Network Analytics is the process of using text analytics methods to analyze networks. This can be done for a variety of purposes, such as identifying influencers, understanding how information flows or finding communities.
Wayne Zachary’s 1967 paper “A Small World Network” is often cited as the first use of the term “small-world network.”
Benefits of Network Analytics
There are many benefits to using network analytics. First, it can help you to identify influencers in a network. These are the people who have the most connections and who are the most central to the network. Second, it can help you to understand how information flows through a network. This is important for understanding things like the spread of rumors or the diffusion of innovation. Third, it can help you to find communities in a network. This is useful for things like marketing or targeted outreach.
Elements of Network Analysis
There are a few key elements to network analysis that are important to understand. First, networks are made up of nodes and edges. Nodes are the individual items in the network, while edges are the connections between them. In a social network, for example, nodes might be people and edges might be relationships between them.
Second, networks can be directed or undirected. In a directed network, the edges have a direction, while in an undirected network, they do not. For example, in a social network, the edge between two people might be directional if one is following the other on Twitter. However, if they are friends on Facebook, the edge between them is undirected.
Third, networks can be weighted or unweighted. In a weighted network, the edges have a weight assigned to them, while in an unweighted network, they do not. The weight can represent anything, such as the strength of a relationship or the number of interactions between two nodes.