A System for Exploring Multivariate Social Networks

2010

Abstract Social networks collected by historians or sociologists typically have a large number of actors and edge attributes. Applying social network analysis (SNA) algorithms to these networks produces additional attributes such as degree, centrality, and clustering coefficients. Understanding the effects of this plethora of attributes is one of the main challenges of multivariate SNA. We present the design of GraphDice, a multivariate network visualization system for exploring the attribute space of edges and actors.

2020

Social network analysis helps analysts to study the relationships among different nodes of a network and get valuable insights. The common output of social networks analysis is measuring the node's centrality and graphical visualization, which helps us to understand the degree and nature of relationships between different nodes of the network. Online Social networks produce data that provides tons of knowledge. But it is challenging to extract the data and get useful information through its analysis and visualization. Getting insights from small-scale networks is comparatively easy since they can be manually constructed and graphically represented. But it gets very time-consuming and hectic process as the network expands. Several social network analysis tools are available for this purpose. These tools also come with embedded techniques for analyzing social networks, reducing the overhead for the manual study of complex networks. The focus of this paper is on the analysis and co...

IEEE Transactions on Visualization and Computer Graphics, 2019

Fig. 1. Juniper visualizing a co-author network starting at the TreePlus paper as a spanning tree. The graph is extended for Catherine Plaisant to include all her papers and co-authors. The papers are shown in aggregate form and faceted by Ǽ CHI and ǟ TVCG. Most of the tree use a conventional layout, but the descendants of Catherine Plaisant's node are shown in level layout, which groups nodes by distance to the branch root. Nodes in this branch are aggregated, with the exception of prolific authors, which are revealed using a degree-of-interest function. Ben Shneiderman is highlighted; two hidden edges originate at his node. The edge-count table shows a summary of the connectivity of each node. The adjacency matrix shows explicit connections to selected, highly connected nodes. The attribute table shows attributes about the authors and papers for individual as well as aggregated rows.

2007 6th International Asia-Pacific Symposium on Visualization, 2007

In this paper, we introduce a new method, GraphScape, to visualize multivariate networks, i.e., graphs with multivariate data associated with their nodes. GraphScape adopts a landscape metaphor with network structure displayed on a 2D plane and the surface height in the third dimension represents node attribute. More than one attribute can be visualized simultaneously by using multiple surfaces. In addition, GraphScape can be easily combined with existing methods to further increase the total number of attributes visualized. One of the major goals of GraphScape is to reveal multivariate graph clustering, which is based on both network structure and node attributes. This is achieved by a new layout algorithm and an innovative way of constructing attribute surface, which also allows visual clustering at different scales through interaction. A simplified attribute surface model is also proposed to reduce computation requirement when visualizing large networks. GraphScape is applied to networks of three different size (20, 100, and 1500) to demonstrate its effectiveness.

2012

Abstract Increasingly, social network datasets contain social attribute information about actors and their relationship. Analyzing such network with social attributes requires making sense of not only its structural features, but also the relationship between social features in attributes and network structures.

Smart Graphics, 2008

The analysis of scientific articles produced by different groups of authors helps to identify and characterize research groups and collaborations among them. Although this is a quite studied area, some issues, such as quick understanding of groups and visualization of large social networks still pose some interesting challenges. In order to contribute to this study, we present a solution based in Overlapper, a tool for the visualization of overlapping groups that makes use of an enhanced variation of force-directed graphs. For a real case study, the tool has been applied to articles in the DBLP database.

HAL (Le Centre pour la Communication Scientifique Directe), 2017

Many computing applications imply dealing with network data, for example, social networks, communications and computing networks, epidemiological networks, among others. These applications are usually based on multivariate graphs, i.e., graphs in which items and relationships have multiple attributes. Most of the visualization techniques described in the literature for dealing with multivariate graphs focus either on problems associated with the visualization of topology or on problems associated with the visualization of the items' attributes. The integration of these two components (topology and multiple attributes) in a single visualization turns into a challenge due to the necessity of simultaneously representing the connections and mapping attributes possibly generating overlapping elements. Among usual strategies to overcome this legibility problem we find filtering and aggregation that makes possible a simplified representation with reduced size and density providing a general view. However, this simplification may lead to a reduction of the amount of information being displayed, while in several applications the graph details still need to be represented in order to make possible in-depth data analysis. In face of that, we propose ClusterVis, a visualization technique aiming at exploring nodes attributes pertaining to sub-graphs, which are either obtained from clustering algorithms or some user-defined criteria. The technique allows comparing attributes of nodes while keeping the representation of the relationships among them. The technique was implemented within a visualization framework and evaluated by potential users. CCS Concepts •Human-centered Computing ➝ Visualization application domains ➝ Information Visualization.

Proceso, 2019

Judith Amador Tello, “Perdón histórico, el atrevimiento del presidente”, Proceso, jueves 4 de abril de 2019. Entrevista con Carlos Aguirre Rojas, Ricardo Pérez Montfort y Rodrigo Martínez Baracs https://www.proceso.com.mx/cultura/2019/4/4/perdon-historico-el-atrevimiento-del-presidente-222783.html

Beat one's self up: tự trách mình (khi dùng, thay one's self bằng mysel, yourself, himself, herself...) Break down: bị hư Break in: đột nhập vào nhà Break up with s.o: chia tay người yêu, cắt đứt quan hệ tình cảm với ai đó Bring s.th up: đề cập chuyện gì đó Bring s.o up: nuôi nấng (con cái) Brush up on s.th: ôn lại Call for sth: cần cái gì đó; Call for s.o : kêu người nào đó, cho gọi ai đó, yêu cầu gặp ai đó Carry out: thực hiện (kế hoạch) Catch up with s.o: theo kịp ai đó Check in: làm thủ tục vào khách sạn Check out: làm thủ tục ra khách sạn Check sth out: tìm hiểu, khám phá cái gì đó Clean s.th up: lau chùi Come across as: có vẻ (chủ ngữ là người) Come off: tróc ra, sút ra Come up against s.th: đối mặt với cái gì đó Come up with: nghĩ ra Cook up a story: bịa đặt ra 1 câu chuyện Cool down: làm mát đi, bớt nóng, bình tĩnh lại (chủ ngữ có thể là người hoặc vật) Count on s.o: tin cậy vào người nào đó Cut down on s.th: cắt giảm cái gì đó Cut off: cắt lìa, cắt trợ giúp tài chính Do away with s.th: bỏ cái gì đó đi không sử dụng cái gì đó Do without s.th: chấp nhận không có cái gì đó Dress up: ăn mặc đẹp Drop by: ghé qua

Below is a collection of tutorial slides originally posted on the Facebook: