Bridges
Glossary
 Directed

Directed trait. The algorithm is welldefined on a directed graph.
 Directed

Directed trait. The algorithm ignores the direction of the graph.
 Directed

Directed trait. The algorithm does not run on a directed graph.
 Undirected

Undirected trait. The algorithm is welldefined on an undirected graph.
 Undirected

Undirected trait. The algorithm ignores the undirectedness of the graph.
 Heterogeneous nodes

Heterogeneous nodes fully supported. The algorithm has the ability to distinguish between nodes of different types.
 Heterogeneous nodes

Heterogeneous nodes allowed. The algorithm treats all selected nodes similarly regardless of their label.
 Heterogeneous relationships

Heterogeneous relationships fully supported. The algorithm has the ability to distinguish between relationships of different types.
 Heterogeneous relationships

Heterogeneous relationships allowed. The algorithm treats all selected relationships similarly regardless of their type.
 Weighted relationships

Weighted trait. The algorithm supports a relationship property to be used as weight, specified via the relationshipWeightProperty configuration parameter.
 Weighted relationships

Weighted trait. The algorithm treats each relationship as equally important, discarding the value of any relationship weight.
Introduction
Given a graph, a bridge is a relationship whose removal increases the number of connected components in the graph. Equivalently, a relationship can only be a bridge if and only if it is not contained in any cycle. The Neo4j GDS Library provides an efficient linear time sequential algorithm to compute all bridges in a graph.
For more information on this algorithm, see:
Syntax
This section covers the syntax used to execute the Bridges algorithm in each of its execution modes. We are describing the named graph variant of the syntax. To learn more about general syntax variants, see Syntax overview.
CALL gds.bridges.stream(
graphName: String,
configuration: Map
)
YIELD
from: Integer,
to: Integer
Name  Type  Default  Optional  Description 

graphName 
String 

no 
The name of a graph stored in the catalog. 
configuration 
Map 

yes 
Configuration for algorithmspecifics and/or graph filtering. 
Name  Type  Default  Optional  Description 

List of String 

yes 
Filter the named graph using the given node labels. Nodes with any of the given labels will be included. 

List of String 

yes 
Filter the named graph using the given relationship types. Relationships with any of the given types will be included. 

Integer 

yes 
The algorithm is singlethreaded and changing the concurrency parameter has no effect on the runtime. 

String 

yes 
An ID that can be provided to more easily track the algorithm’s progress. 

Boolean 

yes 
If disabled the progress percentage will not be logged. 
Name  Type  Description 

from 
Integer 
Start node ID. 
to 
Integer 
End node ID. 
Examples
All the examples below should be run in an empty database. The examples use Cypher projections as the norm. Native projections will be deprecated in a future release. 
In this section we will show examples of running the Bridges algorithm on a concrete graph. The intention is to illustrate what the results look like and to provide a guide in how to make use of the algorithm in a real setting. We will do this on a small social network graph of a handful nodes connected in a particular pattern. The example graph looks like this:
CREATE
(nAlice:User {name: 'Alice'}),
(nBridget:User {name: 'Bridget'}),
(nCharles:User {name: 'Charles'}),
(nDoug:User {name: 'Doug'}),
(nMark:User {name: 'Mark'}),
(nMichael:User {name: 'Michael'}),
(nAlice)[:LINK]>(nBridget),
(nAlice)[:LINK]>(nCharles),
(nCharles)[:LINK]>(nBridget),
(nAlice)[:LINK]>(nDoug),
(nMark)[:LINK]>(nDoug),
(nMark)[:LINK]>(nMichael),
(nMichael)[:LINK]>(nDoug);
This graph has two clusters of Users, that are closely connected. Between those clusters there is one single edge.
MATCH (source:User)[r:LINK]>(target:User)
RETURN gds.graph.project(
'myGraph',
source,
target,
{},
{ undirectedRelationshipTypes: ['*'] }
)
Memory Estimation
First off, we will estimate the cost of running the algorithm using the estimate
procedure.
This can be done with any execution mode.
We will use the stream
mode in this example.
Estimating the algorithm is useful to understand the memory impact that running the algorithm on your graph will have.
When you later actually run the algorithm in one of the execution modes the system will perform an estimation.
If the estimation shows that there is a very high probability of the execution going over its memory limitations, the execution is prohibited.
To read more about this, see Automatic estimation and execution blocking.
For more details on estimate
in general, see Memory Estimation.
CALL gds.bridges.stream.estimate('myGraph', {})
YIELD nodeCount, relationshipCount, bytesMin, bytesMax, requiredMemory
nodeCount  relationshipCount  bytesMin  bytesMax  requiredMemory 

6 
14 
1040 
1040 
"1040 Bytes" 
Stream
In the stream
execution mode, the algorithm returns the bridge for each relationship.
This allows us to inspect the results directly or postprocess them in Cypher without any side effects.
For more details on the stream
mode in general, see Stream.
stream
mode:CALL gds.bridges.stream('myGraph')
YIELD from, to
RETURN gds.util.asNode(from).name AS fromName, gds.util.asNode(to).name AS toName
ORDER BY fromName ASC, toName ASC
fromName  toName 

"Alice" 
"Doug" 