Network Motifs Are a Powerful Tool for Semantic Distinction
Network Motifs Are a Powerful Tool for Semantic Distinction
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网络主题是语义区分的强大工具
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
K. Weihe
中科院分区：
文献类型：
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作者：
Chris Biemann;Lachezar Krumov;Stefanie Roos;K. Weihe
模体是一种通用的网络分析技术，它在统计上将网络结构与网络上的副现象联系起来。这种技术已经在分子生物学中发展并成熟，在那里它已经成功地应用于各种类型的基于网络的化学和生物动力学。在早期，基序技术也被成功地应用于生物学之外的领域--社交网络、电子网络等等。米洛等人的结果表明，网络的基序签名在一定程度上因领域而异，但在一个领域内明显更同质。这一观察一直是本文提出的研究思路的出发点。更具体地说，我们不比较来自不同领域的网络，而是关注来自给定领域的网络。在一些特定领域的案例研究中，我们发现模体签名足以区分某些类别的网络。在本文中，我们总结了我们以前的工作，并提出了一些新的结果。特别是，在Biemann et al.（2012）中，我们发现自然语言和人工生成的语言可以通过同现图的模体签名相互区分。在此基础上，我们提出了工作的同现图，仅限于词类。我们发现，动词（和其他词类，如谓词）的共现图表现出强烈不同的主题签名，可以区分。为了证明这种方法的一般功能，我们提出了进一步的原创性工作的合著网络，对等流网络和邮件网络。
Motifs are a general network analysis technique, which statistically relates network structure to epiphenomena on the network. This technique has been developed and brought to maturity in molecular biology, where it has been successfully applied to network-based chemical and biological dynamics of various types. Early on, the motif technique has been successfully applied outside biology as well – to social networks, electrical networks, and many more. Results by Milo et al. showed that the motif signature of a network varies from realm to realm to some extent but is significantly more homogenous within a realm. This observation has been the starting point of the thread of research presented in this paper. More specifically, we do not compare networks from different realms but focus on networks from a given realm. In several case studies on particular realms, we found that motif signatures suffice to distinguish certain classes of networks from each other. In this paper, we summarize our previous work, and present some new results. In particular, in Biemann et al. (2012), we found that natural and artificially generated language can be distinguished from each other through the motif signatures of the co-occurrence graphs. Based on that, we present work on co-occurrence graphs that are restricted to word classes. We found that the co-occurrence graphs of verbs (and other word classes used like predicates) exhibit strongly different motif signatures and can be distinguished by that. To demonstrate the general power of the approach, we present further original work on co-authorship networks, peer-to-peer streaming networks, and mailing networks.