Name
Title | ||
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A naïve Bayes model based on overlapping groups for link prediction in online social networks |
Abstract | ||
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Link prediction in online social networks is useful in numerous applications, mainly for recommendation. Recently, different approaches have considered friendship groups information for increasing the link prediction accuracy. Nevertheless, these approaches do not consider the different roles that common neighbors may play in the different overlapping groups that they belong to. In this paper, we propose a new approach that uses overlapping groups structural information for building a naïve Bayes model. From this proposal, we show three different measures derived from the common neighbors. We perform experiments for both unsupervised and supervised link prediction strategies considering the link imbalance problem. We compare sixteen measures in four well-known online social networks: Flickr, LiveJournal, Orkut and Youtube. Results show that our proposals help to improve the link prediction accuracy.
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Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2695664.2695719 | SAC 2015: Symposium on Applied Computing
Salamanca
Spain
April, 2015 |
Keywords | Field | DocType |
social networks | Social network,Friendship,Naive Bayes classifier,Computer science,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-3196-8 | 4 | 0.40 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jorge Carlos Valverde-Rebaza | 1 | 79 | 8.11 |
| Alan Valejo | 2 | 15 | 4.60 |
| Lilian Berton | 3 | 16 | 7.82 |
| Thiago de Paulo Faleiros | 4 | 21 | 2.99 |
| Alneu de Andrade Lopes | 5 | 275 | 29.00 |