Social Network Analysis Pests

Successful biological control can be the result of increased natural enemy or the result of trophic interactions occurring between crops and pests (Straub et al., 2008). Generalist pests can co-occur in the same area on two or more cultivated crops during a single growing season. The trophic interactions between crops and pest insects may be built as a network between crops and pests. Although it is analogous to a food web or trophic cascade, with the crop as the central figure, a network between crops and pests is a system with sub-units that are linked into the whole, such as pests linked into a whole food web. This network could include a mathematical analysis of the connections among units. The complex interactions between whole units can

Social network analysis is a research field that studies the relationships linking persons, organizations, interest groups, states, etc., by analyzing the structure of these relationships (Scott, 2012). Social network analysis has traditionally had a strong synergy with business models in certain industries, but has recently been widely used by biologists, mathematicians, and computer scientists (Proulx et al.,

Network analysis The structure of the connectedness among crops and pest insects, as a co-occurrence pattern, was examined using network analysis. Network analysis involves a set of procedures used to identify and measure structural properties of social systems based on relationships among entities in the system rather than on characteristics of the entities. A network can be thought of as a set of nodes with connections or links between these nodes. In our study, the nodes were crops and pest insects, and the links were undirected connections between nodes. In order to describe the topology of the resulting network, a set of measures, such as average node connectivity, average path length, diameter, clustering coefficient and modularity, were calculated (Newman, 2003). The average node connectivity was the average of the local node connectivity for all pairs of nodes in the network. The average path length was the average number of steps along the shortest paths for all possible pairs of network nodes. The diameter of the network was the longest of all the calculated shortest paths in the network. The clustering coefficient was a measure of the degree to which nodes in a graph tended to cluster together. The modularity was a measure of the structure of the network, and was designed to measure the strength of division of the network into