Optimal Path Selection Based On Multiple Constraints In Social Networks

With the rapid development of social media,hundreds of millions of participants join social networks.They engage in daily activities such as experience exchange,posting and commenting in social networks.Social networks have become part of people's daily activities.At present,trust evaluation of participants has become an important issue in social networks.Due to growing participants,the scale of the social network has increased and the network structure is more complicated,which has brought serious challenges to the trust assessment.The focus of this paper is to design an optimal path selection algorithm in social networks,consider the multiple constraints of participants,and complete the trust evaluation from the source participant to the target participant.The main research contents are as follows:(1)In social networks,taking the social attributes of trust,intimacy,prestige as constraints,a multi-constrained bidirectional selection algorithm(MBS)is designed to ensure the quality of path queries and solve the problem about trust evaluation.(2)In MBS,the spatial location and social identity of participants play an important role in path selection.Taking into account the spatial and social attributes of participants,an IR-Tree based multi-constrained bidirectional selection algorithm(IR-Tree-MBS)is proposed.The algorithm makes full use of the characteristics of IR-Tree,performs distance pruning and keyword pruning,reduces the search space quickly and effectively,and saves the query cost.(3)Based on the IR-Tree-MBS,a multi-constrained path pattern matching model(MPM)is proposed to solve the matching from pattern graph to data graph.First,the multi-constrained edge matching algorithm(MEM)finds the matching of any edge from the pattern graph to the data graph.Then,a probability-based sampling estimation method(PSE)is designed to estimate the number of matches,and used to guide the connection sequence of the path mapping query result connection algorithm(PMQRC).Finally,the PMQRC algorithm is designed to connect the query results according to the topology of the pattern graph.It is proved through experiments that this method can achieve the matching from the pattern graph to the data graph.At the same time,this method improves the query efficiency,saves the cost of querying,and finds a more qualified participant set.