nodelinkverkot

Node-link networks, also referred to as node-link graphs, are a fundamental representation in network science where entities are modeled as nodes (or vertices) and their relationships as links (or edges). The structure can be directed or undirected and may include weighted edges that encode the strength, capacity, or frequency of the connection. This form of representation is central to graph theory and is used to study how the arrangement of connections influences system behavior.

Nodes and links in a node-link network can be organized in various ways. In practice, the same network can be described using adjacency lists, adjacency matrices, or edge lists. The data are often stored or exchanged in formats such as GraphML, GEXF, or JSON-based schemas, and graph databases like Neo4j enable querying and traversal of node-link structures.

Analyses of node-link networks typically focus on structural properties and dynamics. Common measures include degree distribution, shortest path length, betweenness and closeness centrality, and clustering coefficients. Community detection aims to identify groups of nodes with dense internal connections. Temporal networks extend the model by incorporating changes over time, while multiplex networks represent multiple types of links among the same set of nodes.

Visualization commonly employs node-link diagrams, where nodes are depicted as points and links as lines. Force-directed layouts help reveal clusters and patterns but can struggle with very large networks, leading to interpretability challenges. Node-link networks are widely used to model social, biological, technological, and information systems, offering insight into connectivity, resilience, and diffusion processes. Limitations include scalability, data quality, and potential misinterpretation of visual cues.