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- What's New
- Service Overview
- Getting Started
-
User Guide
- What Is GES?
- Product Advantages
- Applicable Scenarios
- Basic Concepts
- Constraints and Limitations
- Related Services
- GES Overview
- Permissions Management
- Importing Metadata
- Creating Graphs
- Managing Graphs
-
Accessing and Analyzing Graph Data
- Graph Editor
- Accessing the GES Graph Editor
- Dynamic Graphs
- Graph Exploration
- Multi-Graph Management (Database Edition)
- Adding Custom Operations
- Editing Schema
- Visual Query
- Gremlin Query
- Cypher Query
- DSL Query
- Analyzing Graphs Using Algorithms
- Analyzing Graphs on the Canvas
- Graph Display in 3D View
- Filter Criteria
- Editing Properties
- Statistics Display
- View Running Records
- Viewing Query Results
- Viewing Graph Tasks
- Configuring Permissions
-
Algorithms
- Algorithm List
- PageRank
- PersonalRank
- K-core
- K-hop
- Shortest Path
- All Shortest Paths
- Filtered Shortest Path
- SSSP
- Shortest Path of Vertex Sets
- n-Paths
- Closeness Centrality
- Label Propagation
- Louvain
- Link Prediction
- Node2vec
- Real-time Recommendation
- Common Neighbors
- Connected Component
- Degree Correlation
- Triangle Count
- Clustering Coefficient
- Betweenness Centrality
- Edge Betweenness Centrality
- Origin-Destination Betweenness Centrality
- Circle Detection with a Single Vertex
- Common Neighbors of Vertex Sets
- All Shortest Paths of Vertex Sets
- Filtered Circle Detection
- Subgraph Matching
- Filtered All Pairs Shortest Paths
- Filtered All Shortest Paths
- TopicRank
- Filtered n-Paths
- Temporal Paths
- Best Practices
- FAQs
-
API Reference
- Before You Start
- Calling APIs
- Management Plane APIs (V2)
-
Service Plane APIs
-
Memory Edition
-
Vertex Operation APIs
- Querying Vertices That Meet Filter Criteria
- Querying Vertex Details
- Adding a Vertex
- Deleting a Vertex
- Updating Vertex Properties
- Querying Vertex Data in Batches
- Adding Vertices in Batches
- Deleting Vertices in Batches
- Updating Vertex Properties in Batches
- Adding a Vertex Label
- Deleting a Vertex Label
- Exporting Filtered Vertices
- Deleting Filtered Vertices
- Edge Operation APIs
- Metadata Operation APIs
- Index Operation APIs
- Gremlin Operation APIs
-
Algorithm APIs
- Running Algorithms
-
Algorithm API Parameter References
- Common Algorithm Parameters
- PageRank
- PersonalRank
- K-core
- K-hop
- Common Neighbors
- Common Neighbors of Vertex Sets
- Link Prediction
- Shortest Path
- All Shortest Paths
- Filtered Shortest Path
- SSSP
- Shortest Path of Vertex Sets
- n-Paths
- Filtered n-Paths
- Filtered All Pairs Shortest Paths
- All Shortest Paths of Vertex Sets
- Filtered All Shortest Paths
- Connected Component
- Label Propagation
- Louvain
- Node2vec
- Real-time Recommendation
- Degree Correlation
- Triangle Count
- Cluster Coefficient
- Closeness Centrality
- Filtered Circle Detection
- Subgraph Matching
- Topicrank
- Temporal Graph APIs
- Path APIs
- Graph Statistics APIs
- Graph Operation APIs
- Subgraph Operation APIs
- Job Management APIs
- Custom Operations APIs
- Cypher Queries
- Filtered Query
- Filtered Query V2
- Updating Specified Properties of Vertices and Edges by Importing a File
- Deleting Vertices and Edges by Files
- Granular Permission Control APIs
-
Vertex Operation APIs
- Database Edition
-
Memory Edition
- GES Metrics
- Appendix
-
devg
- Overview
- Preparations
- Importing a Project
-
Using the Service Plane SDK
- Initializing the Client of the GES Service Plane
- Querying Vertices That Meet Filtering Conditions
- Querying Edges That Meet Filtering Conditions
- Querying Vertex Details
- Querying Edge Details
- Querying Graph Metadata Details
- Querying General Information About a Graph
- Executing Gremlin Queries
-
Running Algorithms
- PageRank
- PersonalRank
- K-hop
- K-core
- Shortest Path
- All Shortest Paths
- Filtered Shortest Path
- SSSP
- Shortest Path of Vertex Sets
- n Paths
- Closeness
