Geometric embeddings for faster and better multi-way netlist partitioning
DAC '93 Proceedings of the 30th international Design Automation Conference
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A stochastic self-organizing map for proximity data
Neural Computation
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
Self-Organizing Maps
Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
Handbook of Graphs and Networks: From the Genome to the Internet
Handbook of Graphs and Networks: From the Genome to the Internet
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
How to make large self-organizing maps for nonvectorial data
Neural Networks - New developments in self-organizing maps
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Fast algorithm and implementation of dissimilarity self-organizing maps
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A survey of kernel and spectral methods for clustering
Pattern Recognition
Computer Science Review
Graph self-organizing maps for cyclic and unbounded graphs
Neurocomputing
Median Topographic Maps for Biomedical Data Sets
Similarity-Based Clustering
The network of French legal codes
Proceedings of the 12th International Conference on Artificial Intelligence and Law
Visualizing dissimilarity data using generative topographic mapping
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Probabilistic self-organizing maps for qualitative data
Neural Networks
Network analysis of the French environmental code
AICOL-I/IVR-XXIV'09 Proceedings of the 2009 international conference on AI approaches to the complexity of legal systems: complex systems, the semantic web, ontologies, argumentation, and dialogue
Relational generative topographic mapping
Neurocomputing
Topographic mapping of dissimilarity data
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Assessing the efficiency of health care providers: a SOM perspective
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Linear time heuristics for topographic mapping of dissimilarity data
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Approximation techniques for clustering dissimilarity data
Neurocomputing
Co-occurring cluster mining for damage patterns analysis of a fuel cell
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Kernel robust soft learning vector quantization
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Exploiting the self-organizing financial stability map
Engineering Applications of Artificial Intelligence
Learning vector quantization for (dis-)similarities
Neurocomputing
| Hi-index | 0.01 |
Large graphs are natural mathematical models for describing the structure of the data in a wide variety of fields, such as web mining, social networks, information retrieval, biological networks, etc. For all these applications, automatic tools are required to get a synthetic view of the graph and to reach a good understanding of the underlying problem. In particular, discovering groups of tightly connected vertices and understanding the relations between those groups is very important in practice. This paper shows how a kernel version of the batch self-organizing map can be used to achieve these goals via kernels derived from the Laplacian matrix of the graph, especially when it is used in conjunction with more classical methods based on the spectral analysis of the graph. The proposed method is used to explore the structure of a medieval social network modelled through a weighted graph that has been directly built from a large corpus of agrarian contracts.