Network science involves the study of the properties, characteristics, and behaviours of networks. It explores topics such as network topology, network measures, community detection, centrality analysis, diffusion dynamics, and resilience. Network scientists use mathematical, statistical, and computational methods to analyse network data and gain insights into how networks are organized, how they evolve over time, and how they impact system behaviour.
Graph analytics, on the other hand, focuses on developing algorithms, techniques, and tools for extracting meaningful information from graphs. Graph analytics methods are used to analyse various aspects of graphs, such as node and edge attributes, connectivity patterns, and graph algorithms. Graph analytics techniques include graph traversal, pattern mining, clustering, link prediction, graph embedding, and visualization.
Both network science and graph analytics are interdisciplinary fields that draw on techniques from mathematics, statistics, computer science, and domain-specific knowledge to understand and analyse complex systems. They are used to model and analyse a wide range of networks, including social networks, biological networks, transportation networks, communication networks, and many others.
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