Find Shortest Path Between People in Social Neighborhood

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This example shows how to search a social neighborhood to find the shortest path between people, using the MATLAB® interface to Neo4j®. Assume that you have graph data that is stored on a Neo4j database which represents a social neighborhood. This database has seven nodes and eight relationships. Each node has only one unique property key name with a value ranging from User1 through User7. Each relationship has type knows.

To find the shortest path between User1 and User7, use the MATLAB interface to Neo4j and the digraph object. For details about the MATLAB interface to Neo4j, see Graph Database Workflow for Neo4j Database Interfaces.

The local machine hosts the Neo4j database with port number 7474, user name neo4j, and password matlab. For a visual representation of the data in the database, see this figure.

Connect to Neo4j Database

Create a Neo4j connection object neo4jconn using the URL http://localhost:7474/db/data, user name neo4j, and password matlab.

url = 'http://localhost:7474/db/data';
username = 'neo4j';
password = 'matlab';

neo4jconn = neo4j(url,username,password);

Check the Message property of the Neo4j connection object neo4jconn. The blank Message property indicates a successful connection.

neo4jconn.Message

ans =

     []

Search Entire Graph

Find all the Person nodes and all the relationships associated with each Person node using searchGraph.

social_graphdata = searchGraph(neo4jconn,{'Person'})

social_graphdata = struct with fields:
        Nodes: [7×3 table]
    Relations: [8×5 table]

Convert Graph Data to Directed Graph

Using the table social_graphdata.Nodes, access the name property for each node that appears in the NodeData variable of the table.

Assign the table social_graphdata.Nodes to nodestable.

nodestable = social_graphdata.Nodes

nodestable=7×3 table
         NodeLabels      NodeData                  NodeObject             
         __________    ____________    ___________________________________

    0     'Person'     [1×1 struct]    [1x1 database.neo4j.http.Neo4jNode]
    1     'Person'     [1×1 struct]    [1x1 database.neo4j.http.Neo4jNode]
    2     'Person'     [1×1 struct]    [1x1 database.neo4j.http.Neo4jNode]
    3     'Person'     [1×1 struct]    [1x1 database.neo4j.http.Neo4jNode]
    4     'Person'     [1×1 struct]    [1x1 database.neo4j.http.Neo4jNode]
    5     'Person'     [1×1 struct]    [1x1 database.neo4j.http.Neo4jNode]
    9     'Person'     [1×1 struct]    [1x1 database.neo4j.http.Neo4jNode]

Assign the row names for each row in the table nodestable to rownames.

rownames = nodestable.Properties.RowNames

rownames = 7×1 cell array
    {'0'}
    {'1'}
    {'2'}
    {'3'}
    {'4'}
    {'5'}
    {'9'}

Access the NodeData variable from nodestable for each row. nodedata contains an array of structures.

nodedata = [nodestable.NodeData{rownames}]

nodedata = 1×7 struct array with fields:
    name

To retrieve the name field from each structure, index into the array. nodenames is a cell array of character vectors that contains node names.

nodenames = {nodedata(:).name}

nodenames = 1×7 cell array
    {'User1'}    {'User3'}    {'User2'}    {'User4'}    {'User5'}    {'User6'}    {'User7'}

Create the digraph object social_graph using the neo4jStruct2Digraph function with the graph data stored in social_graphdata and the node names stored in nodenames.

social_graph = neo4jStruct2Digraph(social_graphdata,'NodeNames',nodenames)

social_graph = 
  digraph with properties:

    Edges: [8×3 table]
    Nodes: [7×3 table]

To see a visual representation of the graph, create a figure that displays social_graph.

plot(social_graph,'EdgeLabel',social_graph.Edges.RelationType)