Euclidean Distances, soft and spectral Clustering on Weighted Graphs

We define a class of Euclidean distances on weighted graphs, enabling to perform thermodynamic soft graph clustering. The class can be constructed form the "raw coordinates" encountered in spectral clustering, and can be extended by means of higher-dimensional embeddings (Schoenberg transformations). Geographical flow data, properly conditioned, illustrate the procedure as well as visualization aspects.Comment: accepted for presentation (and further publication) at the ECML PKDD 2010 conferenc

Mapping Sectoral Patterns of Technological Accumulation into the Geography of Corporate Locations. A Simple Model and Some Promising Evidence

Economies of agglomeration are central in understanding the emergence of industrial clustering. However, existing models that incorporate economies of agglomeration to explain industrial concentration have been providing a quite small set of empirically testable predictions. In this paper, we propose a baseline model in which myopic firms make reversible locational choices in presence of dynamic increasing returns from agglomeration. Despite its simplicity, the model is able to deliver predictions about the long-run distribution of the size of spatial clusters. We test the predictions of the model against data on geographical distribution of Italian firms across industrial districts. We show that, at least in some benchmark industries, accordance of theoretical predictions with data is quite high. Finally, we explore the extents to which industrial sectors exhibit different economies of agglomeration. We find that geographical clustering is highly affected by intersectoral differences in industrial innovation patterns and learning regimes.Location Dynamics, Industrial Clustering, Economies of Agglomeration, Firm Locational Choice.

Enhancing Robustness and Immunization in geographical networks

We find that different geographical structures of networks lead to varied percolation thresholds, although these networks may have similar abstract topological structures. Thus, the strategies for enhancing robustness and immunization of a geographical network are proposed. Using the generating function formalism, we obtain the explicit form of the percolation threshold $q_{c}$ for networks containing arbitrary order cycles. For 3-cycles, the dependence of $q_c$ on the clustering coefficients is ascertained. The analysis substantiates the validity of the strategies with an analytical evidence.Comment: 6 pages, 8 figure

Evaluating a Self-Organizing Map for Clustering and Visualizing Optimum Currency Area Criteria

Optimum currency area (OCA) theory attempts to define the geographical region in which it would maximize economic efficiency to have a single currency. In this paper, the focus is on prospective and current members of the Economic and Monetary Union. For this task, a self-organizing neural network, the Self-organizing map (SOM), is combined with hierarchical clustering for a two-level approach to clustering and visualizing OCA criteria. The output of the SOM is a topologically preserved two-dimensional grid. The final models are evaluated based on both clustering tendencies and accuracy measures. Thereafter, the two-dimensional grid of the chosen model is used for visual assessment of the OCA criteria, while its clustering results are projected onto a geographic map.Self-organizing maps, Optimum Currency Area, projection, clustering, geospatial visualization

Spatial Dispersion of Peering Clusters in the European Internet

We study the role played by geographical distance in the peering decisions between Internet Service Providers. Firstly, we assess whether or not the Internet industry shows clustering in peering; we then concentrate on the dynamics of the agglomeration process by studying the effects of bilateral distance in changing the morphology of existing peering patterns. Our results show a dominance of random spatial patterns in peering agreements. The sign of the effect of distance on the peering decision, driving the agglomeration/dispersion process, depends, however, on the initial level of clustering. We show that clustered patterns will disperse in the long run

Mixing navigation on networks

In this Letter, we proposed a mixing navigation mechanism, which interpolates between random-walk and shortest-path protocol. The navigation efficiency can be remarkably enhanced via a few routers. Some advanced strategies are also designed: For non-geographical scale-free networks, the targeted strategy with a tiny fraction of routers can guarantee an efficient navigation with low and stable delivery time almost independent of network size. For geographical localized networks, the clustering strategy can simultaneously increase the efficiency and reduce the communication cost. The present mixing navigation mechanism is of significance especially for information organization of wireless sensor networks and distributed autonomous robotic systems.Comment: 4 pages, and 7 figure

FARC TERRORISM IN COLOMBIA A Clustering Analysis

This paper applies clustering analysis to the Colombian armed conflict. Indeed, when applied to a FARC terrorist act database, this statistical procedure finds a natural clustering of the different FARC units according to the different types of terrorist acts they commit and identifies the military hard core of the FARC. The facts revealed in this paper should be useful not only for future military strategies, but also to determine a better priorization and geographical allocation of the scarce military resources.Clustering analysis

FARC Terrorism in Colombia: A Clustering Analysis

This paper applies clustering analysis to the Colombian armed conflict. Indeed, when applied to a FARC terrorist act database, this statistical procedure finds a natural clustering of the diferent FARC units according to the diferent types of terrorist acts they commit and identities the military hard core of the FARC. The facts revealed in this paper should be useful not only for future military strategies, but also to determine a better priorization and geographical allocation of the scarce military resources.Clustering Analysis,

Identifying Geographic Clusters: A Network Analytic Approach

In recent years there has been a growing interest in the role of networks and clusters in the global economy. Despite being a popular research topic in economics, sociology and urban studies, geographical clustering of human activity has often studied been by means of predetermined geographical units such as administrative divisions and metropolitan areas. This approach is intrinsically time invariant and it does not allow one to differentiate between different activities. Our goal in this paper is to present a new methodology for identifying clusters, that can be applied to different empirical settings. We use a graph approach based on k-shell decomposition to analyze world biomedical research clusters based on PubMed scientific publications. We identify research institutions and locate their activities in geographical clusters. Leading areas of scientific production and their top performing research institutions are consistently identified at different geographic scales

What are the Best Hierarchical Descriptors for Complex Networks?

This work reviews several hierarchical measurements of the topology of complex networks and then applies feature selection concepts and methods in order to quantify the relative importance of each measurement with respect to the discrimination between four representative theoretical network models, namely Erd\"{o}s-R\'enyi, Barab\'asi-Albert, Watts-Strogatz as well as a geographical type of network. The obtained results confirmed that the four models can be well-separated by using a combination of measurements. In addition, the relative contribution of each considered feature for the overall discrimination of the models was quantified in terms of the respective weights in the canonical projection into two dimensions, with the traditional clustering coefficient, hierarchical clustering coefficient and neighborhood clustering coefficient resulting particularly effective. Interestingly, the average shortest path length and hierarchical node degrees contributed little for the separation of the four network models.Comment: 9 pages, 4 figure