Performances and Characteristics of DIGRank, Ranking in the Incomplete Networks

• Published in: 2011 IEEE 11th International Conference on Data Mining pp. 1182 - 1187
• Main Authors: Xiang Niu, Lusong Li, Xiaobing Xiong, Tkach, D., He Li, Ke Xu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2011
• Subjects: Accuracy; Correlation; Electronic mail; Error analysis; Fans; Neodymium; Online Social Network; PageRank; Scale-free; Small-world; Social network services
• ISBN: 1457720752; 9781457720758
• ISSN: 1550-4786; 2374-8486
• DOI: 10.1109/ICDM.2011.117

Page Rank has been widely used in ranking retrieval results on the web, finding the top influential papers in citation networks or detecting valuable users in online social networks. However, in practice, it is usually hard to obtain a complete structure of any above networks to rank nodes. Thus, some researchers have begun to explore how to get estimated ranks efficiently without acquiring the whole network. They have proposed some approximating methods, however, it is difficult to determine which method is the best one or which is suitable to a certain application. In this case, we set experiments in small-world and scale-free generated networks to certify the feasibility and characteristics of four approximating methods. We also use eleven real networks to mention different optimal conditions for these methods. We find the DIG Rank method performs better than other local estimation methods in almost every given sub graph. Besides, Mean field approach method tends to perform well in networks that have low average shortest path length, small amount of nodes with the same low in degree, or weak community structure. Finally, we apply the most versatile method DIG Rank to Sina micro-blog website to precisely classify users in a group as elites, grassroots or mummy users.