Cluster-Based Random Walking for Graph Data Processing
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Solution Overview
Problem
Existing random walking algorithms are inefficient for large-scale graph data processing, requiring frequent database access and lacking in scalability.
Innovation Solution
A cluster-based random walking method that generates index and element vectors to represent node degrees and adjacent nodes, allowing for independent database access and efficient random sequence generation, thereby reducing database access and enhancing processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional random walking algorithm is used to process large-scale graph data, then the algorithm can be implemented, but the processing efficiency is low and database access frequency is high
Solution Approach 1:
The patent pre-calculates and stores the degree of each node and its adjacent nodes in the database before random walking operations. This preliminary action eliminates the need for repeated database queries during random walking, significantly reducing database access frequency and improving processing efficiency
Solution Approach 2:
The patent creates a local copy of the graph data structure (including node degrees and adjacent node information) in memory. This copying approach allows random walking algorithms to operate on the in-memory data structure rather than repeatedly accessing the database, thereby reducing database access time and enhancing overall processing efficiency
2Adaptability or versatility
If traditional random walking algorithm is used, then the implementation is simple, but it lacks scalability for large-scale graph data
Solution Approach 1:
The patent segments the graph data into node degree information and adjacent node information, storing them separately in the database. This segmentation allows the system to handle large-scale graph data more efficiently by enabling parallel processing and reducing the complexity of random walking operations on massive datasets
Data Source
AI summary
Embodiments of the present specification disclose random walking and a cluster-based random walking method, apparatus and device. A solution includes: obtaining information about each node included in graph data, generating, according to the information about each node, an index vector reflecting a degree value of a respective node, then generating an element vector reflecting an identifier of an adjacent node of the node, and generating a random sequence according to the index vector and the element vector, to implement random walks in the graph data. The solution is applicable to clusters and individual machines.