Clustering a medieval social network by SOM using a kernel based distance measure

6 pagesInternational audienceIn order to explore the social organization of a medieval peasant community before the Hundred Years' War, we propose the use of an adaptation of the well-known Kohonen Self Organizing Map to dissimilarity data. In this paper, the algorithm is used with a distance based on a kernel which allows the choice of a smoothing parameter to control the importance of local or global proximities

Batch kernel SOM and related Laplacian methods for social network analysis

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 modeled through a weighted graph that has been directly built from a large corpus of agrarian contracts

Mining a medieval social network by kernel SOM and related methods

This paper briefly presents several ways to understand the organization of a large social network (several hundreds of persons). We compare approaches coming from data mining for clustering the vertices of a graph (spectral clustering, self-organizing algorithms. . .) and provide methods for representing the graph from these analysis. All these methods are illustrated on a medieval social network and the way they can help to understand its organization is underlined

Neural Networks for Complex Data

Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such as graphs and functions. This paper summarizes advances on those themes from the last decade, with a focus on results obtained by members of the SAMM team of Universit\'e Paris

Optimizing an Organized Modularity Measure for Topographic Graph Clustering: a Deterministic Annealing Approach

This paper proposes an organized generalization of Newman and Girvan's modularity measure for graph clustering. Optimized via a deterministic annealing scheme, this measure produces topologically ordered graph clusterings that lead to faithful and readable graph representations based on clustering induced graphs. Topographic graph clustering provides an alternative to more classical solutions in which a standard graph clustering method is applied to build a simpler graph that is then represented with a graph layout algorithm. A comparative study on four real world graphs ranging from 34 to 1 133 vertices shows the interest of the proposed approach with respect to classical solutions and to self-organizing maps for graphs

A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph

International audienceFlexible and efficient variants of the Self Organizing Map algorithm have been proposed for non vector data, including, for example, the dissimilarity SOM (also called the Median SOM) and several kernelized versions of SOM. Although the first one is a generalization of the batch version of the SOM algorithm to data described by a dissimilarity measure, the various versions of the second ones are stochastic SOM. We propose here to introduce a batch version of the kernel SOM and to show how this one is related to the dissimilarity SOM. Finally, an application to the classification of the vertices of a graph is proposed and the algorithms are tested and compared on a simulated data set

A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph

Flexible and efficient variants of the Self Organizing Map algorithm have been proposed for non vector data, including, for example, the dissimilarity SOM (also called the Median SOM) and several kernelized versions of SOM. Although the first one is a generalization of the batch version of the SOM algorithm to data described by a dissimilarity measure, the various versions of the second ones are stochastic SOM. We propose here to introduce a batch version of the kernel SOM and to show how this one is related to the dissimilarity SOM. Finally, an application to the classification of the vertices of a graph is proposed and the algorithms are tested and compared on a simulated data set

Adaptive Capacity of the Water Management Systems of Two Medieval Khmer Cities, Angkor and Koh Ker

abstract: Understanding the resilience of water management systems is critical for the continued existence and growth of communities today, in urban and rural contexts alike. In recent years, many studies have evaluated long-term human-environmental interactions related to water management across the world, highlighting both resilient systems and those that eventually succumb to their vulnerabilities. To understand the multitude of factors impacting resilience, scholars often use the concept of adaptive capacity. Adaptive capacity is the ability of actors in a system to make adaptations in anticipation of and in response to change to minimize potential negative impacts. In this three-paper dissertation, I evaluate the adaptive capacity of the water management systems of two medieval Khmer cities, located in present-day Cambodia, over the course of centuries. Angkor was the capital of the Khmer Empire for over 600 years (9 th -15 th centuries CE), except for one brief period when the capital was relocated to Koh Ker (921 – 944 CE). These cities both have massive water management systems that provide a comparative context for studying resilience; while Angkor thrived for hundreds of years, Koh Ker was occupied as the capital of the empire for a relatively short period. In the first paper, I trace the chronological and spatial development of two types of settlement patterns (epicenters and lower-density temple-reservoir settlement units) at Angkor in relation to state-sponsored hydraulic infrastructure. In the second and third papers, I conduct a diachronic analysis using empirical data for the adaptive capacity of the water management systems at both cities. The results suggest that adaptive capacity is useful for identifying causal factors in the resilience and failures of systems over the long term. The case studies also demonstrate the importance and warn of the danger of large centralized water management features.Dissertation/ThesisDoctoral Dissertation Anthropology 201

A Framework for Measuring Urban Sprawl from Crowd-Sourced Data

The amount of people living in cities by 1800 was roughly around 3 percent of the world population. This number has increased dramatically during the last centuries, and currently it is estimated that one out of two people lives in cities. Furthermore, according to United Nations 60 percent of the world population will live in cities by 2030.This situation brings new challanges on how to conceive cities that host such amounts of population in a sustainable way while sacrificing as little as possible the inhabitants’ quality of life. This sustainability should address to several aspects that can be classified as economical, social and environmental. The cities are and will be centers of economical activity, and thus should provide facilities for business, innovation and culture. Such economical development should benefit all the levels of the social hierarchy, preventing inequalities and social segregation. The environmental part concerns the efficient utilization of the resources as well as the minimization of the impact on the ecosystems that sourround such cities. A convenient public transportation network, the preservation of green areas, the recycling of waste or the use of renewable energies are some examples of means to reduce the cities’ environmental impact.Unluckily by taking a look at the current megacities, it can be easily observed that very few of them meet the sustainability characteristics formerly reviewed. This can be partly explained by the urbanism pattern that derives from the processes of industrialization. When a city experiences such industrialization, with the related economic growth and the expansion of transportation networks, the middle class tends to migrate towards the outskirts of the city, potentially to live in terraced houses sorrounded by green areas. Such a pattern is commonly referred to as urban sprawl, and it was first observed in London and Paris during the 19th century. Cities like New York, Chicago went through this process during the early 20th century, and so did most central and northern- European cities around 1970-1990. Nowadays urban sprawl might even be more prevalent in developing countries, as it is the case with Mexico City, Beijing, Delhi, Johannesburg or Cairo