Mining longitudinal network for predicting company value

Yingzi Jin, Ching Yung Lin, Yutaka Matsuo, Mitsuru Ishizuka

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

Real-world social networks are dynamic in nature. Companies continue to collaborate, align strategically, acquire, and merge over time, and receive positive/negative impact from other companies. Consequently, their performance changes with time. If one can understand what types of network changes affect a company's value, he/she can predict the future value of the company, grasp industry innovations, and make business more successful. However, it often requires continuous records of relational changes, which are often difficult to track for companies, and the models of mining longitudinal network are quite complicated. In this study, we developed algorithms and a system to infer large-scale evolutionary company networks from public news during 1981-2009. Then, based on how networks change over time, as well as the financial information of the companies, we predicted company profit growth. This is the first study of longitudinal network-mining-based company performance analysis in the literature.

本文言語English
ホスト出版物のタイトルIJCAI International Joint Conference on Artificial Intelligence
ページ2268-2273
ページ数6
DOI
出版ステータスPublished - 2011
外部発表はい
イベント22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia
継続期間: 2011 7月 162011 7月 22

Other

Other22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
CityBarcelona, Catalonia
Period11/7/1611/7/22

ASJC Scopus subject areas

  • 人工知能

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