Hierarchical Spatial Organization of Geographical Networks

In this work we propose the use of a hirarchical extension of the polygonality index as a means to characterize and model geographical networks: each node is associated with the spatial position of the nodes, while the edges of the network are defined by progressive connectivity adjacencies. Through the analysis of such networks, while relating its topological and geometrical properties, it is possible to obtain important indications about the development dynamics of the networks under analysis. The potential of the methodology is illustrated with respect to synthetic geographical networks.Comment: 3 page, 3 figures. A wokring manuscript: suggestions welcome

DyNetVis: a system for visualization of dynamic networks

The concept of networks has been important in the study of complex systems. In networks, links connect pairs of nodes forming complex structures. Studies have shown that networks not only contain structure but may also evolve in time. The addition of the temporal dimension adds complexity on the analysis and requests the development of innovative methods for the visualization of real-life networks. In this paper we introduce the Dynamic Network Visualization System (DyNetVis), a software tool for visualization of dynamic networks. The system provides several tools for user interaction and offers two coordinated visual layouts, named structural and temporal. Structural refers to standard network drawing techniques, in which a single snapshot of nodes and links are placed in a plane, whereas the temporal layout allows for simultaneously visualization of several temporal snapshots of the dynamic network. In addition, we also investigate two approaches for temporal layout visualization: (i) Recurrent Neighbors, a node ordering strategy that highlights frequent connections in time, and (ii) Temporal Activity Map (TAM), a layout technique with focus on nodes activity. We illustrate the applicability of the layouts and interaction functionalities provided by the system in two visual analysis case studies, demonstrating their advantages to improve the overall user experience on visualization and exploratory data analysis on dynamic networks

A comparative analysis for visualizing the temporal evolution of contact networks : a user study

Temporal networks are widely used to map phenomena into complex systems in several research disciplines, such as computer science, business, and biology. Several layouts can be used in visual analyses of temporal networks. The identification of the most suitable for a given task is, however, not trivial. This paper presents a user study that analyzes the performance of four different layouts: Massive Sequence View (MSV), Temporal Activity Map, matrix animation, and structural animation, when applied to pattern detection tasks of time-evolving networks. Our results show that all four layouts are appropriate to perform the evaluated tasks; however, the structural animation and MSV scored higher across different types of users

A streaming edge sampling method for network visualization

Visualization strategies facilitate streaming network analysis by allowing its exploration through graphical and interactive layouts. Depending on the strategy and the network density, such layouts may suffer from a high level of visual clutter that hides meaningful temporal patterns, highly active groups of nodes, bursts of activity, and other important network properties. Edge sampling improves layout readability, highlighting important properties and leading to easier and faster pattern identification and decision making. This paper presents Streaming Edge Sampling for Network Visualization-SEVis, a streaming edge sampling method that discards edges of low-active nodes while preserving a distribution of edge counts that is similar to the original network. It can be applied to a variety of layouts to enhance streaming network analyses. We evaluated SEVis performance using synthetic and real-world networks through quantitative and visual analyses. The results indicate a higher performance of SEVis for clutter reduction and pattern identification when compared with other sampling methods

Does Social Software Support Service Innovation?

Abstract Recent Internet technologies and web‐based applications, such as social software, are being increasingly applied in firms. Social software can be employed for knowledge management and for external communication enabling access to internal and external knowledge. Knowledge, in turn, constitutes one of the main inputs to service innovation. Hence, social software has the potential to support service innovation. Using data from German IT and knowledge‐intensive service firms, this is the first paper that empirically analyses whether the use of social software applications triggers innovation. It refers to a knowledge production function in which social software use constitutes the knowledge sourcing activity. The results reveal a positive relationship between social software and service innovation. Since this result is robust when controlling for former innovative activities and the previous propensity to adopt new technologies and to change processes, the analysis suggests that the causality runs from social software to innovation.Social Software, Web 2.0, Service Innovation, Knowledge Management, O31, O33, M10,