Hybrid Breadth-Depth Search Algorithm in Crowd Transaction Network

In the Crowd Intelligence-based Transaction Network (CITN), each intelligent individual stores the commodity information in a local node. The information is shared via searching and routing in the circle of friends. The demand of searching the commodity information in an efficient way motivates this study. We develop an algorithm that can search the information for a certain node in a short period of time and with low network resource consumption. This paper proposes a heuristic search algorithm, the hybrid breadth-depth (HBD) algorithm, which helps to find suitable suppliers and commodities in the CITN for any demand of the buyers. The HBD algorithm takes full advantage of the breadth-first search (BFS) and depth-first search (DFS). It defines the relevance between nodes, optimizes the search rules and forwarding paths based on the relevance between nodes and the neighbor nodes in their circles of friends, and improves both the success rate and efficiency. Our test on the performance of the HBD algorithm shows that it is superior in the success rate, search time, matching degree, network resource consumption, and scalability. Compared with previous search algorithms such as the food algorithm and the random walk algorithm, in the CITN, the HBD algorithm can greatly reduce the search time and the network resource consumption, and increase the success rate and matching degree.