What the eye does not see: visualizations strategies for the data collection of personal networks

The graphic representation of relational data is one of the central elements of social network analysis. In this paper, the author describe the use of visualization in interview-based data collection procedures designed to obtain personal networks information, exploring four main contributions. First, the author shows a procedure by which the visualization is integrated with traditional name generators to facilitate obtaining information and reducing the burden of the interview process. Second, the author describes the reactions and qualitative interpretation of the interviewees when they are presented with an analytical visualization of their personal network. The most frequent strategies consist in identifying the key individuals, dividing the personal network in groups and classifying alters in concentric circles of relative importance. Next, the author explores how the visualization of groups in personal networks facilitates the enumeration of the communities in which individuals participate. This allows the author to reflect on the role of social circles in determining the structure of personal networks. Finally, the author compares the graphic representation obtained through spontaneous, hand-drawn sociograms with the analytical visualizations elicited through software tools. This allows the author to demonstrate that analytical procedures reveal aspects of the structure of personal networks that respondents are not aware of, as well as the advantages and disadvantages of using both modes of data collection. For this, the author presents findings from a study of highly skilled migrants living in Spain (n = 95) through which the author illustrates the challenges, in terms of data reliability, validity and burden on both the researcher and the participants

Protection of an intrusion detection engine with watermarking in ad hoc networks

Mobile ad hoc networks have received great attention in recent years, mainly due to the evolution of wireless networking and mobile computing hardware. Nevertheless, many inherent vulnerabilities exist in mobile ad hoc networks and their applications that affect the security of wireless transactions. As intrusion prevention mechanisms, such as encryption and authentication, are not sufficient we need a second line of defense, Intrusion Detection. In this pa-per we present an intrusion detection engine based on neural networks and a protection method based on watermarking techniques. In particular, we exploit information visualization and machine learning techniques in order to achieve intrusion detection and we authenticate the maps produced by the application of the intelligent techniques using a novel combined watermarking embedding method. The performance of the proposed model is evaluated under different traffic conditions, mobility patterns and visualization metrics

LINE: Large-scale Information Network Embedding

This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Most existing graph embedding methods do not scale for real world information networks which usually contain millions of nodes. In this paper, we propose a novel network embedding method called the "LINE," which is suitable for arbitrary types of information networks: undirected, directed, and/or weighted. The method optimizes a carefully designed objective function that preserves both the local and global network structures. An edge-sampling algorithm is proposed that addresses the limitation of the classical stochastic gradient descent and improves both the effectiveness and the efficiency of the inference. Empirical experiments prove the effectiveness of the LINE on a variety of real-world information networks, including language networks, social networks, and citation networks. The algorithm is very efficient, which is able to learn the embedding of a network with millions of vertices and billions of edges in a few hours on a typical single machine. The source code of the LINE is available online.Comment: WWW 201

How Do ConvNets Understand Image Intensity?

Convolutional Neural Networks (ConvNets) usually rely on edge/shape information to classify images. Visualization methods developed over the last decade confirm that ConvNets rely on edge information. We investigate situations where the ConvNet needs to rely on image intensity in addition to shape. We show that the ConvNet relies on image intensity information using visualization

Anisotropic Radial Layout for Visualizing Centrality and Structure in Graphs

This paper presents a novel method for layout of undirected graphs, where nodes (vertices) are constrained to lie on a set of nested, simple, closed curves. Such a layout is useful to simultaneously display the structural centrality and vertex distance information for graphs in many domains, including social networks. Closed curves are a more general constraint than the previously proposed circles, and afford our method more flexibility to preserve vertex relationships compared to existing radial layout methods. The proposed approach modifies the multidimensional scaling (MDS) stress to include the estimation of a vertex depth or centrality field as well as a term that penalizes discord between structural centrality of vertices and their alignment with this carefully estimated field. We also propose a visualization strategy for the proposed layout and demonstrate its effectiveness using three social network datasets.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017