Kernels on Attributed Networks for Community Detection
- Resource Type
- Conference
- Authors
- Pizzuti, Clara; Socievole, Annalisa
- Source
- 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) Information, Intelligence, Systems & Applications (IISA), 2022 13th International Conference on. :1-8 Jul, 2022
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Computational modeling
Organizations
Complex networks
Kernel
Genetic algorithms
Attributed networks
Community detection
Effective Resistance
Kernels on graphs
- Language
Community detection is a primary problem in the study of complex networks. When graphs are enriched with attributes, it has been found that this additional information can help in better understanding the ties among the actors composing the network and provides a deeper insight into group organization. The paper proposes the investigation of a multiobjective genetic algorithm for attributed networks extended with kernel functions for computing node similarity both in terms of structure and features. The commute-time kernel, based on the concept of random walk, is first applied to the adjacency matrix of the graph and then four kernels are applied for computing the similarity between nodes while simultaneously optimizing structure and feature dimensions. Simulations on both synthetic and real-world citation networks show that kernels effectively improve the quality of the resulting partitions.