Network motifs are recurrent and over-represented patterns having biological relevance. This is one of the important local properties of biological networks. Network motif discovery finds important applications in many areas such as functional analysis of biological components, the validity of network composition, classification of networks, disease discovery, identification of unique subunits etc. The discovery of network motifs is a computationally challenging task due to the large size of real networks, and the exponential increase of search space with respect to network size and motif size. This problem also includes the subgraph isomorphism check, which is Nondeterministic Polynomial (NP)-complete. Several tools and algorithms have been designed in the last few years to address this problem with encouraging results. These tools and algorithms can be classified into various categories based on exact census, mapping, pattern growth, and so on. In this study, critical aspects of network motif discovery, design principles of background algorithms, and their functionality have been reviewed with their strengths and limitations. The performances of state-of-art algorithms are discussed in terms of runtime efficiency, scalability, and space requirement. The future scope, research direction, and challenges of the existing algorithms are presented at the end of the study.

中文翻译：

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生物网络中网络基序发现的工具和算法的回顾。


网络主题是具有生物学相关性的重复出现且过度代表性的模式。这是生物网络的重要局部特性之一。网络基序发现在许多领域都有重要的应用，例如生物成分的功能分析、网络组成的有效性、网络分类、疾病发现、独特亚基的识别等。网络基序的发现是一项计算上具有挑战性的任务，因为它的数据量很大。真实网络的大小，以及搜索空间相对于网络大小和主题大小的指数增长。这个问题还包括子图同构检查，它是非确定性多项式（NP）完全的。过去几年，人们设计了多种工具和算法来解决这个问题，并取得了令人鼓舞的结果。这些工具和算法可以根据精确的人口普查、绘图、模式增长等分为不同的类别。在这项研究中，对网络主题发现的关键方面、背景算法的设计原理及其功能及其优点和局限性进行了回顾。从运行时效率、可扩展性和空间需求方面讨论了最先进算法的性能。研究的最后提出了现有算法的未来范围、研究方向和挑战。