Python Local Clustering Coefficient Calculator

Calculator

Input mode

Target node

Input rule

Node label case

Low degree rule

Minimum absolute weight

Decimal places

Matrix labels

Extra isolated nodes

Graph data

Use one edge per line, one adjacency row per line, or matrix rows. Optional edge weights can be placed after two node names.

Example Data Table

Line	Edge	Meaning
1	A B	A is linked with B.
2	A C	A is linked with C.
3	B C	B is linked with C.
4	C D	C is linked with D.
5	D E	D is linked with E.
6	C E	C is linked with E.
7	B E	B is linked with E.

Formula Used

For an undirected simple graph, the local clustering coefficient of node v is:

C(v) = 2e(v) / [k(v)(k(v) - 1)]

Here, k(v) is the degree of node v. The value e(v) is the number of actual links between neighbors of v. The possible neighbor links equal k(v)(k(v) - 1) / 2. If k(v) is less than two, the coefficient is either zero or undefined, based on your selected rule.

How to Use This Calculator

Select the input mode matching your graph data.
Paste the edge list, adjacency list, or matrix.
Enter the target node you want to inspect.
Choose label handling, direction handling, and precision.
Press Calculate to view results above the form.
Use CSV or PDF buttons to save the current result.

Local Clustering Coefficient Overview

A local clustering coefficient measures how tightly the neighbors of one node connect with each other. It is useful in social networks, research maps, dependency graphs, communication patterns, and recommendation models. A high value means the selected node sits inside a dense neighborhood. A low value means its neighbors are mostly separated.

Why This Measure Matters

Network analysts use this value to detect community behavior. In a friend graph, it shows whether your contacts also know one another. In a citation graph, it can show tight academic groups. In a software graph, it can reveal modules that share many internal links. The metric is simple, but it gives quick insight into local structure.

What This Tool Accepts

This calculator supports edge lists, adjacency lists, and adjacency matrices. You can paste data from spreadsheets, scripts, or graph notes. The form also lets you add isolated nodes. That matters because isolated nodes affect average clustering when you choose the zero rule. The target node field highlights one node for detailed review.

How Results Are Interpreted

The coefficient ranges from zero to one for a simple undirected graph. A value of one means every neighbor pair is connected. A value of zero means no neighbor pair is connected. Nodes with fewer than two neighbors have no possible neighbor pair. You can treat them as zero, or exclude them from averages.

Advanced Review Options

The calculator reports degree, possible neighbor links, actual neighbor links, triangle count, graph density, average clustering, and transitivity. It also lists every node result. This makes checking data easier. You can compare the selected node with the entire graph. Download options help you save results for reports, audits, or class work.

Practical Workflow

Start with a small example before pasting a large network. Check that node names match exactly. Choose the input mode that matches your data. Use the directed option only when your source list contains one-way ties. Review warnings after calculation. Then export the summary when the result matches your expected graph.

Data Checks

Remove duplicate rows carefully when they are accidental before calculation starts. Keep labels consistent. Avoid hidden spaces and stray separators. Place isolated labels in the extra field for totals.

FAQs

What is a local clustering coefficient?

It measures how many links exist between a node's neighbors compared with how many could exist. The value helps describe local community density around one selected node.

What value range should I expect?

For a simple undirected graph, values usually range from 0 to 1. A value of 1 means every neighbor pair is connected.

Can I use an adjacency matrix?

Yes. Select adjacency matrix mode. Paste numeric rows. Add matrix labels if your first row does not already contain node names.

How are duplicate edges handled?

Duplicate edges are reduced to one simple undirected edge. This prevents repeated rows from inflating degree, triangles, or neighbor link counts.

How are self loops handled?

Self loops are ignored because this calculator uses a simple graph model. A self loop does not create a neighbor pair around the node.

What happens when degree is below two?

You can count the coefficient as zero, or mark it undefined. The undefined option excludes that node from average clustering.

Can I download results?

Yes. Use the CSV button for spreadsheet work. Use the PDF button for a compact printable summary of current inputs and results.

Does the calculator support weighted graphs?

It accepts optional weights and a minimum absolute threshold. Included links are then converted to a simple graph for clustering coefficient calculation.