Network Node Sorting via Characteristic and Sparse Matrix Multiplication
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Solution Overview
Problem
Conventional PageRank algorithms are limited in implementing parallel computing and are cumbersome to maintain, making them unsuitable for large datasets and inefficient in sorting network nodes effectively.
Innovation Solution
A method and device for processing network nodes involving the construction of a characteristic matrix and a sparse matrix based on association information, followed by iterative multiplication to obtain a second characteristic matrix, which is used to sort network nodes efficiently, utilizing general-purpose structured query languages and distributed computing platforms like Hive and Hadoop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional PageRank algorithms are implemented using C/C++ or Java, then the algorithms can process small and medium-sized datasets, but they cannot implement parallel computing and are limited in scalability
Solution Approach 1:
The patent segments the network node processing into characteristic matrix construction and sparse matrix multiplication stages, enabling parallel computation across different data partitions while maintaining algorithmic integrity for datasets of varying sizes
Solution Approach 2:
The patent creates a universal processing framework that can handle small, medium, large, and ultra-large datasets through the same characteristic matrix and sparse matrix methodology, adapting to different dataset scales without requiring algorithm changes
2Ease of manufacture
If conventional PageRank algorithms are packaged in business software, then they can be deployed practically, but they are difficult to maintain and inconvenient to update
Solution Approach 1:
The patent introduces characteristic matrices and sparse matrices as intermediary representations that decouple the core algorithm logic from implementation details, making the system easier to maintain and update by allowing independent modification of matrix construction and multiplication stages
3Productivity
If conventional algorithms are used for large and ultra-large datasets, then processing capacity is insufficient, but increasing computational resources increases complexity
Solution Approach 1:
The patent changes the computational parameters from traditional iterative PageRank to characteristic matrix multiplication with sparse matrix operations, which improves processing capacity for large datasets while controlling complexity through efficient matrix representation and computation methods
Data Source
AI summary
Devices and methods are provided for processing network nodes. For example, a sorting request is detected; one or more first network nodes to be sorted are acquired corresponding to the sorting request; a first characteristic matrix is constructed based on at least information associated with the first network nodes; association information between the first network nodes is acquired; a sparse matrix is constructed based on at least information associated with the association information between the first network nodes; iterative multiplication is performed on the sparse matrix and the first characteristic matrix to obtain a second characteristic matrix upon convergence; and the first network nodes are sorted based on at least information associated with the second characteristic matrix.