed in this study has achieved better resource discovery performance in terms of recall,number of found resource and number of visited nodes.