We present a system to extract, visualize, and analyze inter-corporation relationships disclosed ... more 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 a dataset of 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. Information visualization and social network analytic techniques can then be applied to explore and analyze the dataset. 7 1
An important component of simulation-based network research is the selection of nodes to a member... more An important component of simulation-based network research is the selection of nodes to a member group, such as receivers in a multicast group or web clients in a content delivery network. In a seminal paper, Philips et al. introduce an algorithm for generating member groups with different degrees of affinity (clusteredness) and show that affinity can have a significant effect on multicast efficiency. Subsequent studies applying this algorithm have all used the algorithm's input parameter as a method for classifying and comparing affinity groups. In this paper, we propose several distance-and expansion-based analysis metrics and find them to be better measurements of the true affinity of member groups. In three separate case studies (multicast, replica placement, and sensor networks), we demonstrate the benefit of classifying member groups by their true affinity in order to predict network performance variation. By systematizing techniques for measuring affinity, we open the door for more realistic and reproducible research in studies employing affinity-based member selection techniques.
We present EVA, a prototype system for extracting, visualizing, and analyzing corporate ownership... more 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 2 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.
We present a system to extract, visualize, and analyze inter-corporation relationships disclosed ... more 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 a dataset of 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. Information visualization and social network analytic techniques can then be applied to explore and analyze the dataset. 7 1
An important component of simulation-based network research is the selection of nodes to a member... more An important component of simulation-based network research is the selection of nodes to a member group, such as receivers in a multicast group or web clients in a content delivery network. In a seminal paper, Philips et al. introduce an algorithm for generating member groups with different degrees of affinity (clusteredness) and show that affinity can have a significant effect on multicast efficiency. Subsequent studies applying this algorithm have all used the algorithm's input parameter as a method for classifying and comparing affinity groups. In this paper, we propose several distance-and expansion-based analysis metrics and find them to be better measurements of the true affinity of member groups. In three separate case studies (multicast, replica placement, and sensor networks), we demonstrate the benefit of classifying member groups by their true affinity in order to predict network performance variation. By systematizing techniques for measuring affinity, we open the door for more realistic and reproducible research in studies employing affinity-based member selection techniques.
We present EVA, a prototype system for extracting, visualizing, and analyzing corporate ownership... more 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 2 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.
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Papers by Gabriel Lucas