Abstract
In the United States, Obama's presidency administration has moved forward with Open Government Initiative to open public data. Similarly, Tim Berners-Lee, the inventor of the World Wide Web, has led data.gov.uk in England. With the active movement of these global governments, open public data have let citizens know government policies clearly. However, since it is not trivial for people to clearly understand raw public data, active studies have carried out for good visualization. As an example, in 2009, Andrew Odewahn showed how to visualize U.S. Senate social graphs from 1991 until 2009. Given a polling data set in public, a graph consisting of a set of nodes with edges could be created, where each node is a senator and the edge between two nodes represents the similar voting behavior of the senators. However, since such a graph is considerably dense and complex, it is hard to visualize and also figure out the meaning of the graph. Toward this problem, in this paper, we propose novel graph visualization techniques based on friend matching and hierarchical matching. In our empirical study, we applied our visualization algorithms to the voting data of the 18th National Assembly in Korea, Senate bills in U.S., and U.N. voting data. Then, we showed the effectiveness of our proposed methods through user test.