DocumentCode :
2984460
Title :
Predicting Links in Multi-relational and Heterogeneous Networks
Author :
Yang Yang ; Chawla, Niran ; Yizhou Sun ; Jiawei Hani
Author_Institution :
Dept. of Comput. Sci., Univ. of Notre Dame, Notre Dame, IN, USA
fYear :
2012
fDate :
10-13 Dec. 2012
Firstpage :
755
Lastpage :
764
Abstract :
Link prediction is an important task in network analysis, benefiting researchers and organizations in a variety of fields. Many networks in the real world, for example social networks, are heterogeneous, having multiple types of links and complex dependency structures. Link prediction in such networks must model the influence propagating between heterogeneous relationships to achieve better link prediction performance than in homogeneous networks. In this paper, we introduce Multi-Relational Influence Propagation (MRIP), a novel probabilistic method for heterogeneous networks. We demonstrate that MRIP is useful for predicting links in sparse networks, which present a significant challenge due to the severe disproportion of the number of potential links to the number of real formed links. We also explore some factors that can inform the task of classification yet remain unexplored, such as temporal information. In this paper we make use of the temporal-related features by carefully investigating the issues of feasibility and generality. In accordance with our work in unsupervised learning, we further design an appropriate supervised approach in heterogeneous networks. Our experiments on co-authorship prediction demonstrate the effectiveness of our approach.
Keywords :
information networks; pattern classification; probability; unsupervised learning; MRIP probabilistic method; classification task; coauthorship prediction; dependency structure; heterogeneous network; link prediction; multirelational influence propagation; multirelational network; network analysis; social network; sparse network; unsupervised learning; Correlation; Diseases; Equations; Genetics; Integrated circuit modeling; Time series analysis; Unsupervised learning; Heterogeneous Network; Link Prediction; Temporal Analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining (ICDM), 2012 IEEE 12th International Conference on
Conference_Location :
Brussels
ISSN :
1550-4786
Print_ISBN :
978-1-4673-4649-8
Type :
conf
DOI :
10.1109/ICDM.2012.144
Filename :
6413854
Link To Document :
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