Contributions

• Jiajun Fan: 24%
  1. Overall project progress manager.
     1. Front-end and back-end task allocation, task delivery control, task quality control.
     2. Front-end and back-end project docking, interface design.
     3. Regular meetings are held to connect progress and solve various difficult problems at the front and back ends.
  2. Head of Backend Development Team.
     1. Complete the code writing of experiments related to classification tasks, visualization of results and front-end deployment.
  3. Front-end development technical support.
     1. Assist the front-end to optimize the web code, refactor the response function and the corresponding interface.
     2. Find all cited references and implement all linked citations in the front end.
     3. Complete all grammar checks independently, with emphasis on bolding.
  4. Web Page Deployment and Quality Control.
     1. Independently implements the entire process of deploying web pages (local) to the Internet.
     2. Successfully deployed the job to https://www.jiajunfan.com/hl/main for people all over the world to visit.
     3. Paging function implementation, debugging.
  5. Prepare and revise the manuscript of the speech.
     1. Review and correct problems in the speech, word for word.
     2. I rewrote almost the entire speech.
     3. Guaranteed the smooth delivery of the speech.
  6. Summary of Contributions.
     1. A summary of the contributions of everyone in the group.
     2. Contribution distribution is publicized and placed on the webpage
• Chengxiao Luo:15%
  1. Read the paper, especially the experimental part. Investigate the implementation and drawing of hypergraphs, expand the very simple code in THU_HyperG on github, so that it can display more details of hypergraphs, and reproduce experiments such as classification and clustering mentioned in the paper.
  2. In order to facilitate visualization, supplement the experiment of mnist handwritten digit recognition using hypergraph learning, and provide data such as the topological structure of hypergraph.
  3. Connect with the front-end, put forward a list of visualization requirements to the front-end, provide the front-end with the data format required for visualization, and perform preliminary hypergraph visualization and draw a preliminary sketch.
• Tianxiang Li:16%
  1. Read the articles given by the teacher and report in the group, and research other articles in the field of hypergraph learning.
  2. Design the content of the web page, and complete all text writing and visual design work except for the cluster classification experiment.
  3. Handover with front-end students and put forward the needs of picture production.
  4. It is expected to complete the preparation of part of the report draft.
• Zixuan Liu:16%
  1. Complete the development of web page interaction.
  2. Complete the preliminary layout of the web page.
  3. Assist in the optimization of web page layout.
• Tong Wu: 14.5%
  1. The entire front-end is discussed and divided according to the needs, the matter is divided into sub-questions and assigned to the front-end personnel, and the progress of the frontend work is followed up in real time, and the questions raised by the front-end personnel are answered in detail.
  2. Conduct research on the current mainstream visualization work, collect various visualization languages and tools, and select them according to actual needs.
  3. Think about the specific requirements provided by the backend, propose different visualization methods for different tasks, design various possibilities such as static and dynamic interactions, and add additional technical diagrams that may be used.
  4. Complete the introduction to Hypergraph, the example of distance-based Hypergraph generation, the example of attribute-based connection graph generation, and the use of tensor representation to describe the four sets of dynamic graphs of Hypergraph.
  5. Complete the paper for background introduction to Hypergraph.
• Minzhi Xie: 14.5%
  1. Preliminary preparation.
     1. Read the paper Hypergraph Learning: Methods and Practices.
     2. Think about the input and focus that the visualization task requires.
  2. Front-end research.
     1. Multiple proposed front-end tasks are investigated based on paper readings and group meetings.
     2. Find a relatively feasible dynamic graph template, and analyze the input of the template to lay the foundation for back-end data processing and front-end visualization.
  3. Visualization of the clustering process
     1. Write an html program to visualize the evolution of the clustering task based on the data obtained from the back-end clustering experiment.
  4. Manuscript writing for the experimental part.
     1. Summarize the clustering tasks and classification tasks, and write the manuscript for mid-term presentation.
  5. Check website content.
     1. Check the visual website content in detail, and give feedback on existing problems in a timely manner.