Graph Visualization

Networks exist everywhere in this complex modern world, including particle network, gene network, publication network, SNS friendship network, encounter network, and so on. Graph Visualization is an indispensable tool for network analysis, because of its intuitive, user-friendly and insight-triggering representation of complex networks.

2012 Visualization Summershool friendship evolution graph data

Data source 2012 Visualization Summer School was held in Peking University from Aug. 13th to Aug. 20th. There were 57 students from all over the country. We recorded the students friendship evolution every day, since they could be friends by sitting together as deskmate, or discussed projects with each other, or had lunch together. Students were asked to update their new friends each day by filling a web form.


Data description

  1. The data is collected from Aug. 13th to Aug. 19th, 7 days.
  2. The data contains 58 nodes, and 253 edges in total.
  3. The data format :

Part 1 - Students Information

   ID, School, Group
   Each student has a student "ID", the "School" he comes from, and the "Group" he is assigned. 
   Here we use numbers to replace real shool name for protecting students' privacy.
  

Part 2 - Friendship Dynamic Graph

   Logdate, OwnerID, TargetID, Records:Time-Reason-Detail
   
   On the day of "Logdate", student A (OwnerID) makes friend with student B (TargetID) through an event (Records), 
   the "Record" tells the "Time" and "Reason" they know each other with some "Detail"s. The "NOTSET" in "Records" 
   means the user doesn't fill this item, so it is unknown.


Click Here to download the data

Graph data preview


Data Visualization Example
The node-link graph and matirx on Aug. 13th

The node-link graph and matirx on Aug. 19th

Publication

  • Xiaoru Yuan, Limei Che, Yifan Hu, and Xin Zhang. “Intelligent Graph Layout Using Many People's Input”. IEEE Transactions on Visualization and Computer Graphics (InfoVis'12), 18(12):-, 2012.
  • Jing Yang, Yujie Liu, Xin Zhang, Xiaoru Yuan, Ye Zhao, Scott Barlowe, and Shixia Liu. “PIWI: Visually Exploring Graphs Based on Their Community Structure”. IEEE Transactions on Visualization and Computer Graphics, 18(): - , 2012.

Intelligent Graph Layout Using Many People's Input

In this work, we propose a new strategy for graph drawing utilizing layouts of many sub-graphs supplied by a large group of people in a crowd sourcing manner. We developed an algorithm based on Laplacian constrained distance embedding to merge subgraphs submitted by different users, while attempting to maintain the topological information of the individual input layouts. To facilitate collection of layouts from many people, a light-weight interactive system has been designed to enable convenient dynamic viewing, modification and traversing between layouts. Compared with other existing graph layout algorithms, our approach can achieve more aesthetic and meaningful layouts with high user preference.

Co-authorship Visualization of Academic Publications

Co-authorship Visualization of Academic Publications

In our lab, we are now working on

  1. Visual analysis of publication network
  2. Node-link diagram, matrix and compound representation of network
  3. Clustering and level-of-detail methods for network visualization
  4. Visual analysis of blog, forum and twitter opinions network

Participants:

  1. Prof. Xiaoru Yuan (Supervisor)
  2. Limei Che (Student)
  3. Xin Zhang (Student)
 
project/graph.txt · Last modified: 2012/11/18 13:50 by clmtulip