- Label Propagation
- Louvain Algorithm
- Link Prediction
- Node2vec
- Real-time Recommendation
- Common Neighbors
- Connected Component
- Degree Correlation Algorithm
- Triangle Count
- Cluster Coefficient
- Filtered Circle Detection
- Subgraph Matching
- Filtered All Pairs Shortest Paths
- Filtered All Shortest Paths
- TopicRank
- Filtered n Paths
- Common Neighbors of Vertex Sets
- All Shortest Paths of Vertex Sets
- Querying Job Status
- Canceling a Job
- Adding a Vertex
- Deleting a Vertex
- Adding an Edge
- Deleting an Edge
- Adding an Index
- Deleting an Index
- Querying Indexes
- Exporting a Graph
- Removing a Graph
- Adding a Property
- Querying Path Details
- Incrementally Importing Data to Graphs
- Adding Vertices in Batches
- Deleting Vertices in Batches
- Adding Edges in Batches
- Deleting Edges in Batches
- Updating Vertex Properties in Batches
- Updating Edge Properties in Batches
-
Using the Management Plane SDK
- Initializing the Client of the GES Management Plane
- Querying Quotas
- Verifying Metadata Files
- Querying the Graph List
- Querying Graph Details
- Creating a Graph
- Closing a Graph
- Starting a Graph
- Deleting a Graph
- Querying Backups of All Graphs
- Querying the Backup List of a Graph
- Adding a New Backup
- Deleting a Backup
- Querying Job Status
- Using Cypher JDBC Driver to Access GES
- Relationships Between SDKs and REST APIs
-
FAQs
- Customer Consultation
-
API Use
- How Do I Import Data to GES?
- When Binding an EIP to an Existing Instance or an Instance Being Created, What Should I Do to Deal with Insufficient Permissions for Creating Agencies?
- When Binding an EIP to an Existing Instance or an Instance Being Created, What Should I Do to Deal with Insufficient Quotas for Creating Agencies?
- Can I Run Several Gremlin/Cypher Commands At a Time?
- If a Vertex Is Deleted, What Will Happen to Edges Formed Based on the Vertex?
- What Do I Do If an Error Message Indicating a Vertex Does Not Exist Is Displayed When Properties of the Vertex Whose ID Contains Chinese Characters Are Modified?
- GUI
- Others
Edge Betweenness Centrality
Overview
The Edge Betweenness Centrality algorithm calculates shortest paths that pass through an edge.
Application Scenarios
The Edge Betweenness Centrality algorithm can be used for key relationship mining. It is applicable to social networking, financial risk control, transportation networking, and city planning.
Parameter Description
|
Parameter |
Mandatory |
Description |
Type |
Value Range |
Default Value |
|---|---|---|---|---|---|
|
directed |
No |
Whether an edge is directed |
Boolean |
The value can be true or false. |
true |
|
weight |
No |
Weight of an edge |
String |
The value can be an empty string. If this parameter is left blank, the weight and distance of this edge are 1 by default. You can set this parameter to a property of the edge, and the property value will be the weight. If the edge does not have the specified property, the weight is 1 by default.  NOTE:
The weight of an edge must be greater than 0. |
- |
|
seeds |
No |
Vertex ID |
String |
If the graph is large, betweenness calculation can be slow. You can set seeds to the sampling nodes for approximate calculation. The more seeds nodes, the closer results to the accurate calculation. The number of vertices cannot be greater than 100,000. |
- |
|
k |
No |
Number of samples |
Integer |
If the graph is large, betweenness calculation can be slow. You can set k to randomly select k sampling vertices from the graph. The larger value, the closer results to the accurate calculation. The value cannot be greater than 100,000. |
- |
NOTE:
When you perform approximate edge-betweenness calculation, either seeds or k must be specified. If both are specified, seeds vertices will be sampled by default and k will be ignored.
Precautions
None
Example
Set weight="length", directed=true, seeds ="Lee,Alice" and view the result.
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