GNN Neighbor Sampler With Uniform Subrange Random Sampling
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Solution Overview
Problem
Existing GNN model samplers face challenges in efficiently and evenly distributing random samples of neighbor nodes due to disordered random number generation, leading to excessive computation complexity and storage overheads.
Innovation Solution
A sampler that divides the range of neighbor nodes into subranges and uses random numbers to uniformly select nodes within these subranges, employing an input buffer and output buffer to ensure even distribution and reduce storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If random sampling is performed using disordered random numbers, then sampling can be implemented, but the sampled nodes are not evenly distributed and storage overhead increases
Solution Approach 1:
The patent divides the range of neighbor node indices into multiple subranges, and for each subrange, generates a random number to select a node. This segmentation approach ensures that sampled nodes are evenly distributed across the entire neighbor node set while only requiring storage of one index value per subrange, significantly reducing storage overhead compared to storing all neighbor node indices.
2Measurement precision
If all neighbor node information is stored for comparison, then accurate sampling can be achieved, but computation complexity and storage overhead become excessively high
Solution Approach 1:
The patent extracts only the necessary information for sampling by dividing the neighbor node index range into subranges and selecting one index per subrange. Instead of storing and comparing against all N neighbor node indices, the system only needs to store one index value per subrange (K values total), reducing both storage requirements and comparison operations from O(N) to O(K), where K is much smaller than N.
3Productivity
If neighbor nodes are sampled to reduce complexity, then computation overhead decreases, but sampling distribution may become unbalanced
Solution Approach 1:
The patent applies different sampling strategies to different subranges of neighbor nodes. By dividing the index range into subranges and ensuring one sample is selected from each subrange, the method guarantees uniform distribution across the entire neighbor node set. This local quality control within each subrange ensures global sampling uniformity while maintaining computational efficiency through reduced sample size.
Data Source
AI summary
A sampler for executing a graph neural network (GNN) model are disclosed. The sampler is configured to implement random sampling for neighbor nodes around a specified node of a GNN model, and performs: obtaining a quantity of neighbor nodes around the specified node and a target number of neighbor nodes to be sampled; dividing a range into a plurality of subranges based on the target number; generating random numbers; determining a plurality of integer values within the plurality of subranges based on the random numbers; determining index values of the target number of neighbor nodes to be sampled by matching index values of the neighbor nodes and the plurality of determined integer values; and writing the determined index values into an output buffer. The sampler provided in the present disclosure can uniformly sample the neighbor nodes around the specified node for the specified node.