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∑MathAdvanced

Betti Numbers

Key Points

  • •
    Betti numbers count independent k-dimensional holes: β0​ counts connected components, β1​ counts independent loops/tunnels, and β2​ counts voids.
  • •
    Formally, βk​ is the rank (dimension) of the k-th homology group Hk​, usually computed over a field like Z2​ or Q.
  • •
    For a finite simplicial complex, you can compute Betti numbers from boundary matrices using Gaussian elimination modulo 2.
  • •
    A practical formula is \(βk​ = mk​ - rank(∂k​) - rank(∂k+1​)\), where mk​ is the number of k-simplices.
  • •
    In graphs, \(β1​ = ∣E∣ - ∣V∣ + β0​\) follows directly from Euler characteristic, and \(β0​\) is the number of connected components.
  • •
    Computations can be done efficiently over Z2​, avoiding orientation issues and making boundary matrices binary.
  • •
    Euler characteristic relates cells and Betti numbers: \(χ = ∑ (-1)^k mk​ = ∑ (-1)^k βk​\), providing a strong consistency check.
  • •
    C++ implementations typically build boundary matrices and compute ranks with mod-2 Gaussian elimination or use DSU for \(β0​\) on graphs.

Prerequisites

  • →Set theory and combinatorics — To understand simplices as subsets and how complexes are built from faces.
  • →Graphs and basic graph algorithms — β₀ and β₁ for graphs illustrate homology in 1D and motivate DSU.
  • →Linear algebra over fields — Homology uses kernels, images, ranks; computations rely on Gaussian elimination.
  • →Matrix representations and Gaussian elimination — Boundary matrices are central; rank/nullity is computed via elimination.
  • →Modular arithmetic (GF(2)) — We compute over \mathbb{Z}_2 to avoid orientations and use bit operations.
  • →Simplicial complexes — Provide the discrete model of spaces required to define boundary operators.
  • →Euler characteristic — Relates cell counts and Betti numbers; useful for checks and graph β₁.
  • →Disjoint Set Union (Union-Find) — Efficiently computes β₀ on graphs, demonstrating fast topological queries.

Detailed Explanation

Tap terms for definitions

01Overview

Betti numbers are numerical fingerprints that describe the shape of a space by counting holes in different dimensions. They arise from algebraic topology, where spaces are studied by converting geometric questions into algebraic ones. For a given space (often discretized as a simplicial or cubical complex), the k-th Betti number, β_k, measures the number of independent k-dimensional cycles that do not bound a (k+1)-dimensional piece. Intuitively, β_0 counts connected components, β_1 counts loops or tunnels, and β_2 counts voids or cavities. These numbers are robust under continuous deformations (stretching, bending) that do not tear or glue the space, making them topological invariants. In computation, we usually work with finite combinatorial models of spaces: graphs for 1D, triangle meshes for 2D surfaces, and higher-dimensional simplicial/cubical complexes for general shapes or point-cloud filtrations. We assemble boundary matrices encoding how k-simplices are attached to (k−1)-simplices, then apply linear algebra over a field (commonly \mathbb{Z}_2) to compute ranks and nullities. Efficient software packages do this at scale, but understanding the core mechanics—boundary operators, cycles, boundaries, and rank computations—provides deep insight and allows you to implement small, educational solvers in C++. Betti numbers have applications across data analysis (topological data analysis, persistence), graphics and geometry processing, robotics (configuration spaces), sensor networks (coverage holes), and materials science (porous media). They summarize essential topological structure in a compact, interpretable form.

02Intuition & Analogies

Imagine exploring structures with a drone that can fly through rooms, around pillars, and into tunnels.

  • β₀ (connected components): Count how many separate buildings your drone would need to exit and re-enter to visit. Each disconnected piece is one component.
  • β₁ (loops/tunnels): Inside a single building, imagine hallways forming loops. Ask: how many independent circular routes exist that are not just obvious detours around the same obstacle? That number is β₁. If a loop is the boundary of a filled-in sheet (like the rim of a disk), it doesn’t count because it’s not a true tunnel—your loop can be “shrunk” across the sheet.
  • β₂ (voids): Think of sealed rooms with no doors—air pockets inside a 3D object. Each independent cavity is one void counted by β₂.

To make this concrete with LEGO:

  • Build several separate piles: that’s β₀.
  • Connect bricks to form a ring: that ring contributes to β₁. Add a thin film filling the ring’s hole (like a disk) and the loop disappears topologically.
  • Make a hollow ball (like an Easter egg): the inside counts as one β₂.

