EVA is a multidisciplinary research project combining information extraction, information visualization, and social network analysis techniques to bring greater transparency to the public disclosure of inter-relationships between corporations.

You can find out more about this project by reading the following two publications and running the corresponding visualization tools.

K. Norlen, G. Lucas, M. Gebbie, and J. Chuang. EVA: Extraction, Visualization and Analysis of the Telecommunications and Media Ownership Network. Proceedings of International Telecommunications Society 14th Biennial Conference (ITS2002), Seoul Korea, August 2002.
Abstract: We present EVA, a prototype system for extracting, visualizing, and analyzing corporate ownership information as a social network. Using probabilistic information retrieval and extraction techniques, we automatically extract ownership relationships from heterogeneous sources of online text, including corporate annual reports (10-Ks) filed with the U.S. Securities and Exchange Commission (SEC). A browser-based visualization interface allows users to query the relationship database and explore large networks of companies. Applying the system and methodology to the telecommunications and media industries, we construct an ownership network with 6,726 relationships among 8,343 companies. Analysis reveals a highly clustered network, with over 50% of all companies connected to one another in a single component. Furthermore, ownership activity is highly skewed: 90% of companies are involved in no more than one relationship, but the top ten companies are parents for over 24% of all relationships. We are also able to identify the most influential companies in the network using social network analysis metrics such as degree, betweenness, cutpoints, and cliques. We believe this methodology and tool can aid government regulators, policy researchers, and the general public to interpret complex corporate ownership structures, thereby bringing greater transparency to the public disclosure of corporate inter-relationships.

Paper
Presentation
Visualization (no longer being maintained) (Windows & unix: Download the Java Plug-In from Sun)
Dataset

G. Lucas, M. Gebbie, K. Norlen and J. Chuang. Improving Transparency: Extracting, Visualizing, and Analyzing Corporate Relationships from SEC 10-K Documents. 7th International Conference on Technology Policy and Innovation, Monterrey Mexico, June 2003. Extended version to appear in International Journal on Technology Policy and Management.
Abstract: We present a system to extract, visualize, and analyze inter-corporation relationships disclosed by public companies in their annual reports to the U.S. Securities and Exchange Commission (SEC). In improving the transparency of these disclosures, we allow policy makers, analysts, investors and the general public to analyze these relationships at both the firm level and the industry level. Using probabilistic information retrieval and extraction techniques, we automatically extract 45,000 relationships between 26,000 companies from over 15 gigabytes of SEC 10-K documents. These relationships range from ownerships, agreements, and personal connections to competition and legal disagreements. A visual interface allows exploratory study and analysis of this large relationship dataset. Using social network analytic techniques, we find a highly interconnected economy, where 97% of companies involved in relationships are connected to each other, and many central companies in the relationship network come from the finance sector.

Paper
Visualization (no longer being maintained) (Windows & unix: Download the Java Plug-In from Sun)

G. Lucas, M. Gebbie, K. Norlen and J. Chuang. Improving Transparency: Extracting, Visualizing, and Analyzing Corporate Relationships from SEC 10-K Documents. 7th International Conference on Technology Policy and Innovation, Monterrey Mexico, June 2003. [PDF] Extended version to appear in International Journal on Technology Policy and Management.
Principal Investigator
John Chuang, UC Berkeley School of Information Management & Systems

Research Assistants
Mike Gebbie
Gabe Lucas
Kim Norlen

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