Graphical Analysis of Social Group Dynamics

Identifying communities in social networks becomes an increasingly important research problem. Several methods for identifying such groups have been developed, however, qualitative analysis (taking into account the scale of the problem) still poses serious problems. This paper describes a tool for facilitating such an analysis, allowing to visualize the dynamics and supporting localization of different events (such as creation or merging of groups). In the final part of the paper, the experimental results performed using the benchmark data (Enron emails) provide an insight into usefulness of the proposed tool.Comment: Fourth International Conference on Computational Aspects of Social Networks, CASoN 2012, Sao Carlos, Brazil, November 21-23, 2012, pp. 41-46; IEEE Computer Society, 201

Family composition and age at menarche: findings from the international Health Behaviour in School-Aged Children Study

This research was funded by The University of St Andrews and NHS Health Scotland.Background Early menarche has been associated with father absence, stepfather presence and adverse health consequences in later life. This article assesses the association of different family compositions with the age at menarche. Pathways are explored which may explain any association between family characteristics and pubertal timing. Methods Cross-sectional, international data on the age at menarche, family structure and covariates (age, psychosomatic complaints, media consumption, physical activity) were collected from the 2009–2010 Health Behaviour in School-aged Children (HBSC) survey. The sample focuses on 15-year old girls comprising 36,175 individuals across 40 countries in Europe and North America (N = 21,075 for age at menarche). The study examined the association of different family characteristics with age at menarche. Regression and path analyses were applied incorporating multilevel techniques to adjust for the nested nature of data within countries. Results Living with mother (Cohen’s d = .12), father (d = .08), brothers (d = .04) and sisters (d = .06) are independently associated with later age at menarche. Living in a foster home (d = −.16), with ‘someone else’ (d = −.11), stepmother (d = −.10) or stepfather (d = −.06) was associated with earlier menarche. Path models show that up to 89% of these effects can be explained through lifestyle and psychological variables. Conclusions Earlier menarche is reported amongst those with living conditions other than a family consisting of two biological parents. This can partly be explained by girls’ higher Body Mass Index in these families which is a biological determinant of early menarche. Lower physical activity and elevated psychosomatic complaints were also more often found in girls in these family environments.Publisher PDFPeer reviewe

Fast filtering and animation of large dynamic networks

Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: i) captures persistent trends in the structure of dynamic weighted networks, ii) smoothens transitions between the snapshots of dynamic network, and iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.Comment: 6 figures, 2 table

Visual Detection of Structural Changes in Time-Varying Graphs Using Persistent Homology

Topological data analysis is an emerging area in exploratory data analysis and data mining. Its main tool, persistent homology, has become a popular technique to study the structure of complex, high-dimensional data. In this paper, we propose a novel method using persistent homology to quantify structural changes in time-varying graphs. Specifically, we transform each instance of the time-varying graph into metric spaces, extract topological features using persistent homology, and compare those features over time. We provide a visualization that assists in time-varying graph exploration and helps to identify patterns of behavior within the data. To validate our approach, we conduct several case studies on real world data sets and show how our method can find cyclic patterns, deviations from those patterns, and one-time events in time-varying graphs. We also examine whether persistence-based similarity measure as a graph metric satisfies a set of well-established, desirable properties for graph metrics

SWING: A system for visualizing web graphs

A Web graph refers to the graph that is used to represent relationships between Web pages in cyberspace, where a node represents a URL and an edge indicates a link between two URLs. A Web graph is a very huge graph as growing with cyberspace. This paper presents a pipeline for extracting web information from cyberspace to a web graph and layout techniques for making the web graph more readable. As the size of computer screen is limited, only a small part of the Web graph can be displayed. Several layout techniques should be adapted and combined effectively for web graph visualization. The visualization process incorporates graph drawing algorithms, layout adjustment methods, as well as filtering and clustering methods in order to decide which part of the Web graph should be displayed and how to display it based on the user's focus in navigation

Analysis of Graph Layout Algorithms for Use in Command and Control Network Graphs

This research is intended to determine which styles of layout algorithm are well suited to Command and Control (C2) network graphs to replace current manual layout methods. Manual methods are time intensive and an automated layout algorithm should decrease the time spent creating network graphs. Simulations on realistic synthetically generated graphs provide information to help infer which algorithms perform better than others on this problem. Data is generated using statistics drawn from multiple real world C2 network graphs. The three algorithms tested against this data are the Spectral algorithm, the Dot algorithm, and the Fruchterman-Reingold algorithm. The results include a multiple objective statistics designed to inform on the algorithms performance in both aesthetic characteristics defined in literature, as well as some characteristics defined by the research sponsor. The results suggest that the Dot algorithm performs better with respect to the sponsor defined characteristics, whereas the Fruchterman-Reingold algorithm performs better on aesthetic characteristics

Node overlap removal by growing a tree

Node overlap removal is a necessary step in many scenarios including laying out a graph, or visualizing a tag cloud. Our contribution is a new overlap removal algorithm that iteratively builds a Minimum Spanning Tree on a Delaunay triangulation of the node centers and removes the node overlaps by ”growing” the tree. The algorithm is simple to implement yet produces high quality layouts. According to our experiments it runs several times faster than the current state-of-the-art methods