Analysis in link charts helps provide a better understanding of how data is connected and how these connections relate to the structure of the network. Networks can consist of anything from social media connections, Order of Battle, computer networks, and a variety of transaction-type data. Some common networks that can be created include the following:
- Social media connections of given users to help identify who major influencers are on a given platform
- The structure and organization of a criminal or terrorist network
- Transaction data from financial institutions to help conduct fraud detection and anti-money laundering activities
- The command structure of a military unit
- Server logs to help identify potential malicious cyberactivity
- People with whom a sick person has interacted as well as the location of the occurrence, to identify the potential spread of a disease
Obtaining data and creating a link chart is the first step in a network. A network consists of entities and relationships, and multiple networks can be part of a link chart. For more information on creating a link chart, see Create a link chart.
The following are the primary types of analysis that can be performed in the context of a link chart in ArcGIS Pro:
- Centrality—Represents basic statistics in a network. There are three centrality metrics implemented for a link chart. These metrics are betweenness, closeness, and degree.
- Cluster—Partitions the network into like areas based on differing factors determined by the specific algorithm. The four types of clusters implemented for a link chart are Biconnected Component, Edge Betweenness, Hierarchical, and K-Means.
- Neighborhood—Identifies direct connections from a given node out to a certain radius.
- Path—Finds how entities are connected and returns the specific set of entities that will connect given nodes.