Authors:
James N. K. Liu
1
;
Yu-Lin He
2
;
Yan-Xing Hu
1
;
Xi-Zhao Wang
2
and
Simon C. K. Shiu
1
Affiliations:
1
The Hong Kong Polytechnic University, Hong Kong
;
2
College of Mathematics and Computer Science and Hebei University, China
Keyword(s):
OWA Operator, Link Prediction, Social Network Analysis, Ensemble.
Related
Ontology
Subjects/Areas/Topics:
Ensemble Methods
;
Pattern Recognition
;
Theory and Methods
Abstract:
The objective of link prediction for social network is to estimate the likelihood that a link exists between
two nodes x and y. There are some well-known local information-based link prediction algorithms (LILPAs)
which have been proposed to handle this essential and crucial problem in the social network analysis. However,
they can not adequately consider the so-called local information: the degrees of x and y, the number
of common neighbors of nodes x and y, and the degrees of common neighbors of x and y. In other words,
not any LILPA takes into account all the local information simultaneously. This limits the performances of
LILPAs to a certain degree and leads to the high variability of LILPAs. Thus, in order to make full use of
all the local information and obtain a LILPA with highly-predicted capability, an ordered weighted averaging
(OWA) operator based link prediction ensemble algorithm (LPEOWA) is proposed by integrating nine different
LILPAs with aggregation weights which ar
e determined with maximum entropy method. The final experimental
results on benchmark social network datasets show that LPEOWA can obtain higher prediction accuracies
which is measured by the area under the receiver operating characteristic curve (AUC) in comparison with
nine individual LILPAs.
(More)