Community Search in Time-dependent Road-social Attributed Networks
Li Ni, Hengkai Xu, Lin Mu, Yiwen Zhang, Wenjian Luo·May 18, 2025
Summary
A study introduces SSAC and GSSAC for community search in time-dependent networks, addressing limitations of existing methods. SSAC identifies a k-core with high semantic and spatial cohesiveness, using an exact and a greedy algorithm. GSSAC outperforms baselines in structural, semantic, and time-dependent spatial cohesiveness, making it effective for optimizing team composition, identifying suspicious account communities, and detecting specialized, geographically close, and research-similar communities. The paper also introduces ESSAC, a local method for identifying k-cores in road-social networks, focusing on semantic and spatial awareness. GSSAC integrates keyword and location information, optimizing for semantic and time-dependent spatial cohesiveness, and outperforms baselines. The work discusses research on community detection, graph clustering, and dynamic energy management, addressing large attributed graphs and multi-valued networks with applications in social network analysis, knowledge graphs, and microgrid management.
Background
Community Search in Time-Dependent Networks
Challenges with Existing Methods
Limitations in capturing dynamic, semantic, and spatial aspects
Objective
Contribution of SSAC and GSSAC
Addressing limitations in community search for time-dependent networks
SSAC: Semantic and Spatially Aware Community Detection
Method
Algorithm Design
Exact Algorithm
Identifying a k-core with high semantic and spatial cohesiveness
Greedy Algorithm
Efficient approximation for large networks
Evaluation
Comparison with baseline methods
GSSAC: Generalized Semantic and Spatially Aware Community Detection
Method
Integration of Keyword and Location Information
Enhancing semantic and time-dependent spatial cohesiveness
Optimization for Community Detection
Outperforming baselines in structural, semantic, and time-dependent spatial cohesiveness
Applications
Team Composition Optimization
Suspicious Account Community Identification
Specialized, Geographically Close, and Research-Similar Community Detection
ESSAC: Efficient Semantic and Spatial Community Detection
Method
Local Approach
Identifying k-cores in road-social networks
Focus on Semantic and Spatial Awareness
Enhancing community detection in complex networks
GSSAC: Advanced Community Detection
Method
Keyword and Location Information Integration
Optimizing for semantic and time-dependent spatial cohesiveness
Performance Evaluation
Comparison with baseline methods in various aspects
Research Contributions
Community Detection
Graph Clustering
Large Attributed Graphs
Addressing scalability and complexity
Multi-Valued Networks
Handling diverse data types and relationships
Applications
Social Network Analysis
Knowledge Graphs
Enhancing understanding and utilization of information
Microgrid Management
Optimizing energy distribution and management
Conclusion
Summary of Contributions
SSAC and GSSAC
Advancements in community search for time-dependent networks
ESSAC
Local method for efficient community detection
Future Directions
Potential for further research and applications
Basic info
papers
social and information networks
artificial intelligence
Advanced features
Insights
What are the main contributions of the study introducing SSAC and GSSAC for community search in time-dependent networks?
What are the potential limitations of the ESSAC method when applied to road-social networks?
In what ways does GSSAC outperform baseline methods in terms of structural, semantic, and time-dependent spatial cohesiveness?
How do the SSAC and GSSAC algorithms address the limitations of existing methods in community search?