Tengslaneti

Tengslaneti is a term used in network science to denote a relational network that emphasizes the ties between entities rather than just the attributes of the entities themselves. In this sense, a tengslaneti models entities as nodes and the relationships between them as edges. Edges may be directed or undirected, and can carry weights that reflect the strength, frequency, or capacity of the relationship.

Nodes represent actors, objects, concepts, or places, while edges encode interactions such as collaboration, communication, or influence. The network can be static or dynamic, with edges appearing, disappearing, or changing weight over time. Analyses rely on graph-theoretic techniques to derive properties of the whole network and of individual nodes.

Common metrics include degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, as well as clustering coefficients and modularity. Connectivity, path length, and community structure help describe how information or influence spreads through the tengslaneti. Visualizations and statistical models are used to summarize and interpret the network.

Applications span social science, information science, biology, economics, and engineering. In sociology, tengslaneti can map kinship, friendship, or collaboration networks. In data science, it informs recommender systems, citation analysis, and hyperlink structures.

Constructing a tengslaneti requires data that link entities, such as surveys, transaction records, log files, or bibliographic databases. Preprocessing steps include entity resolution, edge extraction, normalization of edge types, and dealing with missing data. Ethical and privacy considerations are important when networks involve individuals.

Because networks are often large and dynamic, analysis can be computationally intensive and sensitive to sampling bias. Results should be interpreted with caution, acknowledging limitations in data quality, temporal dynamics, and model assumptions. The concept is widely used across disciplines, with variations in terminology reflecting disciplinary emphasis.