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Graph Engine Service Overview

Graph Engine Service (GES) facilitates querying and analysis of graph-structure data based on various relationships. It is specifically suited for scenarios requiring analysis of rich relationship data, including social relationship analysis, marketing recommendation, public opinions and social listening, information communication, and anti-fraud.


  • Large Scale

    GES has the efficient data organization capability, facilitating analysis and querying of graphs with tens of billions of vertices and hundreds of billions of edges.

  • High Performance

    GES has optimized the distributed graph processing engine to support high-concurrency, multi-hop, real-time queries in seconds.

  • Integrated Query and Analysis

    GES provides extensive graph analytics algorithms to implement integrated query and analysis, facilitating analysis for scenarios such as relationship analysis, route planning, and marketing recommendation.

  • Ease of Use

    GES provides wizard-based GUI and compatible with Gremlin, facilitating easy graph analysis.

Application Scenarios

  • Internet
    Quickly and effectively mines valuable information from large and complex social networks.
    Figure 1 Internet


    • Friend, Commodity, and Information Recommendation

      Provides personalized and precise recommendations of friends, commodities, and information based on friend relationships, user profiles, behavior similarities, commodity similarities, and propagation paths.

    • User Grouping

      Groups users based on user profiles, behavior similarities, or friend relationships to facilitate precise user group management.

    • Abnormal Behavior Analysis

      Analyzes user behavior, partner relationships, and account/IP login information to detect abnormal behavior, minimizing losses due to fraud.

    • Public Opinion and Social Listening

      Identifies opinion leaders and hot topics by analyzing propagation paths and friend relationships, and enhances the quality of public opinion analysis.

  • Knowledge Graph
    GES-based knowledge graphs integrate various kinds of heterogeneous data, supporting larger graph scales and delivering higher performance.
    Figure 2 Knowledge graph


    • Massive Storage

      Integrates and stores heterogeneous data as vertices and edges in billion-scale graphs.

    • Quick Correlation Query

      Optimized to process thousands of graph queries, returning accurate results within seconds.

    • Knowledge Classification

      Combines similar knowledge points based on graph-based analysis and computing to implement knowledge disambiguation.

    • Learning Path Identification and Recommendation

      Identifies and recommends learning paths based on learning relations of knowledge points.

  • Financial Risk Control
    GES graph queries help detect fraudulent user behavior, minimizing potential financial risks.
    Figure 3 Financial risk control


    • Real-time Fraud Detection

      Identifies users who share the same personal information such as email addresses or IP addresses and highlights cases of known or potential fraud.

    • Group Detection

      Groups users based on interpersonal relationship analysis to identify abnormal groups.

    • Missing Person Tracking

      Tracks the missing person based on various relationships.

  • Urban Industry
    Assists customers in adjusting the pressure and balancing loads of urban roads or pipelines (such as water, gas, power, and oil pipelines) to facilitate refined control over traffic networks and pipelines.
    Figure 4 Urban industry


    • Pipeline Pressure Adjustment

      Assists in analyzing the traffic pressure of the entire pipeline network based on the real-time pipeline monitoring data.

    • Urban Road Network Control

      Conducts traffic congestion analysis for the entire urban road network according to the traffic, road network, and intersection monitoring information to assist in signal light control.

    • Route Planning

      Helps plan routes based on person and vehicle requests in real time, improving the seat occupancy rate and reducing operation costs.

  • Enterprise IT
    Provides intelligent device monitoring and management for your entire network and IT infrastructure.
    Figure 5 Enterprise IT


    • Network Planning

      Facilitates network planning by determining network impact due to faulty nodes and recommending backup routers for nodes bearing heavy loads.

    • Fault Cause Analysis

      Quickly locates the root cause of any network or infrastructure fault.

    • IT Infrastructure Management

      Provides visual relationships of network devices, including device and resource statuses, for more efficient O&M.


  • Extensive Algorithms

    Supports the PageRank, K-core, Shortest Path, Label Propagation, Triangle Count, and Link Prediction algorithms.

  • Visualized Graph Analysis

    Provides wizard-based exploration environment and visualized query results.

  • Query/Analysis APIs

    Provides APIs for graph query, metrics statistics, Gremlin query, graph algorithms, and graph and backup management.

  • Compatibility

    Compatible with open source Apache TinkerPop Gremlin 3.3.0.

  • Graph Management

    Provides the GES overview, graph management, graph backup, and metadata management functions.