Social networks have become increasingly popular and are used for various activities. It is essential to evaluate the trustworthiness of the path between two unknown users in social networks. However, there are usually many social routes between them. In applications of trust, confidence relations among users need to be predicted. Trust route prediction predicts a new trust relationship between two users who are not currently connected. Thus, a challenging problem is finding which social trust route is optimal to yield the most trustworthy route. As a result, this process faces many challenges, such as the sparsity of user-specified trust relations, context awareness of trust, and changes in trust values over time. A new trust route prediction framework was proposed in this paper to enhance prediction accuracy. Considering community relations and node information for community detection, the proposed trust route prediction algorithm, TRTCD, is introduced. The effect of node and community information on link prediction accuracy was empirically investigated here using seven parameters. Experiments on eleven real-world datasets showed that the proposed method performed better than the fourteen existing methods. Based on the obtained experimental results, the proposed method performs better than other methods regarding accuracy and cost. The results show that the TRTCD has, on average, 22% better on eight directed datasets. The results of the NDCG measure show that the TRTCD can reach the average value of trust very close to 1, which is outstanding and performs better than other algorithms in terms of the ATCE criterion since more trusted and integrated communities are identified. In addition, the results show that the TRTCD can be successfully used in directed social networks but needs to work better in undirected social networks.
