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One of the main problems encountered by social networks is the cold start problem. The term "cold start problem" refers to the difficulty in predicting new users' friendships due to the limited number of links those users have with existing nodes. To fill the gap, this paper proposes a Fully Integrated Link Prediction Algorithm (FILPA) that describes the social distance of nodes by using "betweenness centrality," and develops a Social Distance Index (SDI) based on micro- and macro-network structure according to social distance. With the aim of constructing adaptive SDIs that are suitable for the characteristics of a network, a naive Bayes (NB) method is firstly adopted to select appropriate SDIs according to the density and social distance characteristics of common neighbors in the local network. To avoid the risk of algorithm accuracy reduction caused by blind combination of SDIs, the AdaBoost meta-learning strategy is applied to develop a Fully Integrated Social Distance Index (FISDI) composed of the best SDIs screened by NB. The possible friendships among nodes will then be comprehensively presented using high performance FISDI. Finally, in order to realize the "products rapidly attracting users" in new user marketing, FILPA is used to predict the possible friendship between new users in an online brand community and others in different product circles. [ABSTRACT FROM AUTHOR]

Selecting thepotential brand spokesperson on social network (SN), who has the huge growth potential and will own the largest number of fans in the future, can providehigher returns at lessrisks. In this study, smart link prediction algorithm (SLPA) is proposed to predict the evolvements of SNs and the brand spokesperson with the great future potential is selected based on the prediction results of SN evolution. In SLPA, mean roughness classification uniformity (MRCU) is developed to select the high efficient link prediction algorithm (LPA) for node pairs to be predicted from the local similarity based and quasi-local similarity based LPAs. MRCU uses the rough set theory and granular computing to describe the similarity of LPAs, consequently the LPA selected with MRCU can share as much similarity as possible with the other base LPAs. Furthermore, SLPA adopts the branch and bound method to smartly cluster node pairs by adaptively selecting optimal LPA for given node pairs and excluding the ones with the least possibility of linking, and consequently the most reliable results of node pairs with the highest linkable possibility are acquired. The experimental results on three SN datasets confirm the validity of SLPA in selecting band spokesperson. [ABSTRACT FROM AUTHOR]