Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory.[1] It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through social network analysis include social media networks,[2][3]meme spread,[4] information circulation,[5]friendship and acquaintance networks, peer learner networks,[6][7][8] business networks, knowledge networks,[9][10] difficult working relationships,[11]collaboration graphs, kinship, disease transmission, and sexual relationships.[12][13] These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines. These visualizations provide a means of qualitatively assessing networks by varying the visual representation of their nodes and edges to reflect attributes of interest.[14]
Analysis of social structures using network and graph theory
The advantages of SNA are twofold. Firstly, it can process a large amount of relational data and describe the overall relational network structure. tem and parameter selection to confirm the influential nodes in the network, such as in-degree and out-degree centrality. SNA context and choose which parameters to define the “center” according to the characteristics of the network. Through analyzing nodes, clusters and relations, the communication structure and position of individuals can be clearly described.[25]