# Leveraging Social Network Analysis for Crowdsourced Software Engineering Research
## 3 Data and Methods
### Social Network Analysis  
A social network is a network of relationships with two basic elements: nodes (actors or participants within the network) and the edges (ties, or links) connecting them [6]. In the network model, a node is represented by a circle, and the relationship between nodes is represented by an arrow or a line from the source node to the target node. Nodes can be people or whole organizations, and relationships can be collaborations, friendships, or links based on the exchange of information or knowledge [6]. Links can be directional, with the direction represented by an arrow, or nondirectional.  
Social network analysis (SNA) is the process of investigating the social structure of relationships within networks and studying the impact of the structure on the behaviors, attitudes, and performances of individual actors or groups. Analyzing the structural characteristics of social networks using a set of metrics and theories leads to network analysis, which is used to understand the patterns of relationships between nodes to confirm an underlying social structure [5]. There are several implementations for SNA in different fields, including business, communications, information science, economics, physics, and biology.  
Social networks have different properties and metrics that can be described as structural, rational, or functional properties [23; 24; 25]. Structural properties depend on the nodes and the relationships between the nodes. It includes factors such as the density and size of the social network. The rational characteristics are the links between pairs of nodes and can be described in terms of intensity, reciprocity, and multiplexity. Functional properties are transactional meanings, such as how two actors might deal with each other. Some of the most important social network properties are:  
1. Size: The number of nodes in a network.
2. Density: The number of direct links in a network in proportion to the total number of possible links. It is a number between 0 and 1.0, and it indicates greater density if it is close to 1.0 and sparsity otherwise. It is 2 g/N (N - 1) where g is the number of links and N is the number of nodes in the network.
3. Diameter: The shortest distance in a network.
4. Tie strength: The strength of the link between nodes in a network. Nodes that are linked with each other will have high intensity, and intense ties tend to be situated within network clusters.
5. Community detection: To help us to discover the communities that are collaborating the most in a network.
6. Centrality: This includes a number of metrics that aim to determine the importance or influence of a given node in a social network. Centrality can be evaluated at both the individual level and the network level. At the individual level, it measures the proportion of individual relationships to other nodes in the network. At the network level, it measures the degree to which network links are concentrated in one, a few, or several nodes [26]. Some important centrality metrics are:  
1. Degree centrality: This is the simplest and most used metric. The degree centrality of a node is the number of direct links or ties from a node to other nodes [13]. The most popular and important node in the social network tends to have a high degree centrality because it has many ties to other nodes in the network.
2. Closeness centrality: This is the sum of the length of the shortest paths of a given node to all other nodes in the network. The node with the fewest steps to all other nodes in the network will be the most central node with the highest metric of closeness centrality.
3. Betweenness centrality: This is the number of shortest paths that pass through a given node. Nodes with high betweenness centrality have more control between the nodes in the network; that is, they have more influence over the flow of information within a network.
4. Eigenvector centrality: This measures the importance of a given node based on its connections.