Network Adjacency Matrix Builder — Free Matrix to Graph Tool

This tool builds an adjacency matrix from a network’s nodes and edges, and draws the graph alongside it. An adjacency matrix is the table form of a network: rows and columns are nodes, and a 1 marks a connection between that row’s node and that column’s node. Edges here are undirected, so A–B counts both ways and the matrix is symmetric. Matrices and drawings show the same network differently: the drawing reveals clusters and hubs at a glance, while the matrix feeds computation — degrees, paths, and centrality all start from this table.

How to use it

List nodes, comma-separated.
List edges, one per line, as NodeA-NodeB.
Click Build matrix & graph.
Read the matrix: 1 means connected, 0 means not.
Check the drawing against the matrix for mistakes.

Worked example

A four-person team has edges Ana–Bo, Bo–Cy, Cy–Dan, and Ana–Dan — a ring. The matrix shows 1s on the ring connections and 0s on the diagonals (Ana–Cy, Bo–Dan), and the drawing shows a square cycle. If the team adds a Bo–Dan edge, the matrix gains two 1s and the drawing gains a diagonal, turning the ring into a network with a shortcut. A student who builds both forms learns the translation skill every network analysis needs: the same structure, two representations, each good for different questions.

Honest limits

The tool handles undirected, unweighted edges only: directed relationships (A follows B, but B does not follow A), weighted ties (strong versus weak), and multi-edges are all flattened to a single symmetric 1, which misrepresents networks where direction or strength matters. Unknown nodes in edge lists are ignored rather than reported loudly, so a typo in a node name silently drops an edge — check the drawing for missing links. For directed, weighted, or large networks, use dedicated network software where those features are first-class.

Who it is for

This tool is for students learning network representations, researchers sketching a small collaboration or citation network, and teachers showing the matrix-graph equivalence. It is a learning bridge to network analysis, not a replacement for it.

Going further

Read the matrix for structure the drawing hides: a row of all 1s is a hub connected to everyone, a block of 1s is a dense subgroup, and a row of 0s is an isolate that the drawing may place unobtrusively at the edge. Count degrees from the matrix (row sums) before trusting visual impressions, because circle layouts can make peripheral nodes look central and vice versa. When you move to real network software, the adjacency matrix you built here is already the input format most tools accept — export it, in spirit, as a table, and the drawing becomes the first figure of a network study rather than a classroom exercise. Start every network description with the degree of each node, computed from the matrix, before describing any pattern the drawing suggests. Degrees are objective: a node with degree seven is a hub no matter how the layout draws it, while a node that looks central in a circle layout may have degree two. Report the degree distribution in words — how many isolates, how many leaves, the maximum degree — because those few numbers constrain every later claim about cohesion, influence, or vulnerability. If removing one node would split the network, say so explicitly: that single point of failure is often the most actionable finding in a small network study, whether the network is people, papers, or infrastructure. Layouts persuade; degrees prove. For directed networks, remember that this symmetric matrix is only half the story: who points to whom matters as much as who connects to whom, so record direction in your notes even though this tool flattens it, and move to directed software before publishing any claim about influence, hierarchy, or flow through the network.

Embed this tool on your website — free

Copy the code below to add this working tool to your own page. A small credit link stays with it — that’s all we ask.