書誌事項
- タイトル別名
-
- Object Ranking in Evolutional Networks via Link Prediction
抄録
This paper proposes a framework to predict future significance or importance of nodes of a network through link prediction. The network can be of any kind, such as a co-authorship network where nodes are authors and co-authors are linked by edges. In this example, predicting significant nodes means to discover influential authors in the future. There are existing approaches to predicting such significant nodes in a future network and they typically rely on existing relationships between nodes. However, since such relationships are dynamic and would naturally change over time (e.g., new co-authorship continues to emerge), approaches based only on the current status of the network would have limited potentiality to predict the future. In contrast, our proposed approach first predicts future links between nodes by multiple supervised classifiers and applies the RankBoost algorithm for combining the predictions such that the links would lead to more precise predictions of a centrality (significance) measure of our choice. To demonstrate the effectiveness of our proposed approach, a series of experiments are carried out on the arXiv (HEP-Th) citation data set.
収録刊行物
-
- 人工知能学会論文誌
-
人工知能学会論文誌 27 (3), 223-234, 2012
一般社団法人 人工知能学会
- Tweet
詳細情報
-
- CRID
- 1390001205106714112
-
- NII論文ID
- 130001878760
-
- BIBCODE
- 2012TJSAI..27..223M
-
- ISSN
- 13468030
- 13460714
-
- 本文言語コード
- ja
-
- データソース種別
-
- JaLC
- Crossref
- CiNii Articles
-
- 抄録ライセンスフラグ
- 使用不可