This paper focuses on semantic networks that represent the user opinions expressed by social media users on a given set of topics. These networks are found to follow a power-law degree distribution of nodes, with a few hub nodes and a long tail of peripheral nodes. While there exist consolidated approaches supporting the identification and characterization of hub nodes, research on the analysis of the multi-layered distribution of peripheral nodes is limited. In social media, hub nodes represent social influencers. However, the literature provides evidence of the multi-layered structure of influence networks, emphasizing the distinction between influencers and influence. The latter seems to spread following multi-hop paths across nodes in peripheral network layers. This paper proposes a visual approach to the graphical representation of peripheral layers. The core concept of our approach is to partition the node set of a graph into hub and peripheral nodes. Then, a modified force-directed method is applied to clearly display local multi-layered neighborhood clusters around hub nodes. Our approach is tested on a large sample of tweets from tourism domain. Our algorithm is visually compared with state-of-the-art network drawing techniques.
Francalanci, Chiara; Hussain, Ajaz; and Merlo, Francesco
"Representing Social Influencers and Influence using Power-Law Graphs,"
Applied Mathematics & Information Sciences: Vol. 09:
5, Article 29.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol09/iss5/29