DocumentCode :
124174
Title :
Link Prediction Based on Multi-steps Resource Allocation
Author :
Zhifeng Wu ; Yaohui Li
Author_Institution :
Sch. of Inf. Technol. Eng., Tianjin Univ. of Technol. & Educ., Tianjin, China
Volume :
1
fYear :
2014
fDate :
11-14 Aug. 2014
Firstpage :
355
Lastpage :
360
Abstract :
Link prediction is a fundamental problem in social network analysis. The key technique in link prediction is to find an appropriate similarity measure between nodes of a network. Generally, external information besides the network topology is considered in many similarity measures. However, these external information is generally not available or not true. Usage of these external information may result an improper suggestion. Can we just use the information of the network topology to predict the miss links in the network? In this paper, firstly, we discussed the performance of 10 similarity indices, which only use network topology, on seven real networks. Secondly, for improving Resource Allocation index, which only used the common neighbors to calculate the resource of destination nodes gotten from source node, we proposed a new similarity measure called Multi-Steps Resource Allocation (MSRA). In MSRA, we used the information of multi-steps neighbors to transmit the resource from one node to another node. 2 steps neighbors, 3 steps neighbors and 4 steps neighbors are considered in this paper to be a balance in the performance and the computational complexity. Finally, to exhibit the power of MSRA in link prediction, we compare 10 various link prediction methods over 7 networks. The results show that our newly proposed MSRA measures outperform those ten measures on most datasets.
Keywords :
computational complexity; resource allocation; social networking (online); topology; MSRA; computational complexity; link prediction; multisteps neighbors; multisteps resource allocation; network topology; resource allocation index; similarity measure; social network analysis; Accuracy; Indexes; Measurement; Network topology; Prediction algorithms; Probes; Resource management; link prediction; multi-steps resource allocation; resource allocation; similarity measures;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on
Conference_Location :
Warsaw
Type :
conf
DOI :
10.1109/WI-IAT.2014.56
Filename :
6927565
Link To Document :
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