Analysis of Neighbourhoods in Multi-layered Dynamic Social Networks
Piotr Br\'odka, Przemys{\l}aw Kazienko, Katarzyna Musia{\l}, Krzysztof, Skibicki

TL;DR
This paper introduces new structural measures for analyzing multi-layered social networks, investigates their dynamics, and evaluates their effectiveness on real-world data to understand complex human interactions.
Contribution
It proposes novel structural measures for multi-layered social networks and explores their dynamic properties using real-world data, addressing limitations of existing methods.
Findings
New cross-layer clustering coefficient and degree centrality measures introduced.
Analyzed the dynamics of neighborhoods across five social network layers.
Evaluated measures on real-world social network dataset.
Abstract
Social networks existing among employees, customers or users of various IT systems have become one of the research areas of growing importance. A social network consists of nodes - social entities and edges linking pairs of nodes. In regular, one-layered social networks, two nodes - i.e. people are connected with a single edge whereas in the multi-layered social networks, there may be many links of different types for a pair of nodes. Nowadays data about people and their interactions, which exists in all social media, provides information about many different types of relationships within one network. Analysing this data one can obtain knowledge not only about the structure and characteristics of the network but also gain understanding about semantic of human relations. Are they direct or not? Do people tend to sustain single or multiple relations with a given person? What types of…
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