Chunheng Jiang horsehour

Source: The Stanford GraphBase

A network can be represented by an adjacency matrix, where each cell ij represents an edge from vertex i to vertex j. Here, vertices represent characters in a book, while edges represent co-occurrence in a chapter.

Given this two-dimensional representation of a graph, a natural visualization is to show the matrix! However, the effectiveness of a matrix diagram is heavily dependent on the order of rows and columns: if related nodes are placed closed to each other, it is easier to identify clusters and bridges.

This example lets you try different orderings via the drop-down menu. This type of diagram can be extended with manual reordering of rows and columns, and expanding or collapsing of clusters, to allow deeper exploration. Jacques Bertin (or more specifically, his fleet of assistants) did this by hand with

	import importlib
	import os
	from urllib import request

	import tensorflow as tf
	import numpy as np
	import json
	import base64
	import networkx as nx
	from typing import Sequence, Dict, Union, Tuple, Any, List

	# Setup Ubuntu
	sudo apt update --yes
	sudo apt upgrade --yes

	# Get Miniconda and make it the main Python interpreter
	wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
	bash ~/miniconda.sh -b -p ~/miniconda
	rm ~/miniconda.sh

	export PATH=~/miniconda/bin:$PATH

	import lightgbm as lgb
	import numpy as np
	import pandas as pd
	from sklearn.metrics import mean_squared_error

	def custom_subsample(n, frac):
	"""Subsample frac*n indices."""
	return np.random.choice(n, int(n*frac), replace=False)

	# load data

	import sys

	def get_size(obj, seen=None):
	"""Recursively finds size of objects"""
	size = sys.getsizeof(obj)
	if seen is None:
	seen = set()
	obj_id = id(obj)
	if obj_id in seen:
	return 0