链接(几何体)
计算机科学
相似性(几何)
计算
动力系统理论
复杂网络
度量(数据仓库)
索引(排版)
线性预测
任务(项目管理)
线性动力系统
数据挖掘
算法
人工智能
线性系统
数学
工程类
图像(数学)
物理
数学分析
万维网
系统工程
量子力学
计算机网络
作者
Huajian Gao,Jianbin Huang,Qiang Cheng,Hui Sun,Baoli Wang,He Li
标识
DOI:10.1016/j.physa.2019.121397
摘要
Link prediction has attracted increasing research attention recently, which aims to predict missing links in complex networks. However, the existing link prediction methods are primarily based on network structures alone, which are incapable of capturing the dynamics defined on top of the fixed network structures. In this paper, we introduce a linear dynamical response-based similarity measure between nodes into link prediction task. To address the efficiency problem, we design a new iterative procedure to avoid the explicit computation of linear dynamical response (LDR) index. Empirically, we conduct extensive experiments on real networks from various fields. The results show that LDR index leads to promising predicting performance for link prediction.
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