Link prediction

Link prediction is an area of network analysis that seeks to predict possible future connections between nodes, clusters, or other groups in a network. The goal is to anticipate how users or devices will interact before those connections are made. The purpose of link prediction is to provide knowledge and insight about a network prior to its development.

Link prediction is most often modeled on existing networks and examples of real-world data. Generally, link prediction relies on graph theory methods and algorithms to evaluate and interpret the data. Using measurements of various centrality metrics, nodes can be clustered and ranked relative to each other to help determine criteria for future relationships. For example, common centrality metrics used in link prediction such as degree centrality are used to identify highly connected nodes in a network, as well as identify nodes that are likely to form new links. Other metrics, such as betweenness centrality, are used to identify strongly connected nodes that can act as bridge nodes between two groups.

Link prediction can be used to provide insight into a wide range of topics, including collective behavior, online communication networks, and business partnerships. Businesses use link prediction to build upon existing relationships and to create new partnerships. In addition to social network analysis, link prediction has been used in various areas of studies including biology, computational chemistry, and recommendation systems.

Link prediction can be used to analyze existing relationships, predict new links, and possibly to identify critical vulnerabilities in networks. By understanding the dynamics of a network, analysis can lead to informed decisions about how the network will evolve in the future. By using link prediction to anticipate future connections and interactions, organizations can better prepare for any potential issues, allowing them to be more proactive than reactive.