Authors:
C. P. M. T. Muniz
;
R. Choren
and
R. R. Goldschmidt
Affiliation:
Military Institute of Engineering, Brazil
Keyword(s):
Link Prediction, Social Networks, Weighted Graphs.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Discovery and Information Retrieval
;
Knowledge Management
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Sensor Networks
;
Signal Processing
;
Society, e-Business and e-Government
;
Soft Computing
;
Symbolic Systems
;
User Profiling and Recommender Systems
;
Web Information Systems and Technologies
Abstract:
For the last years, a considerable amount of attention has been devoted to the research about the link prediction (LP) problem in complex networks. This problem tries to predict the likelihood of an association between two not interconnected nodes in a network to appear in the future. Various methods have been developed to solve this problem. Some of them compute a compatibility degree (link strength) between connected nodes and apply similarity metrics between non-connected nodes in order to identify potential links. However, despite the acknowledged importance of temporal data for the LP problem, few initiatives investigated the use of this kind of information to represent link strength. In this paper, we propose a weighting criterion that combines the frequency of interactions and temporal information about them in order to define the link strength between pairs of connected nodes. The results of our experiment with traditional weighted similarity metrics in ten co-authorship netw
orks confirm our hypothesis that weighting links based on temporal information may, in fact, improve link prediction. Proposed criterion formulation, experimental procedure and results from the performed experiment are discussed in detail.
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