algo-graph-representations

name	algo-graph-representations
description	Build graph structures (adjacency matrix/list, edge list) in Python/NumPy for PPI, GRN, and metabolic networks. Use when representing a graph, picking sparse vs dense storage, loading an edge-list file, or prepping for BFS/DFS/Dijkstra/MST.
tool_type	python
primary_tool	numpy

Graph Representations

When to Use

• Modeling a protein-protein interaction (PPI) network, gene regulatory network (GRN), or metabolic network as a graph.
• Deciding between adjacency matrix, adjacency list, and edge list before implementing BFS/DFS, Dijkstra, or Kruskal/Prim MST.
• Loading a graph from a node_a node_b weight edge-list file (e.g. STRING/BioGRID exports).
• Estimating memory footprint of a large sparse network (thousands of nodes, sparse edges) before choosing a matrix representation.
• Building a de Bruijn graph (k-mers as vertices, overlaps as edges) for sequence assembly.

Version Compatibility

Python ≥3.9 (uses dict/list generic type hints), NumPy ≥1.24. No other dependencies — collections.defaultdict is stdlib.

Prerequisites

• pip install numpy
• Familiarity with Big-O notation and basic graph terminology (vertex, edge, degree, directed/undirected).
• Useful follow-on skills: algo-bfs-dfs, algo-dijkstra, algo-mst-kruskal-prim, networkx.

Choosing a Representation

Operation	Adjacency Matrix	Adjacency List	Edge List
Space	O(V²)	O(V + E)	O(E)
Add edge	O(1)	O(1)	O(1)
Remove edge	O(1)	O(degree)	O(E)
Check edge exists	O(1)	O(1) w/ set, O(degree) w/ list	O(E)
Get neighbors	O(V)	O(1) access + O(degree) iterate	O(E)
Iterate all edges	O(V²)	O(E)	O(E)

Scenario	Use
Dense graph (E ≈ V²)	Adjacency Matrix
Sparse graph (most bio networks)	Adjacency List
Frequent edge-existence queries on a small graph	Adjacency Matrix
BFS / DFS traversal	Adjacency List
MST (Kruskal's), streaming from file	Edge List

Most biological networks (PPI, GRN, metabolic) are sparse — adjacency lists are almost always the right default.

Application	Vertices	Edges
PPI network	Proteins	Physical interactions
Metabolic network	Metabolites	Reactions
Gene regulatory network	Genes / TFs	Regulatory relationships
de Bruijn graph	k-mers	Sequence overlaps
Phylogenetic tree	Species / sequences	Evolutionary relationships

Adjacency Matrix

Goal: O(1) edge-existence checks for a small, dense graph (e.g. a handful of interacting proteins where you query edges constantly). Approach: Store an N×N NumPy int array indexed by a vertex→index map; set matrix[i][j] = 1 for each edge, mirroring across the diagonal for undirected graphs.

import numpy as np


class AdjacencyMatrix:
    """Graph representation backed by a dense NumPy adjacency matrix."""

    def __init__(self, vertices: list):
        self.vertices = vertices
        self.idx = {v: i for i, v in enumerate(vertices)}
        self.n = len(vertices)
        self.matrix = np.zeros((self.n, self.n), dtype=int)

    def add_edge(self, u, v, directed: bool = False):
        i, j = self.idx[u], self.idx[v]
        self.matrix[i][j] = 1
        if not directed:
            self.matrix[j][i] = 1

    def has_edge(self, u, v) -> bool:  # O(1)
        return bool(self.matrix[self.idx[u]][self.idx[v]])

    def neighbors(self, u) -> list:  # O(V)
        i = .idx[u]
         [.vertices[j]  j  (.n)  .matrix[i][j]]

