NDLI: Bridging centrality: graph mining from element level to group level

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Bridging centrality: graph mining from element level to group level

Content Provider	ACM Digital Library
Author	Kim, Taehyong Ramanathan, Murali Hwang, Woochang Zhang, Aidong
Abstract	Despite the pervasiveness of networks as models for real world systems ranging from the Internet, the World Wide Web to gene regulation and scientific collaborations, only a limited number of metrics capable of characterizing these systems are available. The existing metrics for characterizing networks have broad specificity and lack the selectivity for many applications. The purpose of this paper is to identify and critically evaluate a metric, termed bridging centrality, which is highly selective for identifying bridges in networks. The properties of bridges are unique compared to the other network metrics. For a diverse range of data sets, we found that networks are highly susceptible to disruption but robust to loss structural integrity upon targeted deletion of bridging nodes. A novel graph clustering approach, termed `bridge cut', utilizing bridging edges as module boundary is also proposed. The modules identified by the bridge cut algorithm are more effective than the other graph clustering methods. Thus, bridging centrality is a network metric with unique properties that may aid in network analysis from element to group level in various areas including systems biology and national security applications.
Starting Page	336
Ending Page	344
Page Count	9
File Format	PDF
ISBN	9781605581934
DOI	10.1145/1401890.1401934
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-08-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Bridging centrality Graph clustering
Content Type	Text
Resource Type	Article

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