Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Download to read offline
Sign up for a Scribd free trial to download now.
Download with free trialDownload to read offline
Sign up for a Scribd free trial to download now.
Download with free trialA graph is a data structure that links a set of vertices by a set of edges. Modern graph databases support multi-relational graph structures, where there exist different types of vertices (e.g. people, places, items) and different types of edges (e.g. friend, lives at, purchased). By means of index-free adjacency, graph databases are optimized for graph traversals and are interacted with through a graph traversal engine. A graph traversal is defined as an abstract path whose instance is realized on a graph dataset. Graph databases and traversals can be used for searching, scoring, ranking, and in concert, recommendation. This presentation will explore graph structures, algorithms, traversal algebras, graph-related software suites, and a host of examples demonstrating how to solve real-world problems, in real-time, with graphs. This is a whirlwind tour of the theory and application of graphs.
A graph is a data structure that links a set of vertices by a set of edges. Modern graph databases support multi-relational graph structures, where there exist different types of vertices (e.g. people, places, items) and different types of edges (e.g. friend, lives at, purchased). By means of index-free adjacency, graph databases are optimized for graph traversals and are interacted with through a graph traversal engine. A graph traversal is defined as an abstract path whose instance is realized on a graph dataset. Graph databases and traversals can be used for searching, scoring, ranking, and in concert, recommendation. This presentation will explore graph structures, algorithms, traversal algebras, graph-related software suites, and a host of examples demonstrating how to solve real-world problems, in real-time, with graphs. This is a whirlwind tour of the theory and application of graphs.
Total views
112,164
On Slideshare
0
From embeds
0
Number of embeds
1,949
Downloads
2,460
Shares
0
Comments
0
Likes
157
Join the community of over 1 million readers
Join the community of over 1 million readers
Sign up for a Scribd 30 day free trial to download this document plus get access to the world’s largest digital library.
Cancel anytime.The SlideShare family just got bigger. You now have unlimited* access to books, audiobooks, magazines, and more from Scribd.
Cancel anytime.