What is graph analytics?
Making
the complex simple
MAY 17, 2016
Managing Director of Intelligent Business Strategies
Limited, Intelligent Business Strategies
Graph
analytics, also known as network analysis, is an exciting new area for
analytics workloads. To some extent, the business driver that has shone a
spotlight on graph analysis is the ability to use it for social network
influencer analysis. Marketing managers in particular are interested in
identifying social network influencers because they are potential targets for
marketing campaigns trying to trigger chain reactions among social network
communities to buy products and services. However, many more potential use
cases for graph analysis exist, including several widely used business use
cases:
Detecting
financial crimes such as money laundering
Spotting fraud,
which applies to fraudulent transactions and applications in banking, benefits
fraud in government, applications and claims fraud in insurance and fraudulent
activities in telecommunications
Preventing
crime and performing counterterrorism
Applying
influencer analysis in social network communities
Performing grid
and network quality of service such as identifying weaknesses in power grids,
water grids and transportation networks as well as helping prevent cybercrime
attacks on computer networks
Optimizing
routes in the airlines and retail and manufacturing industries as well as for
supply distribution chains and logistics
Conducting
research in life sciences (bioinformatics) including medical research, disease
pathologies and so on
Different
kinds of graph analysis
Four widely
used types of graph analytics include path analysis, connectivity analysis,
community analysis and centrality analysis:
Path
analysis: This type of analysis can be used to determine the
shortest distance between two nodes in a graph, for example. An obvious use
case is route optimization that is particularly applicable to logistics, supply
and distribution chains and traffic optimization for smart cities.
Connectivity
analysis: This type of graph analysis can be applied tor
determining weaknesses in networks such as a utility power grid. It also
enables comparing connectivity across networks.
Community
analysis: Distance and density–based analysis is used to find
groups of interacting people in a social network, for example, and identifying
whether they are transient and predicting if the network will grow.
Centrality
analysis: This analysis type enables
identifying relevancy to find the most influential people in a social network,
for example, or to find most highly accessed web pages—such as by using the
PageRank algorithm.
Graph analytics
algorithms exist to help perform these kinds of analyses. Graph algorithms
include PageRank, Betweenness Centrality, Eigen Centrality and Closeness.
Anatomy
of graphs in graph analysis
In this
discussion of graph analytics, we are not talking about visualizations. Graphs
are made up of nodes and edges. The mathematical term for nodes is vertices.
You may see articles or hear mathematically oriented people talking about a vertex or vertices and edges:
Bringing this
concept back to the real world, nodes or vertices can be people, such as
customers and employees; affinity groups such as LinkedIn or meet-up groups;
and companies and institutions. They can also be places such as airports,
buildings, cities and towns, distribution centers, houses, landmarks, retail
stores, shipping ports and so on. Vertices can also be things such
as assets, bank accounts, computer servers, customer accounts, devices, grids,
molecules, policies, products, twitter handles, URLs, web pages and so on.
Edges can be
elements that represent relationships such as emails, likes and dislikes, payment
transactions, phone calls, social networking and so on. Edges can be directed,
that is, they have a one-way direction arrow to represent a relationship from
one node to another—for example, Mike made a payment to Bill;
Mike follows Corinne on Twitter. They can also be
non-directed—for example, M1 links London and Leeds—and weighted—for example,
the number of payments between these two accounts is high. The time between two
locations is also an example of a weighted relationship.
Graph analysis
may highlight dominant edges. For example, a large number of payments between
bank accounts may indicate money laundering activity. Consider a social network
analysis example: you may begin by identifying communities within a social
network and then for one or more specific communities you may want to identify
the influencer(s) within that community and the people linking multiple
communities. Graph analytics using centrality analysis can be applied in this
scenario.
Doing graph analysis in NoSQL of graph databases,
for example, is also possible, such as using IBM Graph on the IBM Bluemix
platform. In big data environments, graph analysis can be done at scale using
Apache Spark GraphX by loading data into memory and running graph analysis in
parallel. And connecting Spark to graph databases using the Tinkerpop interface
is also possible. In this way, you can take data out of graph data=================================================== For a great satire on email, please see the following: https://www.youtube.com/watch?v=HTgYHHKs0Zwscoop_post=bcaa0440-2548-11e5-c1bd-90b11c3d2b20&__scoop_topic=2455618===============================================
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