Key Nodes Discovery in Large-Scale Logistics Network Based on MapReduce

In recent years, the study of social network is raising more and more attentions of researchers, locating the key nodes in social network is a hot research point. Lots of papers about how to discover the key nodes in social network such as mail network, micro log network was published. However, few people study on key nodes discovery in logistics network. In addition, most of methods of key nodes discovery only take relationship strength between nodes into account, few take the weight of node into account. In this paper, a node activity degree based on users behavior features was defined, As a result, the logistics networks can be considered as a double-weighted networks by taking relationship strength as edge's weight and node activity as node weight. Based on Page Rank algorithm, an improved algorithms was proposed in this paper. The nodes weights were used as damping coefficient, and weight of the edges was used to compute importance of nodes during iterative process. At last, we implemented the improved Page Rank algorithm using MapReduce. One dataset from a logistics company were selected and comprehensive experiments were conducted. The experimental results show that proposed algorithms can effectively and efficiently discover key nodes in real logistics network.