Netgreining

Netgreining is a field of study and practice that focuses on the analysis of networks and network data. In Norwegian usage, the term covers methods for understanding how the components of a network are connected, how information, goods, or diseases propagate, and how the network’s structure affects performance and resilience. The scope includes computer networks, social networks, transportation grids, supply chains, and knowledge graphs.

Core concepts include nodes (entities) and edges (relationships), edge weights, and various centrality and connectivity metrics. Analysts study community structure, motifs, diffusion processes, and robustness to disruptions. Methods combine graph theory, statistics, data mining, and machine learning, including graph neural networks, as well as simulation and visualization.

Data sources are diverse: network logs, telemetry, social media data, sensor networks, and public datasets. Privacy and ethical considerations are important, with practices like anonymization and access controls guiding work.

Applications span network design and optimization, performance monitoring, cybersecurity, outage and fault detection, traffic planning, epidemiological modeling, and social science research. Challenges include handling very large and dynamic networks, data quality and completeness, changing network structure, and integrating heterogeneous data.

The term is used across academia, industry, and public administration in Norway. Some contexts distinguish nettreviksanalyse or nettverksanalyse as synonyms, while netgreining emphasizes the analytic and inferential aspects of network data. For related topics, see network analysis, graph theory, and data science.