"Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory."
Identifying and analyzing the relationships and connections between individuals, groups or organizations.
Network Theory: This topic covers the fundamental principles of network theory, including network structures, nodes, edges, and links, as well as different types of networks and their properties.
Social Network Analysis (SNA): SNA is the study of social structures through the use of network and graph theories. It examines network structures, dynamics, and properties to understand social phenomena, including communication patterns, power dynamics, and social influence.
Graph Theory: Graph theory is a branch of mathematics that studies networks and graphs. It covers the formal definitions of graphs, types of graphs, and graphs' properties, including connectivity, centrality, and clustering.
Network Visualization: This topic encompasses the use of visual representations to understand and analyze network data. Network visualization techniques include node-link diagrams, matrix representations, and network cartography.
Network Metrics: Network metrics are measures used to quantify different properties of networks, including degree, betweenness centrality, closeness centrality, and eigenvector centrality. They enable analysts to identify key nodes and structures within networks and track changes over time.
Network Topology: Network topology refers to the physical or logical arrangement of nodes and links in a network. This includes the identification of hubs, bridges, and other structural elements that affect network characteristics.
Network Dynamics: Network dynamics cover the changes that occur over time within networks. This includes the study of network growth, evolution, and decay, as well as the assessment of different network states and patterns.
Network Modeling: Network modeling involves the creation of mathematical models and simulations to represent and analyze network phenomena. This includes the use of tools such as network diffusion models, network flow models, and network optimization models.
Network Data Collection: This topic covers the different methods for collecting data on networks, including surveys, social media analysis, and data mining. It also includes discussions on data quality, data cleaning, and data preprocessing.
Network Security: Network security covers the different threats, vulnerabilities, and attacks commonly encountered in network environments. This includes the study of protocols, encryption, firewalls, and intrusion detection systems.
Social Network Analysis (SNA): This technique is used to understand the relationships between individuals or groups in social and professional networks. SNA examines the ties and links that connect people and entities in the network.
Link Analysis: Link analysis is used in criminal investigations and intelligence analysis. It involves identifying the links between individuals, organizations, and other entities, and analyzing the patterns of these links to uncover hidden associations and relationships.
Network Mapping: Network mapping is the process of creating a visual representation of a network. This technique is used to identify the nodes and links in a network, and to identify the strength of connections between them.
Entity Link Analysis: This technique is used to identify the relationships between entities such as people, addresses, and phone numbers. It is often used in financial analysis to uncover patterns of fraud or money laundering.
Text Network Analysis: This technique is used to analyze large volumes of text data, such as emails or social media posts. It involves identifying the relationships between words and phrases, and analyzing the patterns of these relationships to uncover insights and trends.
Flow Network Analysis: Flow network analysis examines the flow of goods, people, or information through a network. It is often used in transportation planning and logistics management to optimize the flow of resources through a system.
Spatial Network Analysis: This technique is used to analyze the relationships between locations and geospatial data. It involves identifying the spatial relationships between entities, and analyzing the patterns of these relationships to identify trends and patterns.
Temporal Network Analysis: Temporal network analysis examines the changing relationships between entities over time. It is often used in historical analysis and predictive modeling to identify patterns and trends in the data.
"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, meme spread, information circulation, friendship and acquaintance networks, peer learner networks, business networks, knowledge networks, difficult working relationships, collaboration graphs, kinship, disease transmission, and sexual relationships."
"These networks are often visualized through sociograms in which nodes are represented as points and ties are represented as lines."
"It has also gained significant popularity in the following - anthropology, biology, demography, communication studies, economics, geography, history, information science, organizational studies, political science, public health, social psychology, development studies, sociolinguistics, and computer science, education and distance education research."
"The advantages of SNA are twofold. Firstly, it can process a large amount of relational data and describe the overall relational network structure."
"System and parameter selection to confirm the influential nodes in the network, such as in-degree and out-degree centrality."
"Through analyzing nodes, clusters, and relations, the communication structure and position of individuals can be clearly described."
"Social network analysis has emerged as a key technique in modern sociology."
"Examples of social structures commonly visualized through social network analysis include... disease transmission."
"These networks are often visualized through sociograms, in which nodes are represented as points and ties are represented as lines, [including] collaboration graphs."
"[SNA] has gained significant popularity in anthropology, biology, demography, communication studies, economics, geography, history, information science, organizational studies, political science, public health, social psychology, development studies, sociolinguistics, computer science, education, and distance education research."
"It has also gained significant popularity in... social psychology."
"SNA context and choose which parameters to define the 'center' according to the characteristics of the network."
"Examples of social structures commonly visualized through social network analysis include... knowledge networks."
"Examples of social structures commonly visualized through social network analysis include... meme spread."
"Examples of social structures commonly visualized through social network analysis include... difficult working relationships."
"It has also gained significant popularity in... political science."
"...is now commonly available as a consumer tool (see the list of SNA software)."
"It has also gained significant popularity in... education and distance education research."