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Score internal links using two columns of "Source" and "Destination". This calculates various link importance metrics link degree centrality, betweenness centrality and PageRank.
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| import networkx as nx | |
| import pandas as pd | |
| def score_links(links_file, domain): | |
| """Score a network on links based on their importance and centrality. | |
| links_file: Path to the file having the links (needs a "Source" and | |
| "Destination" columns) e.g. ScreamingFrog's outlinks file. | |
| domain: Filter all links, making sure they all point to the domain you want. | |
| """ | |
| links = pd.read_csv(links_file) | |
| links = links[links['Type'].eq('Hyperlink') & | |
| links['Destination'].str.contains(domain, regex=False)] | |
| src_dest = links[['Source', 'Destination']] | |
| G = nx.DiGraph() | |
| for src, dest in src_dest.values: | |
| G.add_edge(src, dest) | |
| graph_df = pd.DataFrame({'url': G.nodes}) | |
| deg_cent = nx.degree_centrality(G) | |
| in_deg_cent = nx.in_degree_centrality(G) | |
| out_deg_cent = nx.out_degree_centrality(G) | |
| bet_cent = nx.betweenness_centrality(G) | |
| page_rank = nx.pagerank(G) | |
| graph_df['degree_centrality'] = [deg_cent[url] for url in graph_df['url']] | |
| graph_df['in_degree_centrality'] = [in_deg_cent[url] for url in graph_df['url']] | |
| graph_df['out_degree_centrality'] = [out_deg_cent[url] for url in graph_df['url']] | |
| graph_df['betweenness_centrality'] = [bet_cent[url] for url in graph_df['url']] | |
| graph_df['pagerank'] = [page_rank[url] for url in graph_df['url']] | |
| return graph_df |
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