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 features

Required list input.

Sampled neighbors

Required object input.

Target node

Required integer input.

Self weight

Required number input.

Demonstrate PinSage-style importance pooling from random-walk visit counts and node features. 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]

PinSage aggregation

The resulting pinsage aggregation 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 Breadth-First Traversal

  Visit every node reachable from a start node in breadth-first order over directed edges, visiting neighbors in ascending order and reporting each node's depth.

• 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.