Computationally, we break a shape into simple pieces: points (0-simplices), edges (1-simplices), triangles (2-simplices), and tetrahedra (3-simplices). We record which piece touches which using a boundary matrix. Over \mathbb{Z}_2 (bits), this matrix just notes presence/absence of a face. Linear algebra on this matrix tells us how many cycles exist (loops made of pieces) and how many are trivial (those that bound something). The difference between all cycles and trivial cycles is exactly the Betti number. This perspective turns counting holes into counting independent solutions of linear equations modulo 2.

03Formal Definition

Let K be a finite simplicial complex. Denote by Ck​(K;\,F) the vector space over a field F generated by k-simplices of K. The boundary operator \(∂k​ : Ck​ → Ck−1​\) is a linear map satisfying \(∂k​ ∘ ∂k+1​ = 0\). The k-cycles are \(Zk​ = ker(∂k​)\), and the k-boundaries are \(Bk​ = im(∂k+1​)\). The k-th homology group is the quotient \(Hk​(K;\,F) = Zk​ / Bk​\). Its dimension (rank) over F is the k-th Betti number: \(βk​(K;\,F) = dimF​ Hk​(K;\,F)\). For computations, we often fix F = Z2​ so orientations are irrelevant and matrices are binary. If mk​ is the number of k-simplices, the boundary operator \(∂k​\) can be represented as an \(mk−1​ × mk​\) matrix. Using rank–nullity, we obtain \[ βk​ = dim Zk​ - dim Bk​ = (mk​ - rank(∂k​)) - rank(∂k+1​) = mk​ - rank(∂k​) - rank(∂k+1​). \] The Euler characteristic is \(χ(K) = ∑k≥0​ (-1)^k mk​ = ∑k≥0​ (-1)^k βk​\). For 1-dimensional complexes (graphs), \(β1​ = ∣E∣ - ∣V∣ + β0​\). Over fields of characteristic 0 (e.g., Q), Betti numbers coincide with the ranks of integer homology groups and ignore torsion.

04When to Use

Use Betti numbers when you need a robust, scale-independent summary of shape topology.

  • Graphs and networks: β₀ is the number of connected components; β₁ measures independent cycles in the network, relevant to redundancy in communication or power grids.
  • 2D/3D meshes: β₁ reveals handles and tunnels in surfaces (e.g., a coffee mug has β₁=1), and β₂ reveals enclosed voids (e.g., a hollow sphere has β₂=1).
  • Topological Data Analysis (TDA): Compute Betti numbers across a filtration (varying a distance threshold) to get persistence diagrams, capturing features that persist across scales and are likely to be signal rather than noise.
  • Robotics and motion planning: Homology of configuration spaces suggests the presence of unavoidable loops/obstacles in feasible motion.
  • Medical and materials imaging: Quantify porosity and connectivity by computing β₁ and β₂ of segmented volumes.
  • Sanity checks for meshes: Use Euler characteristic and Betti numbers to check watertightness and genus. Choose computations over \mathbb{Z}_2 for simplicity and speed, and move to persistent homology when features at multiple scales matter. For pure graphs, formulas via Euler characteristic and DSU are fastest; for general complexes, build boundary matrices and compute ranks.

⚠️Common Mistakes

  • Confusing any cycle with β₁: A loop that bounds a filled-in triangle does not contribute to β₁. Only cycles not bounding a 2-chain are counted.
  • Ignoring orientation issues over \mathbb{Z}: Over integers, boundary signs matter; working over \mathbb{Z}_2 avoids sign bookkeeping. If you implement over \mathbb{Z} but treat entries as unsigned, you get wrong ranks.
  • Double-counting simplices: Failing to deduplicate edges or triangles inflates m_k and corrupts Betti counts. Always canonicalize (e.g., sort vertices of an edge so (u,v) and (v,u) are the same).
  • Misapplying the graph formula: (\beta_1 = |E| - |V| + \beta_0) holds for graphs (1-dimensional complexes), not for 2D or 3D meshes with filled faces.
  • Forgetting (\partial^2=0): If your boundary assembly allows a triangle’s edge to appear twice in its boundary over \mathbb{Z}_2), it cancels to zero—this is correct. Over-counting without mod-2 leads to violations of (\partial \circ \partial = 0).
  • Numerical pitfalls: Implementing Gaussian elimination but using toggling instead of setting bits in the boundary matrix can silently introduce errors. Similarly, mixing dense and sparse representations without care can blow up memory.
  • Misinterpreting β over different fields: Betti numbers are invariant across fields for finite complexes (they’re ranks), but torsion information is lost; don’t expect (\mathbb{Z}_2) computations to reveal torsion.

