Link prediction has received a great deal of attention from researchers. Most of the existing researches are based on the network topology but ignore the importance of its preference; for aggregating multiple pieces of information, they normally sum up them directly. In this paper, a path-based probabilistic model is proposed to estimate the potential connectivity between any two nodes. It takes carefully the effective influence of nodes and the dependency among paths between two fixed nodes into account. Furthermore, we formulate the connectivity of two inner-community nodes and that of two inter-community nodes. The qualitative analysis shows that the links between inner-community nodes are more likely to be predicted by the proposed model. The performance is verified on both the multi-barbell network and Lesmis network. Considering the proposed model’s practicability, we develop an algorithm that iterates over the adjacent matrix to simulate paths of different lengths, with the parameters automatically grid-searched. The results of the experiments show that the proposed model outperforms competitive methods.

链接预测已引起研究人员的广泛关注。现有的大多数研究都基于网络拓扑，但忽略了其优先级的重要性。为了汇总多条信息，他们通常直接将其汇总。在本文中，提出了一种基于路径的概率模型来估计任意两个节点之间的潜在连通性。它仔细考虑了节点的有效影响以及两个固定节点之间路径之间的依赖性。此外，我们制定了两个内部社区节点和两个社区间节点的连接性。定性分析表明，内部社区节点之间的联系更可能由所提出的模型预测。在多杠铃网络和Lesmis网络上均已验证了性能。考虑到提出的模型的实用性，我们开发了一种算法，该算法在相邻矩阵上进行迭代以模拟具有不同长度的路径，并自动对参数进行网格搜索。实验结果表明，该模型优于竞争方法。