Graph Analysis has become a groundbreaking way for organizations to look at their data and understand the relationships between them. For two years running, Gartner selected graphs as one of their top analytics and data trends because of the significant potential for value creation.
Graphs capture relationships and connections between entities. The relationships and connections between the entities are used in data analysis. Knowing how the data is connected, and building a graph to understand the relationships are becoming increasingly important because they make it easier to explore those connections and made new insights. For example, understanding how a person’s buying pattern is influenced by all the entities the person is connected with.
Centrality analysis: Estimates how important a node is for the connectivity of the network. It helps to estimate the most influential people in a social network or most frequently accessed web pages by using the PageRank algorithm.
Community detection: Distance and density of relationships can be used to find groups of people interacting frequently with each other in a social network. Community analytics also deals with the detection and behaviour patterns of communities.
Connectivity analysis: Determine how strongly or weakly connected two nodes are.
Path analysis: Examines the relationships between nodes. Mostly used in shortest distance problems.
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