Link prediction problem has increasingly become prominent in many domains
such as social network analyses, bioinformatics experiments, transportation
networks, criminal investigations and so forth. A variety of techniques has
been developed for link prediction problem, categorized into 1) similarity
based approaches which study a set of features to extract similar nodes; 2)
learning based approaches which extract patterns from the input data; 3)
probabilistic statistical approaches which optimize a set of parameters to
establish a model which can best compute formation probability. However,
existing literatures lack approaches which utilize strength of each approach by
integrating them to achieve a much more productive one. To tackle the link
prediction problem, we propose an approach based on the combination of first
and second group methods; the existing studied works use just one of these
categories. Our two-phase developed method firstly determines new features
related to the position and dynamic behavior of nodes, which enforce the
approach more efficiency compared to approaches using mere measures. Then, a
subspace clustering algorithm is applied to group social objects based on the
computed similarity measures which differentiate the strength of clusters;
basically, the usage of local and global indices and the clustering information
plays an imperative role in our link prediction process. Some extensive
experiments held on real datasets including Facebook, Brightkite and HepTh
indicate good performances of our proposal method. Besides, we have
experimentally verified our approach with some previous techniques in the area
to prove the supremacy of ours.

提出一种结合第一和第二组方法的两阶段方法，其中第一阶段确定与节点位置和动态行为相关的新特征，第二阶段应用子空间聚类算法对社会对象进行分组，以区分集群的强度，在 Facebook、Brightkite 和 HepTh 等真实数据集上进行了广泛的实验，并与该领域的一些先前技术进行了实验验证，证明了该方法的优越性。