Create Graph

The Scope allows you to create edges between the source nodes (Objects in the selection of the currently active Type) and target nodes (Objects referenced by the indicated path in the Scope).

Network  

The currently available network algorithms are:

AlgorithmDescription
Degree CentralityThe number of links of one node to and from other nodes. More info.
Shortest pathThe distance between one or a group of nodes (From) to any or another (Target) node or a group of nodes. More info.

In nodegoat it is also possible to highlight node relevance within shortest paths; calculate shortest path with a mode of betweenness centrality.

Betweenness centralityThe number of times a node lies on the shortest paths between other nodes. More info.
Closeness centralityThe average of the length of the shortest paths between a node and all other nodes. More info.
Closeness eccentricityThe sum of the length of the shortest paths between a node and all other nodes. More info.
Clustering coefficientThe degree to which nodes cluster together. More info.
PagerankThe number and quality of links to a node to determine a rough estimate of its importance. More info.

Similarity  

With the support of vectors, it is possible to embed vectorised data that results from a machine learning model (e.g. large language models, computer vision, natural language processing) and use metrics for evaluating their similarity. The currently available similarity algorithms are:

AlgorithmDescription
Vector distanceThe calculated similarity between source and target vectors based on a distance measure (e.g. euclidean_distance). The algorithm can be applied using two different approaches:
  • Connected: Select the source and target vectors, and use the Scope to create a network graph between the vectors. The selected target vector has to be at the end in the selection of the Scope. The calculated distance is based on similarities between vectors when they are also part of the network. Weighting is available in this approach.
  • Disconnected: Select the source and target vectors, and disregard any aspect of the network. The calculated distance is purely based on similarities in the vectors.

Weight  

When applicable to the algorithm you can indicate whether you want to make use of the graph's weight. A weight is the total edge length between two nodes and can be based on:

  • Duplicate edges becoming the one edge.
  • Weighting collapsed nodes and source/target nodes using the Conditions.
  • Combination of both.
WeightDescription
To closenessEdges add to a shorter total edge length (lower weight).
To distanceEdges add to a longer total edge length (higher weight).