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.
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
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.
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Data Visualization Example
The node-link graph and matirx on Aug. 13th
The node-link graph and matirx on Aug. 19th
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.
In our lab, we are now working on