Browse Definitions :
Definition

# graph analytics

Graph analytics is a category of tools used to apply algorithms that will help the analyst understand the relationship between graph database entries.

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:

1. Path analysis- This focuses on the relationships between two nodes in a graph. This type of graph analytics can help identify the shortest path between nodes, find the widest path between weighted nodes and calculate a spanning tree around a center point.
2. Connectivity analysis- This focuses on the weight of the edges between nodes. It can be applied to identify weaknesses in a system or anomalies such as abnormally high or low activity.
3. Community analysis- This focuses on the interactions between nodes. It clusters nodes into labeled groups of similar objects to help with organization.
4. Centrality analysis- This focuses on the relevancy of each node in a graph. It can be used to rank popularity or influence between nodes.

### Examples of applications for graph analytics

Graph analytics can be used for a variety of applications, such as:

• Detecting cybercrimes such as money laundering, identity fraud and cyberterrorism.
• Applying analysis to social networks and communities such as monitoring statistics and identifying influencers.
• Performing analysis on the traffic and quality of service for computer networks.
• Optimizing logistics for manufacturing and transportation industries.
• Determining page rank analytics and tracking their popularity or amount of clicks.
• Analyzing the parts of a software application and how they interact to find potential issues.
This was last updated in July 2019

• network traffic

Network traffic is the amount of data that moves across a network during any given time.

• dynamic and static

In general, dynamic means 'energetic, capable of action and/or change, or forceful,' while static means 'stationary or fixed.'

A MAC address (media access control address) is a 12-digit hexadecimal number assigned to each device connected to the network.

• Trojan horse

In computing, a Trojan horse is a program downloaded and installed on a computer that appears harmless, but is, in fact, ...

• quantum key distribution (QKD)

Quantum key distribution (QKD) is a secure communication method for exchanging encryption keys only known between shared parties.

• Common Body of Knowledge (CBK)

In security, the Common Body of Knowledge (CBK) is a comprehensive framework of all the relevant subjects a security professional...

• benchmark

A benchmark is a standard or point of reference people can use to measure something else.

• spatial computing

Spatial computing broadly characterizes the processes and tools used to capture, process and interact with 3D data.

• organizational goals

Organizational goals are strategic objectives that a company's management establishes to outline expected outcomes and guide ...

• talent acquisition

Talent acquisition is the strategic process employers use to analyze their long-term talent needs in the context of business ...

• employee retention

Employee retention is the organizational goal of keeping productive and talented workers and reducing turnover by fostering a ...

• hybrid work model

A hybrid work model is a workforce structure that includes employees who work remotely and those who work on site, in a company's...

• database marketing

Database marketing is a systematic approach to the gathering, consolidation and processing of consumer data.

• cost per engagement (CPE)

Cost per engagement (CPE) is an advertising pricing model in which digital marketing teams and advertisers only pay for ads when ...