Applied Mathematics & Information Sciences

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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.