Networkssimilar

Networkssimilar is a Python library designed to facilitate the comparison and analysis of network structures. It provides a suite of algorithms and tools for quantifying the similarity between two or more graphs. The library aims to offer a flexible and efficient way to determine how alike different network topologies are, which can be useful in various fields such as social network analysis, bioinformatics, and computer science.

The core functionality of networkssimilar revolves around graph similarity metrics. It implements several well-established measures, including graph edit distance, maximum common subgraph, and spectral similarity methods. These metrics allow users to assign a numerical score representing the degree of resemblance between networks. The library also supports different types of graphs, including directed and undirected, weighted and unweighted, and labeled or unlabeled networks.

Beyond direct similarity scores, networkssimilar can also assist in identifying corresponding nodes or subgraphs between similar networks. This can be achieved through techniques like graph matching or network alignment algorithms. By providing these capabilities, the library enables researchers and practitioners to uncover structural relationships, detect anomalies, and compare network evolution over time. The implementation prioritizes performance and ease of use, making it accessible for both novice and experienced users of network analysis tools.