Abstract
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.
Original language | English (US) |
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Title of host publication | Proceedings - 2016 IEEE 28th International Conference on Tools with Artificial Intelligence, ICTAI 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 606-613 |
Number of pages | 8 |
ISBN (Electronic) | 9781509044597 |
DOIs | |
State | Published - Jan 11 2017 |
Event | 28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016 - San Jose, United States Duration: Nov 6 2016 → Nov 8 2016 |
Other
Other | 28th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2016 |
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Country/Territory | United States |
City | San Jose |
Period | 11/6/16 → 11/8/16 |
Keywords
- Artificial intelligence
- Link prediction
- Machine learning
- Social network
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications