It depends on the problem. Adjacency Matrix Uses O(n^2) memory It is fast to lookup and check for presence or absence of a specific edge between any two nodes O(1) It is slow to iterate over all edges It is slow to add/delete a node; a complex operation O(n^2) It is fast to add a … Read more
Here’s a nice example. I did 4×4 so we can see it easily, but it’s all adjustable by n. It’s also fully vectorized so should have good speed. n = 4 mat = matrix(1:n^2, nrow = n) mat.pad = rbind(NA, cbind(NA, mat, NA), NA) With the padded matrix, the neighbors are just n by n … Read more
Assuming: edges = [(‘a’, ‘b’), (‘a’, ‘b’), (‘a’, ‘c’)] Here’s some code for the matrix: from collections import defaultdict matrix = defaultdict(int) for edge in edges: matrix[edge] += 1 print matrix[‘a’, ‘b’] 2 And for the “list”: from collections import defaultdict adj_list = defaultdict(lambda: defaultdict(lambda: 0)) for start, end in edges: adj_list[start][end] += 1 print … Read more