• Journal of Internet Computing and Services
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• v.15 no.4
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• pp.103-110
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• 2014

In study we analyze the network about the legislators in Korean National Assembly. We focused on 17th National Assembly since there were full of important changes in composition. Mutual cooperation is necessary to pass the bills beyond their given mission in legislation. In order to find out the relationship of legislators based on the introducing bills, total 5728 bills in 17th National Assembly, we used bipartite network projection. We can find who is a highly influential legislator and the difference between a ruling party and a main opposition party in aspects of cooperative behavior.

• Journal of Digital Convergence
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• v.12 no.2
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• pp.11-25
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• 2014

The purpose of this study is analyzing the network of the National Assembly based on the bill data in 18th, 19th National Assembly lawmakers submit to Committees. By using bipartite projection we find out a strong committee and understand the relationship of committees. We focused on bills that ten or more of the legislators propose and compare between the first opposition party and the ruling party, which accounts for more than 80% of the overall structure of the National Assembly. We point out an influential legislator and committee in the network. This result presents which committees and lawmakers have a significant effect on process of legislation. This work gives a reasonable source as qualified to judge whether the committee and legislators group enact positively or not.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2030-2052
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• 2023

Analysis of a bipartite (two-mode) network is a significant research area to understand the formation of social communities, economic systems, drug side effect topology, etc. in complex information systems. Most of the previous works talk about a projection-based model or latent feature model, which predicts the link based on singular similarity. The projection-based models suffer from the loss of structural information in the projected network and the latent feature is hardly present. This work proposes a novel method for link prediction in the bipartite network based on an ensemble of composite similarities, overcoming the issues of model-based and latent feature models. The proposed method analyzes the structure, neighborhood nodes as well as latent attributes between the nodes to predict the link in the network. To illustrate the proposed method, experiments are performed with five real-world data sets and compared with various state-of-art link prediction methods and it is inferred that this method outperforms with ~3% to ~9% higher using area under the precision-recall curve (AUC-PR) measure. This work holds great significance in the study of biological networks, e-commerce networks, complex web-based systems, networks of drug binding, enzyme protein, and other related networks in understanding the formation of such complex networks. Further, this study helps in link prediction and its usability for different purposes ranging from building intelligent systems to providing services in big data and web-based systems.