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Finally, we characterize the clusters in the network of replies using the same metrics of the above section.

In this section we present the results of detecting and characterizing the major clusters in the network of replies, i.e., the online party discussion networks.

Properties Our scheme Localized [6] Cluster-based [9] Size of Replies (e.g., RREP) Small Large Small Scalable (certificate management) Yes No Yes Number of paths between two nodes One Multiple Multiple Path discovery Easy Hard Mediocre Resistance to DoS attacks Yes No No Join-leave Easy Hard Mediocre.

An interaction matrix A is presented in Fig. 12 where, now, an entry (A_{i,j}) is the number of replies from users from cluster (i^mathrm{rt}) to users from cluster (j^mathrm{rt}).

In addition, the N-Louvain method ((N=100, varepsilon =0.5)) in the network of replies detects other clusters which are worth examining.

We present in Table 8 how many users from each reply cluster (rows) belong to each retweet cluster (columns); i.e., the party distribution of reply clusters.

For better readability, the label of public users is shown Fig. 12 Normalized amount of replies between users from retweet clusters.

To answer this second question, we will therefore examine the online party discussion networks by analyzing the network of replies and comparing the structure of clusters to the ones from the network of retweets.

In the network of replies we also find two BeC clusters: one around the candidate account @adacolau (hereafter ({rm BeC textc ^{rm{rp}}})), and another around the party account @bcnencomu (hereafter ({rm BeC textc ^{rm{rp}}})).

"A little more," Clara Juárez replied, wrapping a cluster of leaves with a rubber band.

A Packet of Replies.

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