import subprocess import zipfile import numpy as np import pandas as pd import matplotlib.cm as cm import graph_tool as gt import graph_tool.draw as gtd import deepgraph as dg # ## The Noordin Top Terrorist Data # **Preprocessing the Nodes** # zip file containing node attributes get_nodes_zip = ("wget -O terrorist_nodes.zip " "https://sites.google.com/site/sfeverton18/" "research/appendix-1/Noordin%20Subset%20%28ORA%29.zip?" "attredirects=0&d=1") subprocess.call(get_nodes_zip.split()) # unzip zf = zipfile.ZipFile('terrorist_nodes.zip') zf.extract('Attributes.csv') zf.close() # create node table v = pd.read_csv('Attributes.csv') v.rename(columns={'Unnamed: 0': 'Name'}, inplace=True) # create a copy of all nodes for each layer (i.e., create "node-layers") # there are 10 layers and 79 nodes on each layer v = pd.concat(10*[v]) # add "aspect" as column to v layer_names = ['Business', 'Communication', 'O Logistics', 'O Meetings', 'O Operations', 'O Training', 'T Classmates', 'T Friendship', 'T Kinship', 'T Soulmates'] layers = [[name]*79 for name in layer_names] layers = [item for sublist in layers for item in sublist] v['layer'] = layers # set unique node index v.reset_index(inplace=True) v.rename(columns={'index': 'V_N'}, inplace=True) # swap columns cols = list(v) cols[1], cols[10] = cols[10], cols[1] v = v.ix[:,cols] # get rid of the data columns for demonstrational purposes, # will be inserted again later # v, vinfo = v.iloc[:, :2], v.iloc[:, 2:] # **Preprocessing the Edges** # paj file containing edges for different layers get_paj = ("wget -O terrorists.paj " "https://sites.google.com/site/sfeverton18/" "research/appendix-1/Noordin%20Subset%20%28Pajek%29.paj?" "attredirects=0&d=1") subprocess.call(get_paj.split()) # get data blocks from paj file with open('terrorists.paj') as txtfile: comments = [] data = [] part = [] for line in txtfile: if line.startswith('*'): # comment lines comment = line comments.append(comment) if part: data.append(part) part = [] else: # vertices if comment.startswith('*Vertices') and len(line.split()) > 1: sublist = line.split('"') sublist = sublist[:2] + sublist[-1].split() part.append(sublist) # edges or partitions elif len(line) > 1: part.append(line.split()) # append last block data.append(part) # extract edge tables from data blocks ecomments = [] eparts = [] for i, c in enumerate(comments): if c.startswith('*Network'): del data[0] elif c.startswith('*Partition'): del data[0] elif c.startswith('*Vector'): del data[0] elif c.startswith('*Arcs') or c.startswith('*Edges'): ecomments.append(c) eparts.append(data.pop(0)) # with corresponding data parts (indices found manually via comments) inds = [11, 10, 5, 6, 7, 8, 0, 1, 2, 3] eparts = [eparts[ind] for ind in inds] # convert to DataFrames layer_frames = [] for name, epart in zip(layer_names, eparts): frame = pd.DataFrame(epart, dtype=np.int16) # get rid of self-loops, bidirectional edges frame = frame[frame[0] < frame[1]] # and rename columns frame.rename(columns={0: 's', 1: 't', 2: name}, inplace=True) frame['s'] -= 1 frame['t'] -= 1 layer_frames.append(frame) # set indices for i, e in enumerate(layer_frames): e['s'] += i*79 e['t'] += i*79 e.set_index(['s', 't'], inplace=True) # concat the layers e = pd.concat(layer_frames) # alternative representation of e (uses less ram) e['type'] = 0 e['weight'] = 0 for layer in layer_names: where = e[layer].notnull() e.loc[where, 'type'] = layer e.loc[where, 'weight'] = e.loc[where, layer] e = e[['type', 'weight']] # ## DeepGraph's Supra-Graph Representation of a MLN, $G = (V, E)$ # Above, we have preprocessed the downloaded data into a node table v # and an edge table e, that correspond to the supra-graph representation # of a multilayer network. This is the preferred representation of a MLN # by a deep graph, since - as we will demonstrate below - all other # representations are entailed in the supra-graph's partition lattice. g = dg.DeepGraph(v, e) # ====================================================================== # plot circular # scale factor (resolution of the plot) scale_factor = 1 # create graph_tool graph object gtg = g.return_gt_graph(features=True, relations=True) gtg.set_directed(False) # build tree t = gt.Graph() # add nodes nodes_by_layer = {} for layer in layer_names: nodes_by_layer[layer] = t.add_vertex(n=79) layer_nodes = {} for layer in layer_names: layer_nodes[layer] = t.add_vertex() root = t.add_vertex() # add edges for layer in layer_names: t.add_edge(root, layer_nodes[layer]) for node in nodes_by_layer[layer]: t.add_edge(layer_nodes[layer], node) # radial tree layout tpos = gtd.radial_tree_layout(t, root) cts = gtd.get_hierarchy_control_points(gtg, t, tpos) # add layer nodes to gtg and adjust plot sizes vsize = gtg.new_vertex_property('double', [5*scale_factor]*790) names = gtg.vp['Name'] for layer in layer_names: node = gtg.add_vertex() names[node] = layer vsize[node] = 0 pos = gtg.own_property(tpos) # labels text_rot = gtg.new_vertex_property('double') for node in gtg.vertices(): if pos[node][0] > 0: text_rot[node] = np.arctan(pos[node][1] / pos[node][0]) else: text_rot[node] = np.pi + np.arctan(pos[node][1] / pos[node][0]) # font size of node texts fs = np.ones(800) * 3.125 * scale_factor fs[790:] = 15 * scale_factor fs = gtg.new_vertex_property('double', fs) # plot gtd.graph_draw(gtg, pos=pos, edge_control_points=cts, vertex_fill_color=gtg.vp['V_N'], vcmap=cm.viridis_r, vertex_size=vsize, vertex_font_size=fs, vertex_text=names, vertex_text_color='k', vertex_text_rotation=text_rot, vertex_text_position=1, # edge_color=gtg.ep['weight'], # ecmap=cm.viridis, vertex_anchor=0, edge_pen_width=.375*scale_factor, bg_color=[1,1,1,0], output_size=[1024*scale_factor,1024*scale_factor], output='radial_g_{}.png'.format(scale_factor), )