LinkModell

LinkModell is a generic term used in German-language literature to describe a framework for modeling relational structures by representing entities as nodes and their relationships as links. In this approach, a network is formalized as a graph G = (V, E) where V is the set of entities and E is the set of edges; edges may be directed or undirected and may carry weights and attributes. The model emphasizes the prediction and analysis of links, rather than only the properties of individual nodes, and often combines statistical, probabilistic, and machine learning methods to estimate the presence or strength of links.

Key concepts include link prediction, link attributes, temporal dynamics, and multi-relational or heterogeneous networks. Variants may extend to multiplex networks where different types of relations are represented by separate edge layers, or to dynamic networks where edges appear and disappear over time.

Common methods used within LinkModell include feature-based models such as logistic regression or gradient boosting on features derived from node attributes and local network structure, matrix factorization approaches, and contemporary graph representation learning techniques such as node embeddings and graph neural networks. Evaluation typically relies on predictive metrics for link existence and link quality, such as AUC, precision, recall, or mean reciprocal rank.

Applications span social networks, bibliometric analyses, biology, supply chain risk, and information retrieval. The approach is valued for its emphasis on relationships and its ability to integrate heterogeneous data, but faces challenges including data sparsity, scalability for large networks, and interpretability of complex models.

In literature, LinkModell is used as a broad label for several relational modeling approaches; precise definitions vary by domain. It is often discussed alongside related concepts such as link prediction, relational learning, and network analysis.