Union-Find Leiden Refinement for Weakly Connected Communities
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Solution Overview
Problem
Existing community detection algorithms face inefficiencies and inaccuracies in handling weak connectivity issues during graph construction on large-scale online platforms, leading to dispersed communities and increased computational time.
Innovation Solution
Implementing an optimized Leiden-Refine algorithm combined with a Union-Find algorithm to traverse edges only once, split weakly connected communities, and merge them into cohesive groups, ensuring accurate and efficient community detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing community detection algorithms are used to process large-scale online platforms, then community detection can be performed, but computational time increases significantly and weak connectivity issues cause communities to become dispersed
Solution Approach 1:
The algorithm segments the graph processing into distinct phases: initial community identification, refinement iterations, and final output generation. By dividing the computation into manageable segments with targeted operations in each phase, the algorithm reduces overall computational time while maintaining detection accuracy
Solution Approach 2:
The algorithm performs preliminary actions by pre-identifying candidate communities and pre-processing graph structures before full detection. This includes initial node classification and edge weighting that prepares the data structure for faster subsequent processing, reducing the computational burden during main detection phases
2Stability of the object's composition
If existing community detection algorithms are used, then communities can be detected, but weak connectivity causes communities to be dispersed rather than cohesive
Solution Approach 1:
The algorithm applies dynamic adjustments during refinement iterations, adapting community assignments based on evolving connectivity patterns. Nodes are dynamically re-assigned to communities based on current edge weights and connectivity metrics, allowing weakly connected nodes to be properly integrated into cohesive communities rather than remaining dispersed
Solution Approach 2:
The algorithm incorporates feedback mechanisms where community assignments are continuously evaluated and refined based on connectivity metrics. Weak connectivity issues are detected through feedback loops that measure intra-community edge densities, and adjustments are made in subsequent iterations to improve community cohesion and eliminate dispersion
Data Source
AI summary
A computer-implemented method includes: receiving data encoding an input graph comprising a group of nodes and a group of edges each connecting two nodes from the group of nodes, wherein each node represents a user on a platform with a group of users clustered into an initial set of communities; partitioning the input graph into a set of subgraphs each comprising more than one partitioned community of users; processing each subgraph using a Union-Find algorithm to generate a processed subgraph showing a revised community with a intra-community connectivity metric higher than the more than one partitioned community of users; processing each processed subgraph to split the revised community into a set of fine-grained communities; and merging the set of fine-grained communities to construct an output graph showing the plurality of users clustered into a new set of communities.