using ithoughtsx to create social network map

USING ITHOUGHTSX TO CREATE SOCIAL NETWORK MAP CODE
USING ITHOUGHTSX TO CREATE SOCIAL NETWORK MAP DOWNLOAD

Nx.draw_networkx(G_fb, pos=pos, with_labels=False,

The code to do this is: pos = nx.spring_layout(G_fb)īetCent = nx.betweenness_centrality(G_fb, normalized=True, endpoints=True) We can also visualize the network such that the node color varies with Degree and node size with Betweenness Centrality. The network consists of 4,039 nodes, connected via 88,234 edges. G_fb = nx.read_edgelist('/Users/jankirenz/Dropbox/Data/facebook_combined.txt', create_using = nx.Graph(), nodetype=int) print(nx.info(G_fb)) We read in the file and construct the Graph:ĭf = pd.read_csv('/Users/jankirenz/Dropbox/Data/facebook_combined.txt') df.info()

You can download the required facebook_combined.txt file from the Stanford University site. Let us start with the Facebook data, for our analysis here we will use Facebook combined ego networks dataset, it contains the aggregated network of ten individuals’ Facebook friends list. Learning to Discover Social Circles in Ego Networks. Thus, using the anonymized data it is possible to determine whether two users have the same political affiliations, but not what their individual political affiliations represent. For instance, where the original dataset may have contained a feature “political=Democratic Party”, the new data would simply contain “political=anonymized feature 1”. Also, while feature vectors from this dataset have been provided, the interpretation of those features has been obscured. The dataset includes node features (profiles), circles, and ego networks.įacebook data has been anonymized by replacing the Facebook-internal ids for each user with a new value. Facebook data was collected from survey participants using this Facebook app. This dataset consists of ‘circles’ (or ‘friends lists’) from Facebook. Sorted(betCent, key=betCent.get, reverse=True) Nx.draw_networkx(G_symmetric, pos=pos, with_labels=True, nx.betweenness_centrality(G_symmetric) pos = nx.spring_layout(G_symmetric)īetCent = nx.betweenness_centrality(G_symmetric, normalized=True, endpoints=True) It has options to select if we want betweenness values to be normalized or not, weights to be included in centrality calculation or not, and to include the endpoints in the shortest path counts or not. Networkx has the function betweenness_centrality() to measure it for the network. An individual at such a strategic position can influence the whole group, by either withholding or coloring the information in transmission. The nodes with high betweenness centrality can have a strategic control and influence on others. The nodes with high betweenness centrality play a significant role in the communication/information flow within the network. It quantifies how many times a particular node comes in the shortest chosen path between two other nodes. It represents the frequency at which a point occurs on the shortest paths that connected pair of points. The Betweenness Centrality is the centrality of control. Nx.draw_networkx_labels(G_weighted, pos, font_size=20, font_family='sans-serif') Nx.draw_networkx_edges(G_weighted, pos, edgelist=esmall,width=6, alpha=0.5, edge_color='b', style='dashed') Nx.draw_networkx_edges(G_weighted, pos, edgelist=elarge,width=6)

Nx.draw_networkx_nodes(G_weighted, pos, node_size=700) Pos = nx.circular_layout(G_weighted) # positions for all nodes G_weighted.add_edge('George', 'Steven', weight=4) elarge = > 8]Įsmall = <= 8] G_weighted.add_edge('George', 'John', weight=8) G_weighted.add_edge('Michelle','Marc', weight=1) G_weighted.add_edge('Laura', 'Michelle',weight=1) G_weighted.add_edge('Steven', 'Michelle',weight=1) G_weighted.add_edge('Steven', 'John', weight=11) G_weighted.add_edge('Steven', 'Marc', weight=8) G_weighted.add_edge('Steven', 'Laura', weight=25) Let us make one again of the employees, but this time we add weight to the network, each edge has a weight signifying the number of projects they have done together. Till now we had networks without weights, but it is possible that networks are made with weights, for example, if in our initial network we consider the number of projects done together as a weight, we will get a weighted Network.

USING ITHOUGHTSX TO CREATE SOCIAL NETWORK MAP CODE

USING ITHOUGHTSX TO CREATE SOCIAL NETWORK MAP DOWNLOAD

Using ithoughtsx to create social network map