Key Formulas

Betti Number Definition

βk​(K;F)=rankF​Hk​(K;F)

Explanation: The k-th Betti number is the dimension (rank) of the k-th homology group over a field F. It counts independent k-dimensional holes.

Homology Group

Hk​(K;F)=ker(∂k​)\/im(∂k+1​)

Explanation: k-cycles are those with zero boundary, and k-boundaries are those that are themselves boundaries of (k+1)-chains. Homology is cycles modulo boundaries.

Betti via Boundary Ranks

βk​=mk​−rank(∂k​)−rank(∂k+1​)

Explanation: For a finite complex with mk​ k-simplices, Betti numbers can be computed by counting columns and subtracting ranks of adjacent boundary matrices.

Euler Characteristic

χ(K)=k≥0∑​(−1)kmk​=k≥0∑​(−1)kβk​

Explanation: The alternating sum of the number of simplices equals the alternating sum of Betti numbers. This is a powerful consistency check for computations.

Rank–Nullity

dimker(A)=n−rank(A)

Explanation: For an n-column matrix A, the dimension of its null space equals n minus its rank. This underlies the Betti formula using boundary ranks.

Boundary of a Boundary is Zero

∂k​∘∂k+1​=0

Explanation: Applying two consecutive boundary operators always yields zero. Algebraically, this ensures that boundaries are cycles.

Graph Betti-1

β1​(graph)=∣E∣−∣V∣+β0​

Explanation: For 1-dimensional complexes (graphs), the first Betti number equals edges minus vertices plus components. It counts independent cycles in the graph.

Instantaneous Betti in Persistence

βk​(t)=∣{[bi​,di​)∣bi​≤t<di​}∣

Explanation: At a filtration value t, the k-th Betti number equals the number of persistence intervals covering t. This connects homology with barcodes.

Cellular Euler Characteristic

χ=V−E+F−T+⋯

Explanation: In cell complexes, the Euler characteristic equals the alternating sum of the number of i-cells. It matches the alternating sum of Betti numbers.

Poincaré Duality (Betti Symmetry)

βk​=βn−k​ for closed orientable n-manifolds

Explanation: On such manifolds, Betti numbers are symmetric around the middle dimension. This constrains possible values and serves as a check.

Complexity Analysis

Let N be the total number of simplices and mk​ the number of k-simplices. Building boundary matrices for a simplicial complex requires enumerating faces: each k-simplex has k+1 faces, so assembling \(∂k​\) touches O((k+1) mk​) entries. Over Z2​\), matrices are very sparse: each column of \(∂k​\) has exactly k+1 ones. If we store them densely, memory can rise to O(mk−1​·mk​); in practice, bit-packed rows or sparse structures (e.g., column lists) are preferred. Computing ranks with naive Gaussian elimination over a field on an r×c matrix is O(min(r,c)·r·c) in time and O(r·c) in space if done densely. With bit-packing over Z2​\), per-row operations are over words, so a factor of 64 speedup is typical, but asymptotics remain cubic in the worst case. For our 2D complexes, we compute ranks of \(∂1​\) (∣V∣×∣E∣) and \(∂2​\) (∣E∣×∣T∣), leading to worst-case time O(∣V∣∣E∣min(∣V∣,∣E∣)) and O(∣E∣∣T∣min(∣E∣,∣T∣)) if treated as dense; with sparsity and bitsets, performance is much better in practice. For pure graphs, β0​ can be computed with a Union-Find (DSU) in near-linear time O(∣E∣ α(∣V∣)), and β1​ follows from the Euler formula \(β1​ = ∣E∣ - ∣V∣ + β0​\). This is optimal for large sparse graphs. For higher dimensions or filtrations (persistent homology), the standard matrix reduction algorithm has worst-case O(N3) time and O(N2) space, though practical datasets often behave closer to near-quadratic with appropriate ordering and sparsity exploitation. In summary: DSU-based β0​/β1​ on graphs is near-linear; general Betti computations via boundary ranks are polynomial, usually dominated by matrix elimination; memory is the key constraint for dense representations, making bitsets or sparse formats advisable.

