analytics is a category of tools used to apply algorithms that
will help a data analyst understand the relationship between graph database
The structure of a graph is made up of nodes (also known as vertices) and edges. Nodes denote points in the graph data. For example, accounts, customers, devices, groups of people, organizations, products or locations may all be represented as a node. Edges symbolize the relationships, or lines of communication, between nodes. Every edge can have a direction, either one-way or bidirectional, and a weight, to depict the strength of the relationship.
Once the graph database is constructed, analytics can be applied. The algorithms can be used to identify values or uncover insights within the data such as the average path length between nodes, nodes that might be outliers and nodes with dominant activity. It can also be used to arrange the data in new ways such as partitioning information into sections for individual analysis or searching for nodes that meet specific criteria.
Some common tools used to create graph analytics include Apache Spark GraphX, IBM Graph, Gradoop, Google Charts, Cytoscape and Gephi.
Types of graph analytics
There are four main types of analytics that can be applied to graphs:
Graph analytics can be used for a variety of applications, such as:
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From Wikipedia, the free encyclopedia
Catfishing is a type of deceptive activity where a person creates a sock puppet social networking presence, or fake identity on a social network account, usually targeting a specific victim for deception.
Catfishing is often employed for romance scams on dating websites. Catfishing may be used for financial gain, to compromise a victim in some way, or simply as a form of trolling or wish fulfillment.
Catfishing media has been produced, often centering around victims who wish to identify their catfisher
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