Abstract
In this paper, we study how to detect the most influential users in the microblogging social network platform Twitter and their evolution over time. To this aim, we consider the Dynamic Retweet Graph (DRG) proposed in Amati et al. (2016) and partially analyzed in Amati et al. (IADIS Int J Comput Sci Inform Syst, 11(2) 2016), Amati et al. (2016). The model of the evolution of the Twitter social network is based here on the retweet relationship. In a DRGs, the last time a tweet has been retweeted we delete all the edges representing this tweet. In this way we model the decay of tweet life in the social platform. To detect the influential users, we consider the central nodes in the network with respect to the following centrality measures: degree, closeness, betweenness and PageRank-centrality. These measures have been widely studied in the static case and we analyze them on the sequence of DRG temporal graphs with special regard to the distribution of the $75\%$ most central nodes. We derive the following results: (a) in all cases, applying the closeness measure results into many nodes with high centrality, so it is useless to detect influential users; (b) for all other measures, almost all nodes have null or very low centrality and (c) the number of vertices with significant centrality are often the same; (d) the above observations hold also for the cumulative retweet graph and, (e) central nodes in the sequence of DRG temporal graphs have high centrality in cumulative graph.
Highlights
One of the fundamental and most studied features in a social network is the detection of central nodes, which can usually be considered as the most important nodes [7, 8, 13].This work was conducted in the Laboratory of Big Data of ISCOM-MISE (Institute of communication of the Italian Ministry for Economic Development)
We derive that the Dynamic Retweet Graph (DRG) allow to detect the most authoritative users, since: 1. in all cases the closeness centrality provides too many central nodes, it is useless to detect influential users; 2. with regard the other measures, almost all nodes have null or very low centrality; 3. vertices with centrality values above 75% of the maximum is a small set and they are often repeated in the three centrality measures; 4. the above observations hold for the static graphs; 5. central nodes in the sequence of DRG temporal graphs have high centrality in static graphs
In this paper we have studied the evolution of four centrality measures on the DRG temporal retweet graphs based on three datasets: Table 6 Relative betweenness centrality in the cumulative World Series dataset of nodes that are central in the temporal graphs
Summary
One of the fundamental and most studied features in a social network is the detection of central nodes, which can usually be considered as the most important nodes [7, 8, 13]. To study how influential users evolve over the time, we analyze the distribution of the centrality measures on an temporal evolutionary model of the Twitter network, the Dynamic Retweet Graph (DRG) proposed in [3] and partially analyzed in [2, 4]. We study the evolution of the most influential users in the microblogging social network platform Twitter with respect to four centrality measures (betweenness, degree, closeness, and PageRank) and we analyze their behavior on the DRG evolutionary model of the retweet social networks proposed in [3]. We derive that the DRGs allow to detect the most authoritative users, since: 1. in all cases the closeness centrality provides too many central nodes, it is useless to detect influential users; 2. with regard the other measures, almost all nodes have null or very low centrality; 3. vertices with centrality values above 75% of the maximum is a small set and they are often repeated in the three centrality measures; 4. the above observations hold for the static graphs (the cumulative DRG); 5. central nodes in the sequence of DRG temporal graphs have high centrality in static graphs
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