Network analysis is still a growing field with a great deal of opportunity for new and transformative contributions. The term social network refers to the articulation of a social relationship, official or achieved, among individuals, families, households, villages, communities, regions, and so on. Each of them can play dual roles, acting both as a unit or node of a social network as well as a social actor
Social Network Analysis : Definition
Social network theory views a network as a group of actors who are connected by a set of relationships. Social networks develop when actors meet and form some kind of relation between each other. These can be of an informal as well as of a formal nature. Hereby actors are often people, but can also be nations, organizations, objects etc. Social Network Analysis (SNA) focuses on patterns of relations between these actors. It seeks to describe networks of relations as fully as possible. This includes teasing out the prominent patterns in such networks, tracing the flow of information through them, and discovering what effects these relations and networks have on people and organizations. It can therefore be used to study network patterns of organizations, ideas, and people that connected via various means in an online environment
“Social network analysis (SNA) means analyzing various characteristics of the pattern of distribution of relational ties as mentioned above and drawing inferences about the network as a whole or about those belonging to it considered individually or in groups”
Social network analysis views social relationships in terms of network theory consisting of nodes and ties (also called edges, links, or connections). Nodes are the individual actors within the networks, and ties are the relationships between the actors. The resulting graph-based structures are often very complex. There can be many kinds of ties between the nodes.
Network analysis is the study of social relations among a set of actors. It is a field of study — a set of phenomena or data which we seek to understand. In the process of working in this field, network researchers have developed a set of distinctive theoretical perspectives as well. Some of the hallmarks of these perspectives are:
- focus on relationships between actors rather than attributes of actors
- sense of interdependence: a molecular rather atomistic view
- structure affects substantive outcomes
- emergent effects
Network theory is sympathetic with systems theory and complexity theory. Social networks is also characterized by a distinctive methodology encompassing techniques for collecting data, statistical analysis, visual representation, etc.
Social relations can be thought of as dyadic attributes. Whereas mainstream social science is concerned with monadic attributes (e.g., income, age, sex, etc.), network analysis is concerned with attributes of pairs of individuals, of which binary relations are the main kind. Some examples of dyadic attributes:
- Kinship: brother of, father of
- Social Roles: boss of, teacher of, friend of
- Affective: likes, respects, hates
- Cognitive: knows, views as similar
- Actions: talks to, has lunch with, attacks
- Flows: number of cars moving between
- Distance: number of miles between
- Co-occurrence: is in the same club as, has the same color hair as
- Mathematical: is two links removed from
A social network is a social structure made up of individuals (or organizations) called “nodes”, which are tied (connected) by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige.
Figure: An example of a Social Network diagram
Metrics (Measures) in Social Network Analysis
Betweenness: The extent to which a node lies between other nodes in the network. This measure takes into account the connectivity of the node’s neighbors, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links
Bridge: An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph.
Centrality: This measure gives a rough indication of the social power of a node based on how well they “connect” the network. “Betweenness”, “Closeness”, and “Degree” are all measures of centrality.
Centralization: The difference between the numbers of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between numbers of links each node possesses.
Closeness: The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the “grapevine” of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network. The shortest path may also be known as the “geodesic distance”.
Clustering coefficient: A measure of the likelihood that two associates of a node are associates them. A higher clustering coefficient indicates a greater ‘cliquishness’.
Degree: The count of the number of ties to other actors in the network.
 Thomas Friemela N, “1 Applications of Social Network Analysis.” (2007).
 “Social Network Analysis: Theory and Applications”, available online at: https://www.politaktiv.org/documents/10157/29141/SocNet_TheoryApp.pdf
 Zaphiris, Panayiotis and Chee Siang Ang, “Introduction to Social Network Analysis”, In INTERACT (2), pp. 940-941. 2009.