neo4j.com1
Ebook
The #1 Platform for Connected Data
A Comprehensive Guide
to Graph Algorithms
in Neo4j
Mark Needham, Developer Relations Engineer at Neo4j
Amy E. Hodler, Analytics & AI Program Manager at Neo4j
Preface
Connectivity is the single most pervasive characteristic of today’s networks and systems.
From protein interactions to social networks, from communication systems to power grids,
and from retail experiences to supply chains – networks with even a modest degree of
complexity are not random, which means connections are not evenly distributed nor static.
This is why simple statistical analysis alone fails to suciently describe – let alone predict
– behaviors within connected systems. Consequently, most big data analytics today do
not adequately model the connectedness of real-world systems and have fallen short in
extracting value from huge volumes of interrelated data.
As the world becomes increasingly interconnected and systems increasingly complex,
it’s imperative that we use technologies built to leverage relationships and their dynamic
characteristics. Not surprisingly, interest in graph analytics has exploded because it was
explicitly developed to gain insights from connected data. Graph analytics reveal the
workings of intricate systems and networks at massive scales – not only for large labs but
for any organization. Graph algorithms are processes used to run calculations based on
mathematics specically created for connected information.
We are passionate about the utility and importance of graph analytics as well as the joy of
uncovering the inner workings of complex scenarios. Until recently, adopting graph analytics
required signicant expertise and determination, since tools and integrations were dicult
and few knew how to apply graph algorithms to their quandaries. It is our goal to help change
this. We wrote this ebook to help organizations better leverage graph analytics so they make
new discoveries and develop intelligent solutions faster.
While there are other graph algorithm libraries and solutions, we’ve chosen to focus on
the graph algorithms in the Neo4j platform. However, you'll nd this guide helpful for
understanding more general graph concepts regardless of what graph technology you use.
TABLE OF CONTENTS
Part I: Connected Data and
Graph Analysis 3
Making Sense of
Connected Data 4
The Rise of Graph Analytics 8
Neo4j Graph Analytics 14
Part II: Graph Algorithms
in Neo4j 17
Graph Algorithm Concepts 18
The Neo4j Graph
Algorithms Library 20
Pathnding and
Graph Search Algorithms 24
Centrality Algorithms 34
Community Detection
Algorithms 52
Graph Algorithms
in Practice 71
Conclusion 79
Appendix A:
Performance Testing 80
Appendix B: Installing the
Neo4j Graph Algorithms
Library 81
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