Link Prediction in Social Networks with Edge Aging
| Autor: | Chilukuri K. Mohan, Ricky Laishram, Kishan G. Mehrotra |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Social network
business.industry Computer science Supervised learning 02 engineering and technology Machine learning computer.software_genre Evolving networks 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Enhanced Data Rates for GSM Evolution Link (knot theory) business computer |
| Zdroj: | ICTAI |
| DOI: | 10.1109/ictai.2016.0098 |
| Popis: | In social networks that change with time, an important problem is the prediction of new links that may be formed in the future. Existing works on link prediction have focused only on networks where links are permanent, an assumption that is not valid in many real world social networks. In many real world networks, in addition to new links being created, existing links also get removed. In this paper, we extend existing link prediction methods and apply a supervised learning algorithm to networks with non-permanent links. The results we obtain on Twitter @-mention networks show that our method performs very well in such networks. |
| Databáze: | OpenAIRE |
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