When such an adjacency is found, the length of the quote is added to the edge weight , under the hypothesis that the significance of the relationship between two individuals is proportional to the length of the dialogue that they exchange.

Finally, we normalized each edge’s weight by the length of the novel.

These coefficients are used for the edge weights .

We then construct a network where characters are vertices and edges signify an amount of bilateral conversation between those characters, with edge weights corresponding to the frequency and length of their exchanges.

Concretely, the semantic-graph is defined as follows: A semantic-graph is a weighted graph G = (V, E), where each node represents a distinct concept; and each edge between a pair of nodes represents the semantic relation between the two concepts corresponding to these nodes, with the edge weight indicating the strength of the semantic relation.

That is, for each pair of extracted concepts, we identify whether there are semantic relations between them: 1) If there is only one semantic relation between them, we connect these two concepts with an edge, where the edge weight is the strength of the semantic relation; 2) If there is more than one semantic relations between them, we choose the most reliable semantic relation, i.e., we choose the semantic relation in the knowledge sources according to the order of WordNet, Wikipedia and NE Co-concurrence corpus (Suchanek et al., 2007).

To simplify the description, we assign each node in se-mantic-graph an integer index from 1 to |V1 and use this index to represent the node, then we can write the adjacency matrix of the semantic-graph G as A, where A[i,j] or Ail- is the edge weight between node i and node j.