# Visual overview of basic graph types generated using NetworkX

Recently, I’ve been working on understanding how graphs can be represented and manipulated in Python. I needed to generate a reasonable random graph and its adjacency matrix in Python, and found the NetworkX package very useful for this purpose.

Since I am somewhat naïve about graph theory, I found it useful to construct a visual overview of the different types of basic graphs that we are able to generate using NetworkX. I experimented with the arguments to better interpret the properties of these graphs.

Sharing this for future reference, and to help anyone just starting out with graphs in Python.

 `balanced_tree`(r, h[, create_using]) r = 2 — branching factorh = 3 — height `barbell_graph`(m1, m2[, create_using]) m1 = 4 — no. of nodes in each identical, complete sub-graphm2 = 2 — no. of nodes in connecting path `complete_graph`(n[, create_using]) n = 10 — no. of nodes `complete_multipartite_graph`(*subset_sizes) subset sizes = 1, 2, 3 `circular_ladder_graph`(n[, create_using]) n = 6 — length of ladder (3 creates an “extruded triangle”, 4 an “extruded square”, 6 an “extruded hexagon” and so on) `circulant_graph`(n, offsets[, create_using]) n = 10 — no. of nodesoffsets = [1,2] `cycle_graph`(n[, create_using]) n = 5 — no. of nodes `dorogovtsev_goltsev_mendes_graph`(n[, …]) n = 3 — number of nodes added to initial triangle `empty_graph`([n, create_using, default]) `full_rary_tree`(r, n[, create_using]) Here I chose r and n in order to recreate the diamond tetrahedral structure.r = 4 – branching factor of the treen = 17 – number of nodes in the tree `ladder_graph`(n[, create_using]) n = 7 — length of ladder `lollipop_graph`(m, n[, create_using]) m = — no. of nodes in complete graphn = — path length `null_graph`([create_using]) `path_graph`(n[, create_using]) n = 7 — path length `star_graph`(n[, create_using]) n = 10 — no. of points in star graph `trivial_graph`([create_using]) Trivial! `turan_graph`(n, r) n = 5 – no. of nodesr = 2 – no. of partitions `wheel_graph`(n[, create_using]) 0