multiaspect

Multiaspect refers to the property of entities or models that are described by multiple independent dimensions or aspects. In information science, a multiaspect approach allows representing complex objects whose identity or behavior cannot be captured by a single attribute.

In graph theory and network modeling, multiaspect graphs (MAGs) are a formalism for networks that incorporate multiple orthogonal dimensions—such as time, space, and semantic type—into their structure. Each aspect defines a finite set of values; a node is a vector selecting one value from each aspect, and edges connect node vectors and may carry additional dimension-specific information. This enables simultaneous representation of, for example, a person’s presence in different locations over time, or various kinds of relationships among entities.

Applications include social networks with time and place, transportation or logistics networks where nodes carry location and schedule, biomedical networks that combine patient, condition, and treatment dimensions, and knowledge graphs that fuse type, provenance, and context. MAGs support queries that cross dimensions, such as identifying all interactions of a given individual within a time window or a location.

Compared with single-aspect graphs, MAGs offer richer expressive power at the cost of higher complexity, both in data collection and computation. Common challenges include data sparsity, scalability, and the need for specialized algorithms to perform search, clustering, or path-finding in multi-dimensional spaces. Related concepts include temporal graphs, hypergraphs, and multidimensional databases.