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Network analysis of online bidding activity
1School of Physics and Center for Theoretical Physics, Seoul National University, Seoul 151-747, Korea.
Summary
Network theory reveals complex bidder interactions in online auctions. Hierarchical clustering of these interactions offers a new way to categorize auction items, differing from traditional schemes.
Area of Science:
- Computer Science
- Network Theory
- E-commerce Analytics
Background:
- Digital media has shifted commercial transactions online, with online auctions being a prime example.
- Online auctions exhibit complex bidding patterns due to unbounded participants and instantaneous responses, unlike traditional offline transactions.
Purpose of the Study:
- To analyze bidder interaction patterns in online auctions using network theory.
- To compare a network-based item classification with traditional schemes.
Main Methods:
- Application of network theory to model bidder interactions for the same item.
- Analysis of the resulting bidder network using hierarchical clustering algorithms.
- Construction of a dendrogram for item subcategories based on clustering.
Main Results:
- A bidder interaction network was constructed and analyzed.
- Hierarchical clustering yielded a dendrogram for item subcategories.
- The network-derived classification was compared to traditional classification schemes.
Conclusions:
- Network theory provides a novel approach to understanding online auction dynamics.
- The hierarchical clustering of bidder interactions offers an alternative item categorization method.
- Differences between network-based and traditional classifications have significant implications for understanding online markets.
