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Deep Modularity Networks with Diversity-Preserving Regularization

graph-neural-networks › graph-learning

📄 Abstract

Abstract: Graph clustering plays a crucial role in graph representation learning but often faces challenges in achieving feature-space diversity. While Deep Modularity Networks (DMoN) leverage modularity maximization and collapse regularization to ensure structural separation, they lack explicit mechanisms for feature-space separation, assignment dispersion, and assignment-confidence control. We address this limitation by proposing Deep Modularity Networks with Diversity-Preserving Regularization (DMoN-DPR), which introduces three novel regularization terms: distance-based for inter-cluster separation, variance-based for per-cluster assignment dispersion, and an assignment-entropy penalty with a small positive weight, encouraging more confident assignments gradually. Our method significantly enhances label-based clustering metrics on feature-rich benchmark datasets (paired two-tailed t-test, $p\leq0.05$), demonstrating the effectiveness of incorporating diversity-preserving regularizations in creating meaningful and interpretable clusters.

Authors (2)

Yasmin Salehi

Dennis Giannacopoulos

Submitted

January 23, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

Proposes Deep Modularity Networks with Diversity-Preserving Regularization (DMoN-DPR) to address limitations in feature-space diversity for graph clustering. It introduces three novel regularization terms: distance-based for inter-cluster separation, variance-based for intra-cluster dispersion, and an assignment-entropy penalty for confidence control, significantly enhancing clustering performance.

Business Value

Improves the ability to discover meaningful groups within complex network data, leading to better insights in social network analysis, customer segmentation, and biological network studies.

Paper Metadata

Innovation Type

Algorithmic Enhancement

Deployment Feasibility

Moderate to High, depending on the complexity of the graph and the computational resources available.

Limitations Addressed

Lack of explicit mechanisms for feature-space separation in DMoN,Lack of assignment dispersion control,Lack of assignment-confidence control

Performance Gains

Significantly enhances label-based clustering metrics on feature-rich benchmark datasets.

Technical Tags

Deep Modularity NetworksDMoNGraph ClusteringDiversity-Preserving RegularizationModularity MaximizationFeature-space DiversityAssignment DispersionAssignment ConfidenceDistance-based RegularizationVariance-based RegularizationAssignment Entropy Penalty

Research Topics

Graph ClusteringGraph Representation LearningCommunity DetectionDeep ClusteringRegularization TechniquesFeature Space Analysis

Methods & Architectures

Deep Modularity Networks with Diversity-Preserving Regularization (DMoN-DPR)Modularity maximizationCollapse regularizationDistance-based regularizationVariance-based regularizationAssignment-entropy penalty Deep Modularity Networks (DMoN)

Applications & Tasks

Social Networks Biological Networks Information Networks Recommendation Systems Achieving feature-space diversity in graph clusteringEnsuring structural separationControlling assignment dispersionImproving assignment confidence Graph clusteringCommunity detection

Datasets & Benchmarks

Datasets

feature-rich benchmark datasets

Benchmarks

paired two-tailed t-test, p<=0.05

label-based clustering metrics

Related Fields

Machine LearningGraph TheoryNetwork AnalysisClustering AlgorithmsCommunity Detection

Keywords

Graph ClusteringDeep Modularity NetworksDMoNCommunity DetectionRepresentation LearningRegularizationFeature DiversityAssignment DispersionGraph Neural NetworksNetwork AnalysisClustering MetricsModularity Maximization

Academic Context

#Graph Clustering#Graph Representation Learning#Community Detection#Deep Clustering#Regularization Techniques#Feature Space Analysis

Commercial Potential

Potential Products

Advanced graph clustering toolsCommunity detection libraries

Target Industries

Social MediaMarketingTelecommunicationsBiotechnologyCybersecurity

Use Case Examples

Identifying user communities on social platformsDetecting fraudulent groups in financial networksAnalyzing protein-protein interaction networks

Competitive Edge

Offers improved graph clustering by explicitly addressing feature-space diversity and assignment confidence, going beyond structural modularity maximization.

Market Opportunity

Growing demand for advanced network analysis and community detection tools.

Revenue Models

N/A (research)

Resource Requirements

Compute Needs

Moderate to High, depending on graph size and complexity.

Data Requirements

Requires graph data with associated features.

Deployment Constraints

Performance can be sensitive to hyperparameter tuning and the quality of graph features.

Scalability

Scalability depends on the underlying DMoN architecture and the efficiency of the regularization terms.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into existing platforms.

Patent Potential

Low to Moderate, for the novel regularization techniques.

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