     () -> :
         (np.(.matrix[.idx[u]]))



proteins = [, , , , ]
interactions = [
    (, ),   
    (, ),  
    (, ),    
    (, ),   
    (, ),  
    (, ),   
]
ppi = AdjacencyMatrix(proteins)
 p1, p2  interactions:
    ppi.add_edge(p1, p2)

 ppi.has_edge(, )  
 ppi.degree() ==   

Adjacency List

Goal: Efficient storage and traversal for sparse networks (the common case for PPI/GRN graphs with thousands of nodes). Approach: Map each vertex to a set of neighbors via defaultdict(set) — sets give O(1) edge checks without the O(V²) memory cost of a matrix.

from collections import defaultdict


class AdjacencyList:
    """Sparse graph representation: vertex -> set of neighbors."""

    def __init__(self):
        self.graph: dict = defaultdict(set)  # sets give O(1) edge lookup

    def add_edge(self, u, v, directed: bool = False):
        self.graph[u].add(v)
        if not directed:
            self.graph[v].add(u)

    def has_edge(self, u, v) -> bool:  # O(1) with sets
        return v in self.graph.get(u, set())

    def neighbors(self, u) -> set:  # O(1) access
        return self.graph.get(u, set())

    def degree(self, u) -> int:
        return len(self.graph.get(u, []))

    def edges(self) -> list:  # undirected: each edge reported once
        seen = ()
        result = []
         u  .graph:
             v  .graph[u]:
                key = (([(u), (v)]))
                 key   seen:
                    seen.add(key)
                    result.append((u, v))
         result

Edge List and File Loading

Goal: Represent (and sort) weighted edges — the natural format for MST algorithms and for parsing tab/space-delimited interaction files (e.g. STRING confidence scores). Approach: Keep a flat list of (u, v, weight) tuples; convert to an adjacency list on load for traversal.

from pathlib import Path
from collections import defaultdict


class EdgeList:
    """Weighted edge list, useful for Kruskal's MST and bulk file I/O."""

    def __init__(self):
        self.edges: list[tuple] = []
        self._vertices: set = set()

    def add_edge(self, u, v, weight: float = 1.0):
        self.edges.append((u, v, weight))
        self._vertices.update([u, v])

    def sorted_by_weight(self) -> list:  # for Kruskal's MST
        return sorted(self.edges, key=lambda e: e[2])


def load_edge_list(filepath: str) -> dict[str, list[tuple[str, float]]]:
    """Parse a weighted edge-list file ('node_a node_b weight' per line)
    into an undirected adjacency list: {node: [(neighbor, weight), ...]}.
    """
    graph: dict = defaultdict(list)
    with open(filepath)  f:
         line  f:
            line = line.strip()
              line:
                
            u, v, w = line.split()
            graph[u].append((v, (w)))
            graph[v].append((u, (w)))  
     (graph)

Memory Analysis (Human PPI: 20,000 proteins, 300,000 interactions)

V, E = 20_000, 300_000
matrix_mb = V * V * 8 / 1e6       # ~3,200 MB
list_mb = (2 * E * 8 + V * 56) / 1e6  # ~5.9 MB
# Ratio: ~542x — adjacency list wins for sparse biological networks

Pitfalls

• Adjacency matrix for large sparse graphs: 20k proteins → 3.2 GB matrix for a network that fits in ~6 MB as an adjacency list.
• List vs set for adjacency list: using a list makes has_edge O(degree); use set for O(1) lookup if duplicate edges are not needed.
• Undirected graphs and edges(): naively iterating gives each edge twice; track seen pairs with a set or only emit u < v.
• Vertex not in graph: indexing a defaultdict (graph[v]) silently creates an empty entry; use graph.get(v, set()) for read-only lookups to avoid spurious vertices.
• Edge list for BFS/DFS: finding neighbors requires scanning all edges — O(E) per node. Convert to an adjacency list first.

See Also

• algo-bfs-dfs — traversal algorithms built on top of the adjacency list.
• algo-dijkstra — shortest paths on weighted graphs represented as adjacency lists.
• algo-mst-kruskal-prim — MST algorithms that consume the edge list directly.
• networkx — full-featured graph library when you need built-in algorithms instead of hand-rolled representations.