Relationship

Network adjacency matrix

Shows link presence or weight between every pair of network nodes.

Table
Relationship
Structure

What it answers

Where are connections present or strong?

Shape: Node-by-node connection grid
What it shows: Blocks, asymmetry and connection density.

Example

The question

Which colleagues are connected, and does the network have clear groups?

Working links between ten colleagues (adjacency matrix)

A grid with the same people on rows and columns; a filled square means the two work together. Filled squares cluster in two blocks along the diagonal: the two teams.

Illustrative data. It belongs to no company.

When to use it

Use it when

Dense networks or exact pairwise inspection.

Avoid it when

When paths and neighbourhood traversal are primary.

How to read it

Read row/column node names and cell values.

Note

An undirected network normally produces a symmetric matrix.

The data you need

Layout: Edge list plus node table
Fields: Source: text ID; Target: text ID; optional Weight: number
One row is: One relationship between two nodes. What is the grain?
Rules: Declare directed or undirected links. Handle duplicate edges and self-loops intentionally.
Optional: Separate Nodes table: NodeID,Label,Type. Include isolated nodes there.
Preparation: Resolve node identities and endpoint joins; define direction and duplicate/self-link handling. Aggregate weights only under a stated relationship or flow definition.
Units: Unweighted links encode presence. Weights need a unit; flow shares divide by stated source, destination or network totals, not an assumed common denominator.

An edge list: one row per link, from one node to another, often with a node table beside it.

Worked example

Undirected edgesA-B, A-C yield ones in[A, B],[B, A],[A, C],[C, A]. The mirrored cells reflect undirected links.

Source	Target
A	B
A	C
B	C

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