Connecting Patterns Inspire Link Prediction in Complex Networks.

COMPLEXITY(2017)

引用 17|浏览38
暂无评分
摘要
Link prediction uses observed data to predict future or potential relations in complex networks. An underlying hypothesis is that two nodes have a high likelihood of connecting together if they sharemany common characteristics. The key issue is to develop different similarity-evaluating approaches. However, in this paper, by characterizing the differences of the similarity scores of existing and nonexisting links, we find an interesting phenomenon that two nodes with some particular low similarity scores also have a high probability to connect together. Thus, we put forward a new framework that utilizes an optimal one-variable function to adjust the similarity scores of two nodes. Theoretical analysis suggests that more links of low similarity scores (long-range links) could be predicted correctly by our method without losing accuracy. Experiments in real networks reveal that our framework not only enhances the precision significantly but also predicts more long-range links than state-of-the-art methods, which deepens our understanding of the structure of complex networks.
更多
查看译文
关键词
patterns inspire link prediction,networks,complex
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要