spektra

spectral graph theory library

In mathematics, spectral graph theory is the study of the properties of a graph in relationship to the characteristic polynomial, eigenvalues, and eigenvectors of matrices associated with the graph, such as its adjacency matrix or Laplacian matrix.

Why is this needed?

The development of algorithms that leverage spectral properties allows for efficient handling of massive graphs where traditional combinatorial methods are insufficient.

Spectral graph theory is central to many modern applications:

• clustering algorithms
• graph partitioning
• network robustness analysis
• image segmentation
• and even emerging fields like quantum computing.

Installation

$ npm install spektra

Features

1. Eigenvalues

   const { eigenvalues } = require('spektra');
   const A = [[1, 2], [2, 1]];
   const eig = eigenvalues(A);
   console.log(eig);
   // [3, -1]

2. Determinant

   const { determinant } = require('spektra');
   const A = [[1, 2], [2, 1]];
   const det = determinant(A);
   console.log(det);
   // -3

3. Mean

   const { mean } = require('spektra');
   const A = [[1, 2], [2, 1]];
   const m = mean(A);
   console.log(m);
   // 0.5

4. Laplacian Matrix

   const { laplacian } = require('spektra');
   const edges = [[0, 1], [1, 2]];
   const numVertices = 3;
   const L = laplacian(edges, numVertices);
   console.log(L);
   // [[1, -1, 0], [-1, 2, -1], [0, -1, 1]]

5. Characteristic Polynomial

   const { characteristicPolynomial } = require('spektra');
   const A = [[1, 2], [3, 4]];
   const p = characteristicPolynomial(A);
   console.log(p);
   // [-3, -2, 1]  (represents λ^3 - 2λ - 1)

6. Cospectrality

   const { areCospectral, adjacency } = require('spektra');
   const edges1 = [[0, 1], [1, 2], [2, 3], [3, 0]]; // C4
   const A1 = adjacency(edges1, 5); // C4 U K1
   const edges2 = [[0, 4], [1, 4], [2, 4], [3, 4]]; // K1,4 (star graph)
   const A2 = adjacency(edges2, 5);
   const cospectral = areCospectral(A1, A2);
   console.log(cospectral);
   // true

Roadmap - Completed

Item	Status
Adjacency matrix	Completed
Laplacian matrix	Completed
Characteristic polynomial	Completed
Cospectrality	Completed

Roadmap - Spectral Properties and Algorithms

Function / Feature	Description	Relevance to Current Roadmap
Eigenvalues and Eigenvectors	Compute eigenvalues and eigenvectors for adjacency and Laplacian matrices.	Essential for characteristic polynomial and cospectrality.
Spectral Radius	Largest absolute eigenvalue of the adjacency matrix.	Derived from eigenvalue computation; useful in network analysis.
Spectral Clustering Utility	Utilities using Laplacian eigenvectors for graph partitioning (e.g., Fiedler vector).	Practical application of Laplacian spectrum.
Matching Polynomial	Polynomial related to number of matchings of various sizes.	Similar role to characteristic polynomial in revealing structure.

Roadmap - Utilities and Input/Output

Function / Feature	Description	Relevance to Current Roadmap
Graph Input/Output (e.g., GML, DOT, JSON)	Standardized methods to load a graph from and save a graph to common formats.	Crucial for real-world usability.
Graph Generation (e.g., specific graph types)	Functions to generate common graph structures like complete graphs (K_n), cycles (C_n), paths (P_n), or basic random graphs (Erdos-Renyi).	Provides test cases and ready-to-use examples for the spectral functions.
Graph Isomorphism Check	Determine if two graphs are structurally identical (potentially using spectral methods as a quick check, though not a complete solution).

Contribute

Pull requests and stars are always welcome. For bugs and feature requests, please create an issue.

License

Copyright © 2023, Jayson Mulwa. Released under the MIT License.