101. Sampling based one-source link prediction in directed network
- Author
-
Liu Qinghe and Chen Ling
- Subjects
business.industry ,Computation ,Sampling (statistics) ,Machine learning ,computer.software_genre ,01 natural sciences ,010305 fluids & plasmas ,Set (abstract data type) ,Range (mathematics) ,Similarity (network science) ,0103 physical sciences ,Path (graph theory) ,Artificial intelligence ,Katz index ,010306 general physics ,business ,Link (knot theory) ,computer ,Algorithm ,Mathematics - Abstract
Link prediction in directed network is attracting growing interest among many network researchers. We propose a fast approach based on similarity to predict the links of the related nodes. In this method, we extended the Katz index to the directed network, and use the path sampling method to compute the approximated Katz index. By setting the appropriate size of the sampling, the error of similarity can be restricted to a given threshold range. Because only the information on the paths of the sampling set is required in computing the similarity score, the time cost for computation this algorithm is greatly reduced. Experimental results on real networks indicate that our algorithm can obtain results with higher accuracy in less time than other methods.
- Published
- 2017