Use this graph operation to transform, inspect, classify, or aggregate a graph whose directedness, edge semantics, weights, node identifiers, and duplicate-edge policy are defined.

Node scores

Required list input.

Neighbor scores

Required object input.

Self weight

Required number input.

Combine a node's class scores with weighted neighbor scores and predict a class. The tool evaluates the supplied inputs together and returns the named outputs below; it does not infer omitted operating conditions or change the units shown.^[1]

Classification

The resulting classification returned as an object.

• Graph results depend on representation and conventions for self-loops, parallel edges, direction, isolated nodes, weights, normalization, and traversal order. Learned embeddings additionally depend on sampling and training parameters.
• Use finite inputs in the displayed units and preserve more precision than the final presentation requires. Independently verify safety-critical, financial, compliance, or production decisions.

Validate node and edge counts before and after transformation, test small known graphs, and preserve an explicit graph schema with algorithm parameters.

• Node2Vec++ Transition Demonstrator

  Demonstrate weighted node2vec-style transitions with a threshold for strong previous-node connections.

• Graph Depth-First Traversal

  Visit every node reachable from a start node in iterative depth-first preorder over directed edges, descending into the smallest-numbered neighbor first and reporting each node's depth.

• Dijkstra Shortest Path Calculator

  Find shortest paths from a start node over non-negative weighted directed edges given as [from, to, weight] rows.