Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | IJCAI International Joint Conference on Artificial Intelligence |
| Pages | 2268-2273 |
| Number of pages | 6 |
| DOIs | |
| Publication status | Published - 2011 |
| Externally published | Yes |
| Event | 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia Duration: 2011 Jul 16 → 2011 Jul 22 |
Other
| Other | 22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 |
|---|---|
| City | Barcelona, Catalonia |
| Period | 11/7/16 → 11/7/22 |
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ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Mining longitudinal network for predicting company value. / Jin, Yingzi; Lin, Ching Yung; Matsuo, Yutaka; Ishizuka, Mitsuru.
IJCAI International Joint Conference on Artificial Intelligence. 2011. p. 2268-2273.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Mining longitudinal network for predicting company value
AU - Jin, Yingzi
AU - Lin, Ching Yung
AU - Matsuo, Yutaka
AU - Ishizuka, Mitsuru
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84881068498&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881068498&partnerID=8YFLogxK
U2 - 10.5591/978-1-57735-516-8/IJCAI11-378
DO - 10.5591/978-1-57735-516-8/IJCAI11-378
M3 - Conference contribution
AN - SCOPUS:84881068498
SN - 9781577355120
SP - 2268
EP - 2273
BT - IJCAI International Joint Conference on Artificial Intelligence
ER -