Finding Network Motifs: A comparative study between ILP and Symmetric Rank-One NMF

This paper compares the performance of two popular methods, an integer linear programming (ILP) based method and a non-negative matrix factorization (NMF) based approximation, for identifying network motifs. Our analysis demonstrates that the ILP-based method is highly accurate but computationally expensive, whereas the NMF-based method is faster and more scalable but less accurate. The choice of method should depend on the specific research question and the size and complexity of the network being analyzed. We also highlight the need for further research to develop more efficient and accurate methods for identifying network motifs, especially for very large and complex networks. Understanding the significance of network motifs and their functions can lead to new therapeutic strategies and improved diagnostics for a wide range of diseases.In addition, we provide an easy-to-use Matlab code implementation for researchers to get started with analyzing their own network motifs. Our code is based on both the ILP-based method, and can be customized to suit the specific needs of the user. This implementation can help facilitate the wider adoption of network motif analysis and enable researchers to gain a deeper understanding of complex systems.