Paper Info
Title
On Member Labelling in Social Networks
Abstract
Software agents are increasingly used to search for experts, recommend resources, assess opinions, and other similar tasks in the context of social networks, which requires to have accurate information that describes the features of the members of the network. Unfortunately, many member profiles are incomplete, which has motivated many authors to work on automatic member labelling, that is, on techniques that can infer the null features of a member from his or her neighbourhood. Current proposals are based on local or global approaches; the former compute predictors from local neighbourhoods, whereas the latter analyse social networks as a whole. Their main problem is that they tend to be inefficient and their effectiveness degrades significantly as the percentage of null labels increases. In this paper, we present Katz, which is a novel hybrid proposal to solve the member labelling problem using neural networks. Our experiments prove that it outperforms other proposals in the literature in terms of both effectiveness and efficiency.
Year
DOI
Venue
2015
10.1007/978-3-319-19222-2_41
ADVANCES IN COMPUTATIONAL INTELLIGENCE, PT II
Keywords
Field
DocType
Social networks,Member labelling,Hybrid approach,Neural networks
Social network,Computer science,Software agent,Neighbourhood (mathematics),Artificial intelligence,Labelling,Artificial neural network,Machine learning
Conference
Volume
ISSN
Citations 
9095
0302-9743
0
PageRank 
References 
Authors
0.34
12
3
Name
Order
Citations
PageRank
Rafael Corchuelo138949.87
Antonia M. Reina Quintero2196.45
Patricia Jiménez3143.99