eng International Academy of Ecology and Environmental Sciences Selforganizology 2410-0080 2015-12-1 2 4 91 101 3 article WenJun Zhang 1 2 School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; International Academy of Ecology and Environmental Sciences, Hong Kong In present study, I proposed a node-similarity based algorithm for prediction of missing connections in the network. In this algorithm, whether a node vk can connect to vi or not, depending on the similarity between vk and vi, the similarities between vi and its adjacent nodes, the similarities between vk and the adjacent nodes of vi, and the degree of node vi, and vice versa. Pearson correlation measure, cosine measure, and (negative) Euclidean distance measure (the three measures are for interval attributes), contingency correlation measure (for nominal attributes), and Jaccard coefficient measure (for binary attributes) were used as the between-node similarity. Two application examples showed a better prediction of the algorithm (approximately 60% of missing connections are successfully predicted). Matlab codes of the algorithm were provided. http://www.iaees.org/publications/journals/selforganizology/articles/2015-2(4)/prediction-of-missing-connections-in-the-network.pdf network connections prediction node similarity algorithm