In this work, we revisit the classic Les Misérables benchmark network from the perspective of social communication. Using the 1998 film adaptation of the novel, we construct the publicly available Les Misérables Dialogue temporal text network, which explicitly distinguishes speakers, addressees, and other recipients of each utterance, along with its content and timing. Using the ten social dimensions model, we also derive multilayer network representations of the network, in which layers correspond to distinct relational dimensions. Beyond the network dataset, this work offers a general methodology for building similarly rich communication datasets from similar narrative sources.
Figures & tables
Component
Majority (0-1 flags)
Unanimous (0 flags)
Speaker
99.23%
97.81%
Addressee(s)
98.75%
94.42%
Recipient(s)
99.35%
90.63%
Table 1: Agreement during the independent validation of the utterance annotations.
Figure 1: Flattened communication network LMD-flat a . The 21 characters also present in the canonical Les Misérables network are highlighted and labelled by name.
Network
Nodes
Edges
⟨k⟩
σk
r
c
S
Les Misérables
77
254
6.60
6.04
-0.17
0.50
1.00
LMD-flat a
106
172
3.25
6.18
-0.40
0.09
0.91
LMD-flat r
116
345
5.95
8.39
-0.29
0.29
1.00
Table 2: Structural properties of the original Les Misérables co-occurrence network and the two flattened communication networks derived from our dataset. We report the number of non-isolate nodes and edges, mean degree ⟨k⟩ , standard deviation of the degree distribution σk , degree assortativity r , global clustering coefficient c , and relative size of the largest connected component S .
Layer
Edges
⟨k⟩
σk
r
c
S
d
Power
12 ( 19 )
0.21 ( 0.33 )
0.74 ( 1.12 )
-0.57 ( -0.41 )
0.00 ( 0.05 )
0.03 ( 0.03 )
2 ( 2 )
Similarity
1 ( 1 )
0.02 ( 0.02 )
0.13 ( 0.13 )
– ( – )
0.00 ( 0.00 )
– ( – )
– ( – )
Conflict
27 ( 62 )
0.47 ( 1.07 )
1.50 ( 3.05 )
-0.36 ( -0.36 )
0.10 ( 0.08 )
0.04 ( 0.06 )
3 ( 4 )
Identity
0 ( 0 )
0.00 ( 0.00 )
0.00 ( 0.00 )
– ( – )
0.00 ( 0.00 )
– ( – )
– ( – )
Support
28 ( 54 )
0.48 ( 0.93 )
1.98 ( 3.41 )
-0.62 ( -0.53 )
0.00 ( 0.01 )
0.02 ( 0.03 )
1 ( 3 )
Knowledge
9 ( 13 )
0.16 ( 0.22 )
0.79 ( 0.98 )
-0.74 ( -0.49 )
0.00 ( 0.06 )
0.03 ( 0.03 )
2 ( 2 )
Table 3: Structural properties of the multiplex network derivations, LMD-ML a0.8 and LMD-ML r0.8 (in parentheses). We report number of nodes and edges, mean degree ⟨k⟩ , standard deviation of the degree distribution σk , degree assortativity r , clustering coefficient c , and relative size of the largest strongly connected component S .
Figure 2: Distribution of classification scores for the ten dimensions across all messages.
Figure 3: Cumulative message frequency by social dimension over time, for the three most common character pairs. A message counts toward a social dimension if its score for that dimension exceeds 0.8, binned into 5-minute intervals. Color indicates the running total of messages within each bin. Each row shows one dyad, with the two columns giving the two directions of communication (sender → receiver).