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Social Network Analysis and Its Applications in Agriculture Popular Article www.vigyanvarta.com Vol-1 Issue-6 Naresh and Tamta (2020) Social Network Analysis and its Applications in Agriculture Naresh Kumar. B1 and Meenakshi Tamta2 1Ph.D Scholar, Department of Extension Education, PAU, Ludhiana, Punjab 2Ph.D Scholar, Department of Apparel and Textile Science, PAU, Ludhiana, Punjab Corresponding Author Naresh Kumar. B Email: [email protected] OPEN ACCESS Keywords Social network analysis, nodes, ties, adjacency matrix, Sociomatrix and SNA tools. How to cite this article: Naresh Kumar, B. and Tamta, M. 2020. Social Network Analysis and its Applications in Agriculture. Vigyan Varta 1(6): 23-27. ABSTRACT Social network analysis is a connection between individuals or institutes or organizations which are connected through vertex (nodes) and edges (ties). Vertexes are the individual actors within the networks, and edges are the relationships between the actors. Social network analysis is a graphical representation of the relationship. SNA with its wide range of utility has evolved with applications in various disciplines. some important terminology used in social network analysis were nodes, ties, network, multiplex networks, weighted ties, geodesic distance, degree centrality, betweenness centrality, Eigenvector centrality and closeness centrality. Social network analysis mainly two types, edge list data and Sociomatrix (also known as an Adjacency Matrix). Social network analysis has a wide range of application in Social sciences, adoption studies, monitoring and impact assessment and value chain. Various tools used for social network analysis were Netdraw, Pajek, Gephi, NodeXL, GraphStream, NetworKit. UNISoN, SocioViz and UCI etc. INTRODUCTION reluctance, and sex, knowledge or prestige and relationship of beliefs. social network is a social structure made up of individuals (or Social network analysis views social A organizations) called "nodes" that are relationships in terms of network theory (connected) to one or more specific types of consisting of nodes and ties (also called edges, interactions, such as friendship, kinship, links, or connections). Nodes are the individual common interest, financial exchange, actors within the networks, and ties are the 23 | P a g e Popular Article www.vigyanvarta.com Vol-1 Issue-6 Naresh and Tamta (2020) relationships between the actors. The resulting evolved through various phases and overtime graph-based structures are often very complex. turned to be a data analysis technique with There can be many kinds of ties between the wider applications. nodes. Research in a number of academic fields has shown that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals. In its simplest form, a social network is a map of specified ties, such as friendship, between the nodes being studied. The nodes to which an individual is thus connected are the social contacts of that individual. The network can also be used to measure social capital – the value that an individual gets from the social network. These Figure 1. Lineage of Social network analysis concepts are often displayed in a social network (Scott, 2000). diagram, where nodes are the points and ties are the lines. Some Definitions Social Network Analysis (SNA) is a Before we can get started we need to define methodology to map and qualifying actors some terminology so we can use a consistent (nodes) and their relationship in a network. language when talking about social networks: SNA with its wide range of utility has evolved with applications in various disciplines. The 1. Actor: also called a node or a vertex, network perspective is becoming a key referrers to an individual or organisation that approach in social and biological sciences can have relationships with other individuals or (Borgatti and Li, 2008). Origin of social organisation. network analysis dates back to ancient Greeks, but major developments occurred in 1930’s 2. Tie: also called a relation or edge, describes (Scott, 2000). Kohler (1925) works on mind a particular, well specified, relationship and other gestalt tradition researchers work was between two Actors. This could refer to a the basis for social network theory. Other three relationship like “went to the same school” or gestalt scientist Moreno (sociogram), Lewin “likes potato chips” or something like “likes” or (group behavior) and Heider (balance theory) “trades with”. Ties can be un-directed (like further developed the theory. Moreno’s went to the same school), when the relationship sociogram allowed researchers to visualize means the same thing to both actors. Ties can relations among different groups. Lewin (1951) also be directed (such as “looks up to”) and further employed mathematical techniques either one directional or bidirectional. such as topology and set theory to explore the 3. Network: also called a Graph, particularly social space, but Koenig (1936) graph theory in the physics and CS literature, refers to a provided the crucial breakthrough in collection of Actors and the Ties between application of mathematical concept in them. sociometric analysis. On the other hand, anthropologist Radcliffe-Brown developed in a 4. Multiplex networks: are networks where relatively nontechnical form of social network more than one kind of tie is present. analysis. Other Anthropologist and sociologist like Warner and Mayo build on his concepts. It 24 | P a g e Popular Article www.vigyanvarta.com Vol-1 Issue-6 Naresh and Tamta (2020) 5. Weighted Ties: Just as networks can contain actor) between those actors]. This intuitively multiple different kinds of edges between measures the degree to which information or actors, they can also contain relationships of relationships have to flow through a particular varying strength. For example, A might like B actor and their relative importance as an a whole lot, but B and C only like each other intermediary in the network. moderately. 3. Closeness centrality: measures how many 6. Group: A group in a network is just a subset steps (ties) are required for a particular actor to of the actors which share some characteristic in access every other actor in the network. This is common. If we were to look at an measured as 1 divided by the sum of geodesic organizational network, one group could be distances from an actor to all alters in the made up of all actors that work in the human network. The measure will reach its maximum resources department. The definition of groups for a given network size when an actor is as commonality on some salient trait allows us directly connected to all others in the network to examine a number of network hypotheses and its minimum when an actor is not connected and defined useful measures that are to any others. This captures the intuition that conditional on knowing the group membership short path lengths between actors signal that of actors. For example we might want to test a they are closer to each other. Note that this hypothesis about the number of friendship ties measure is sensitive to network size and is between workers at a company who are part of decreasing in the number of actors in the different departments versus those in the same network. This makes intuitive sense in many departments. situations because it gets more difficult to maintain close relationships with all members 7. Geodesic Distance: is defined as the least of the network as the network grows but can number of connections (ties) that must be also be corrected for by multiplying by the traversed to get between any two nodes. number of actors in the network. Properties of Nodes 4. Eigenvector centrality: measures the degree to which an actor is connected to other well- 1. Degree Centrality: is the most basic network connected actors. It takes advantage of a measure and captures the number of ties to a mathematical property of networks given actor. For un-directed ties this is simply a (represented as adjacency matrices) that allows count of the number of ties for every actor. For for the easy calculation of how well connected directed networks, actors can have both an actor is to other well-connected actors. indegree and outdegree centrality scores. As the While we will not get into the details of its name implies, centrality measures how central calculation, this measure captures the value of or well-connected an actor is in a network. This having a lot of friends in high places. theoretically signals importance or power and increased access to information or just general 5. Brokerage: describes the position of actors activity level and high degree centrality is such that they occupy an advantageous position generally considered to be an asset to an actor. where they can broker interactions between other actors in the network. Brokerage 2. Betweenness Centrality: is roughly defined Centrality is then a measure of the degree to as the number of shortest paths between alters which an actor occupies a brokerage position that go through a particular actor. More across all pairs of alters. It is meant to capture precisely, it is the sum of [the shortest path the intuition that a broker serves as a go- lengths between every set of alters where the between and thus can gain benefits from their path goes through the actor we are calculating position as an intermediary. There are five the measure for divided by the shortest path lengths (not necessarily through the target 25 | P a g e Popular Article www.vigyanvarta.com Vol-1 Issue-6 Naresh and Tamta (2020) kinds of brokerage relationships, each of which interactions of the individuals in the group. It we will discuss briefly below: helps to understand the actors and the relationship between them in a specific social (a) A Coordinator is an Actor in the same context.
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