Graph Partitioning for Network Spillover Minimization
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Solution Overview
Problem
Existing software feature experimentation methods are hindered by spillover effects, where changes in user behavior in one group inadvertently affect another group due to network connections, leading to inaccurate and unreliable results.
Innovation Solution
A framework using graph partitioning and multi-level coarsening techniques to segregate users into treatment and holdout groups, minimizing spillover effects by analyzing user relationships and partitioning large graphs efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users are randomly assigned to treatment and holdout groups, then the experimentation process is simple and quick, but spillover effects occur due to network connections between groups, leading to inaccurate results
Solution Approach 1:
The patent applies graph partitioning to divide the user network into distinct treatment and holdout groups. By segmenting the graph into communities with minimal cross-group edges, the system creates isolated groups that prevent spillover effects while maintaining experimental validity. This segmentation resolves the contradiction by enabling accurate measurement without requiring complex setup procedures.
Solution Approach 2:
The patent introduces a graph partitioning algorithm as an intermediary mechanism between the treatment assignment and the network structure. This intermediary processes the user network to identify optimal groupings that minimize spillover, thereby enabling both simple random assignment procedures and accurate result measurement simultaneously.
2Measurement precision
If users are carefully segregated into treatment and holdout groups to minimize spillover effects, then experimentation result accuracy improves, but the complexity of group assignment increases
Solution Approach 1:
The patent replaces manual or complex mechanical group assignment processes with an automated graph partitioning algorithm. This algorithm mathematically optimizes group segregation based on network connections, eliminating the need for complex manual procedures while achieving high measurement precision through systematic, algorithm-driven assignment.
Solution Approach 2:
The patent transforms the group assignment problem into a graph theory optimization problem by changing the parameters of group formation from arbitrary assignments to community-based partitions. This parameter transformation enables automated, efficient computation of optimal groupings that minimize spillover effects without increasing operational complexity.
3Measurement precision
If the user network is fully analyzed to create optimal groups, then spillover effects are minimized, but computational resources and processing time increase
Solution Approach 1:
The patent applies graph partitioning algorithms that process the user network to a sufficient degree to achieve meaningful spillover minimization without requiring complete analysis of all possible connections. By using heuristics and iterative optimization, the system achieves adequate group segregation with partial processing, reducing computational resource consumption while maintaining measurement precision.
Data Source
AI summary
Methods and systems are presented for facilitating computer software feature experimentation by using one or more graph analyses to partition users into different experimentation groups. A graph is generated to represent users of a service provider and relationships among the users. When a request to perform a software feature experiment on the users of the service provider, the graph is analyzed to determine an estimated spillover effect. The graph is then coarsened across multiple levels using a label propagation technique based on one or more coarsening parameters. After the graph has been coarsened, the graph is partitioned into multiple partitions based on one or more partitioning parameters. The coarsening parameters and/or the partitioning parameters may be determined based on the estimated spillover effect. A subset of users is selected for performing the software feature experiment based on the partitioning of the coarsened graph.