Network Community Detection with Adjustable Resolution
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Solution Overview
Problem
Existing methods for community decomposition in network-type data struggle to accurately classify nodes into communities with varying resolutions, leading to inconsistencies in community formation and importance degree calculations.
Innovation Solution
An information processing apparatus that acquires network information, calculates classification ratios, and generates first and second type communities using sequential computations with adjustable resolutions, allowing for precise community classification and importance degree determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If community decomposition is performed on network-type data using existing methods, then communities can be identified, but the classification accuracy and consistency across different resolutions deteriorate
Solution Approach 1:
The patent applies dynamics by making the resolution parameter adjustable and variable during the community decomposition process. The system allows users to specify different resolution values to control the granularity of community detection, and dynamically updates the classification ratios as resolutions change. This enables the same decomposition method to adapt to different resolution requirements while maintaining classification accuracy.
Solution Approach 2:
The patent utilizes parameter changes by introducing a resolution parameter that controls the classification process. By changing the resolution parameter, the system can adjust the level of detail in community detection. The classification ratios are recalculated based on the specified resolution, allowing the method to maintain accuracy across different scales of analysis.
2Ease of manufacture
If fixed resolution classification is used, then computation is simpler, but the ability to handle varying precision requirements deteriorates
Solution Approach 1:
The system implements dynamics by allowing the resolution parameter to be changed during the process. Instead of using a fixed resolution, the classification ratio calculation unit recalculates classifications based on the specified resolution, enabling the system to adapt to different precision requirements while maintaining computational efficiency through the iterative update mechanism.
Solution Approach 2:
The patent applies segmentation by dividing the community decomposition process into distinct stages: initial classification ratio calculation, resolution-based filtering, and iterative refinement. This segmentation allows the system to handle varying precision requirements by adjusting which stages are executed and at what resolution, balancing computational simplicity with adaptability.
3Measurement precision
If multiple community classifications are generated with different resolutions, then precision and adaptability improve, but device complexity increases
Solution Approach 1:
The patent applies merging by integrating multiple classification results at different resolutions into a unified community decomposition outcome. The system combines the strengths of coarse-grained and fine-grained classifications, using the classification ratios from different resolutions to produce a comprehensive and accurate community structure that leverages multiple perspectives without requiring separate independent analyses.
Solution Approach 2:
The system uses preliminary action by performing an initial classification at a coarse resolution to establish broad community structures, then using these preliminary results as a foundation for finer-grained classifications. This preliminary classification reduces the complexity of subsequent detailed analyses by providing a simplified framework that guides the more complex fine-grained decomposition.
4Productivity
If existing community decomposition methods are used, then processing speed is maintained, but classification consistency across resolutions deteriorates
Solution Approach 1:
The patent applies continuity of useful action by implementing an iterative process where classification ratios are continuously updated and refined based on the specified resolution. Rather than performing separate independent decompositions at different resolutions, the system maintains a continuous refinement process that ensures consistency across resolutions by building upon previous classification results and adjusting them systematically.
Solution Approach 2:
The system uses feedback by comparing classification results at different resolutions and using this information to adjust and refine the classification ratios. The classification ratio calculation unit incorporates feedback from the resolution parameter and the resulting community structures to maintain consistency, ensuring that classifications at different resolutions are coherent and stable rather than contradictory.
Data Source
AI summary
An information processing apparatus includes: a network information acquisition unit that acquires network information which includes target nodes and adjacent nodes; a classification ratio calculation unit that calculates a classification ratio, in which the target nodes are respectively classified as a plurality of communities corresponding to a predetermined number in the network information, so as to have correlation according to given resolutions with a classification ratio in which the adjacent nodes are respectively classified as the plurality of communities; a first type community generation unit that generates one or more first type communities; a classification ratio updating unit that updates the classification ratio relevant to the target nodes so as to have correlation with the classification ratio in which the adjacent nodes are respectively classified as the plurality of communities; and a second type community generation unit that generates one or more second type communities.