Mining longitudinal network for predicting company value

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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 languageEnglish
Title of host publicationIJCAI International Joint Conference on Artificial Intelligence
Number of pages6
Publication statusPublished - 2011
Externally publishedYes
Event22nd International Joint Conference on Artificial Intelligence, IJCAI 2011 - Barcelona, Catalonia
Duration: 2011 Jul 162011 Jul 22


Other22nd International Joint Conference on Artificial Intelligence, IJCAI 2011
CityBarcelona, Catalonia

ASJC Scopus subject areas

  • Artificial Intelligence


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