Code Examples

Betti numbers for a graph: β₀ by DSU and β₁ from Euler’s formula
1#include <bits/stdc++.h>
2using namespace std;
3
4struct DSU {
5 int n; vector<int> p, r;
6 DSU(int n=0): n(n), p(n), r(n,0) { iota(p.begin(), p.end(), 0); }
7 int find(int x){ return p[x]==x? x: p[x]=find(p[x]); }
8 bool unite(int a, int b){ a=find(a); b=find(b); if(a==b) return false; if(r[a]<r[b]) swap(a,b); p[b]=a; if(r[a]==r[b]) r[a]++; return true; }
9};
10
11// Example graph: two components, one contains a triangle (one independent cycle)
12// V = {0,1,2,3,4}, E = {(0,1),(1,2),(2,0),(3,4)}
13int main(){
14 int V = 5;
15 vector<pair<int,int>> edges = {{0,1},{1,2},{2,0},{3,4}};
16
17 DSU dsu(V);
18 for (auto [u,v] : edges) dsu.unite(u,v);
19
20 // Count components (β0)
21 unordered_set<int> reps;
22 for (int v=0; v<V; ++v) reps.insert(dsu.find(v));
23 int beta0 = (int)reps.size();
24
25 // β1 for a graph: |E| - |V| + β0
26 int beta1 = (int)edges.size() - V + beta0;
27
28 cout << "Graph Betti numbers:\n";
29 cout << "beta0 (components) = " << beta0 << "\n";
30 cout << "beta1 (independent cycles) = " << beta1 << "\n";
31
32 return 0;
33}
34

This program computes Betti numbers for a 1-dimensional complex (a graph). We use a Disjoint Set Union (Union-Find) to count connected components (β₀). For graphs, β₁ equals the number of edges minus vertices plus components, following the Euler characteristic for 1D complexes. The example graph has two components and one independent cycle (the triangle).

