Bridging centrality: graph mining from element level to group level
Bridging centrality: graph mining from element level to group level
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DOI：
10.1145/1401890.1401934
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发表时间：
2008-08
期刊：
影响因子：
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通讯作者：
W. Hwang;Taehyong Kim;M. Ramanathan;A. Zhang
中科院分区：
文献类型：
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作者：
W. Hwang;Taehyong Kim;M. Ramanathan;A. Zhang
尽管网络作为现实世界系统的模型无处不在，从互联网、万维网到基因调控和科学合作，但只有有限数量的能够表征这些系统的指标可用。用于表征网络的现有指标具有广泛的特异性，并且缺乏对许多应用的选择性。本文的目的是识别并严格评估一个称为桥接中心性的度量，该度量对于识别网络中的桥具有高度选择性。与其他网络指标相比，网桥的属性是独特的。对于各种数据集，我们发现网络很容易受到破坏，但在有针对性地删除桥接节点时，网络很容易丢失结构完整性。还提出了一种新颖的图聚类方法，称为“桥切”，利用桥接边缘作为模块边界。桥切算法识别的模块比其他图聚类方法更有效。因此，桥接中心性是一种具有独特属性的网络度量，可以帮助包括系统生物学和国家安全应用在内的各个领域从元素到组级别的网络分析。
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.