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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
Label Propagation
Overview
The Label Propagation algorithm is a graph-based semi-supervised learning method. Its basic principle is to predict the label information about unlabeled nodes using that of the labeled nodes. This algorithm can create graphs based on the relationships between samples. Nodes include labeled data and unlabeled data, and the edge indicates the similarity between two nodes. Node labels are transferred to other nodes based on the similarity. Labeled data is like a source used to label unlabeled data. The greater the node similarity is, the easier the label propagation will be.
Application Scenarios
This algorithm applies to scenarios such as information propagation, advertisement recommendation, and community discovery.
Parameter Description
Parameter |
Mandatory |
Description |
Type |
Value Range |
Default Value |
---|---|---|---|---|---|
convergence |
No |
Convergence |
Double |
A real number between 0 and 1 (excluding 0 and 1) |
0.00001 |
max_iterations |
No |
Maximum iterations |
Int |
1-2,000 |
1,000 |
initial |
No |
Name of the property used as the initialization label on a vertex |
String |
Null or character string
If the value of initial is not null, the number of vertices with initialization labels must be greater than 0 and less than the total number of vertices. |
- |
Precautions
Label Propagation uses IDs as labels by default.
Example
Set parameters coverage to 0.00001 and max_iterations to 1,000, the sub-graphs with different labels are displayed on the canvas. The color of a node varies with labels. The JSON result is displayed in the query result area.
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