Time: O(|E| α(|V|)) time and O(|V|) space for DSU; constant-time arithmetic for the β₁ formula.Space: O(|V|) for DSU plus O(|E|) to store edges.
Betti numbers (β₀, β₁, β₂) of a 2D simplicial complex via boundary ranks over Z2
1#include <bits/stdc++.h>
2using namespace std;
3
4// Bit-packed matrix over GF(2), stored as rows of uint64_t words
5struct BitMatrix {
6 int rows, cols, W; // W = words per row
7 vector<vector<uint64_t>> a; // a[rows][W]
8 BitMatrix(int r=0, int c=0): rows(r), cols(c) {
9 W = (cols + 63) / 64;
10 a.assign(rows, vector<uint64_t>(W, 0ULL));
11 }
12 inline void set1(int r, int c){
13 int w = c >> 6, b = c & 63;
14 a[r][w] |= (1ULL << b);
15 }
16 inline bool get(int r, int c) const {
17 int w = c >> 6, b = c & 63;
18 return (a[r][w] >> b) & 1ULL;
19 }
20 inline void xor_row(int i, const vector<uint64_t>& pivot){
21 for(int w=0; w<W; ++w) a[i][w] ^= pivot[w];
22 }
23};
24
25int rank_mod2(BitMatrix M){
26 int r = 0; // current pivot row
27 for(int col=0; col<M.cols && r < M.rows; ++col){
28 int sel = -1;
29 for(int i=r; i<M.rows; ++i){ if (M.get(i,col)) { sel = i; break; } }
30 if (sel == -1) continue; // no pivot in this column
31 swap(M.a[sel], M.a[r]);
32 // Eliminate this column in all other rows
33 for(int i=0; i<M.rows; ++i){ if (i!=r && M.get(i,col)) M.xor_row(i, M.a[r]); }
34 ++r;
35 }
36 return r; // number of pivots = rank
37}
38
39struct PairHash { size_t operator()(const pair<int,int>& p) const noexcept { return (uint64_t)p.first<<32 ^ (uint64_t)p.second; } };
40
41// Build edges and boundary matrices for a 2D simplicial complex
42// Input: V vertices labeled 0..V-1, list of triangles (each as 3 vertex indices)
43// Output: beta0, beta1, beta2 over Z2
44struct Betti2D {
45 int V, E, T;
46 int beta0, beta1, beta2;
47};
48
49Betti2D compute_betti_2D(int V, const vector<array<int,3>>& triangles){
50 // Deduplicate edges from triangles
51 unordered_map<pair<int,int>, int, PairHash> edge_id;
52 vector<pair<int,int>> edges;
53 auto add_edge = [&](int u, int v){ if(u>v) swap(u,v); auto key = make_pair(u,v); if(!edge_id.count(key)){ int id = (int)edges.size(); edge_id[key]=id; edges.push_back(key); } };
54
55 for (auto tri : triangles){
56 array<int,3> t = tri;
57 sort(t.begin(), t.end()); // canonicalize triangle vertex order (not strictly needed over Z2)
58 add_edge(t[0], t[1]); add_edge(t[0], t[2]); add_edge(t[1], t[2]);
59 }
60 int E = (int)edges.size();
61 int T = (int)triangles.size();
62
63 // Build boundary matrices over Z2: \partial_1: C1->C0 is V x E; \partial_2: C2->C1 is E x T
64 BitMatrix d1(V, E);
65 for (int e=0; e<E; ++e){
66 auto [u,v] = edges[e];
67 d1.set1(u, e);
68 d1.set1(v, e);
69 }
70
71 BitMatrix d2(E, T);
72 for (int t=0; t<T; ++t){
73 int a = triangles[t][0], b = triangles[t][1], c = triangles[t][2];
74 // canonicalize edges (u<v) to match edge_id keys
75 auto key1 = make_pair(min(a,b), max(a,b));
76 auto key2 = make_pair(min(a,c), max(a,c));
77 auto key3 = make_pair(min(b,c), max(b,c));
78 int e1 = edge_id[key1];
79 int e2 = edge_id[key2];
80 int e3 = edge_id[key3];
81 d2.set1(e1, t); d2.set1(e2, t); d2.set1(e3, t);
82 }
83
84 int rank_d1 = rank_mod2(d1);
85 int rank_d2 = rank_mod2(d2);
86
87 // Betti numbers over Z2 for a 2D complex (no 3-simplices):
88 // beta0 = m0 - rank(d1), beta1 = m1 - rank(d1) - rank(d2), beta2 = m2 - rank(d2)
89 Betti2D out; out.V=V; out.E=E; out.T=T;
90 out.beta0 = V - rank_d1;
91 out.beta1 = E - rank_d1 - rank_d2;
92 out.beta2 = T - rank_d2; // since \partial_3 = 0 (no tetrahedra)
93 return out;
94}
95
96int main(){
97 // Example: boundary of a tetrahedron (a sphere S^2)
98 // Vertices: 0,1,2,3; Triangles: all 4 faces
99 int V = 4;
100 vector<array<int,3>> tris = {
101 {0,1,2}, {0,1,3}, {0,2,3}, {1,2,3}
102 };
103 auto betti = compute_betti_2D(V, tris);
104
105 cout << "2D complex stats: V=" << betti.V << ", E=" << betti.E << ", T=" << betti.T << "\n";
106 cout << "Betti numbers over Z2:" << "\n";
107 cout << "beta0 (components) = " << betti.beta0 << "\n";
108 cout << "beta1 (tunnels) = " << betti.beta1 << "\n";
109 cout << "beta2 (voids) = " << betti.beta2 << "\n";
110
111 // Expected for sphere: beta0=1, beta1=0, beta2=1
112 return 0;
113}
114

We construct boundary matrices ∂₁ (edges→vertices) and ∂₂ (triangles→edges) over Z₂ using bit-packed rows. Gaussian elimination modulo 2 yields their ranks. Betti numbers then follow from β₀ = m₀ − rank(∂₁), β₁ = m₁ − rank(∂₁) − rank(∂₂), and β₂ = m₂ − rank(∂₂) (since there are no 3-simplices). The example is the surface of a tetrahedron (a sphere), which has β₀=1, β₁=0, β₂=1.

Time: Building boundaries: O(T) to enumerate edges and O(E + T). Rank computation: O(V·E·W + E·T·W) where W=ceil(max(E,T)/64); worst-case cubic but fast in practice for small complexes.Space: O(V·W + E·W) words for matrices (bit-packed), plus O(E) for edges.
#betti numbers#homology#simplicial complex#boundary matrix#euler characteristic#persistent homology#z2 gaussian elimination#union-find#connected components#cycles#tunnels#voids#topological data analysis#rips complex#rank nullity