Centrality Analysis of undirected graph

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I'm doing a Social Network Analysis of this dataset with NetworkX and I want to make a degree and closeness centrality analysis.

The graph I obtain is undirected (graph.is_directed() returns false), and I have node 1 with degree 593 but it has 0 as target and weight on the edges csv. The graph is undirected so I expect node 1 to be the central node but it's not and I don't get why (the dataset is based on the animated series The Simpson so I bet I know who is the most central character).

I'm afraid the analysis ends up unreliable this way.

---edit

This is the code where I import and create the graph.

dfN=pd.read_csv('gdrive/My Drive/SNA/simpsonsNodes.csv')
dfE=pd.read_csv('gdrive/My Drive/SNA/simpsonsEdges.csv')
df = pd.merge(left=dfN, right=dfE, left_on="Id", right_on='Source', how='outer').drop(['Id', 'Type'], axis=1)
df.columns = ['Name', 'Source', 'Target', 'Weight']
df = df.fillna(0)
df = df.astype({'Source':'int', 'Target':'int', 'Weight':'int'})
df

graph = nx.from_pandas_edgelist(df, 'Source', 'Target', edge_attr='Weight', create_using=nx.Graph() )
print(graph.is_directed())

I dropped two columns: id because is redundant and Type because it's "Undirected" for every row so I don't really need it.

I used df = df.fillna(0) because node with id = 1 had source, target and weight as NaN so I converted it and used df.loc[0,"Source"]=1 to insert 1 as its source.

sna social-networking networkx graph data-science
1 Answers

The Homer as a NaN edge is an artifact of the outer merge and the asymmetry of the edges file: Homer appears as node 1, so appears as a target plenty often, but never as a source. Dropping the artifact row, or doing an inner merge, should take care of that. As it stands, you artificially created a new node labeled 0 with just the one edge joining it to Homer; but since it has weight 0 also, it probably won't affect any algorithms that take weight into account.

Doing the merge at all is a little odd to me: you only end up with names associated with the sources, and not the targets. Anyway, they don't modify the graph in a significant way.

I ran the betweenness centrality algorithm (oops, without weights), and Homer does indeed end up the most central, with a relative score of 0.259 (Marge and Bart at 0.177 and 0.176, then Lisa at 0.155, then a sharp dropoff to 0.022 for...Lenny?...). With weights, the scores are changed, but the order among the top four is the same.

Ben Reiniger0
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