Global vs. Local Graph Ranking | Marko A. Rodriguez
Graph ranking algorithms are all about mapping a complex graphical structure to a numeric vector. For a given algorithm, a single numeric value in the resultant vector corresponds to the score of a particular vertex in the graph. In code, the previous structures are defined below using Groovy and Blueprints. def g = new Neo4jGraph('/tmp/neo4j'); // the graph def m = new HashMap<Vertex,Double>(); /
Exploring Wikipedia with Gremlin Graph Traversals | Marko A. Rodriguez
A graph is a data structure composed of vertices and edges. These terms are synonymous with the following: vertices, dots, nodes, things, points edges, lines, links, relations, arcs From vertices and edges, numerous artificial and real-world systems can be modeled and processed. The purpose of this post is to explore a graph representation of Wikipedia persisted in the graph database Neo4j and pro
A Graph-Based Movie Recommender Engine | Marko A. Rodriguez
A recommender engine helps a user find novel and interesting items within a pool of resources. There are numerous types of recommendation algorithms and a graph can serve as a general-purpose substrate for evaluating such algorithms. This post will demonstrate how to build a graph-based movie recommender engine using the publicly available MovieLens dataset, the graph database Neo4j, and the graph
MySQL vs. Neo4j on a Large-Scale Graph Traversal
Loading the Graph The graph data set was loaded both into MySQL and Neo4j. In MySQL a single table was used with the following schema. CREATE TABLE graph ( outV INT NOT NULL, inV INT NOT NULL ); CREATE INDEX outV_index USING BTREE ON graph (outV); CREATE INDEX inV_index USING BTREE ON graph (inV); After loading the data, the table appears as below. The first line reads: “vertex 0 is connec
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Graph Pattern Matching with Gremlin 1.1 | Marko A. Rodriguez
On Graph Computing | Marko A. Rodriguez
Recommendation System « Marko A. Rodriguez
