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[T-VCG 2022] Multi-Level Area Balancing of Clustered Graphs

uhhyunjoo opened this issue · comments

  • 다양한 필드의 complex relationships 를 잘 이해하기 위해 clusted graphs 를 laying out 하기 위한 multi-level area balancing techgnique 을 제안한다.
  • Clustered graphs 는 주로 attribute-based grouping information 을 이용하여 relationships 를 모델링하는 데 쓰인다.
  • 여기서는 여러 수준의 세부 정보에서 Voronoites tessellations 을 사용하여 input screen space을 계층적으로 분할할 것을 제안한다.

graph layout 에 대해 space partioning 을 하는 것

  • decomposed into several levels in top-down fashion

  • 각 레벨에 대한 area balancing 이 고려된다. information complexity or density of the level underneath

  • four-level area balancing approach to allocate appropriate space for each vertext within a cluster

  • this design decision has been made based on the topological properties of networks

  1. Category-level : cluster properties
  2. Component-level : connected-component properties
  3. Topology-level : the abstract form of sub-networks
  4. Detail-level : the detailed sub-networks
    -> space partitioninig
  • in practice, each level is computed by a force-based layout followed by a schematization approach for simplifying the sahpes of the contours to accomplish detail-level vertext area balancing

graph skeleton g_s \in {G_C, G_M, G_T, G_D} for each of the for levels

  • the layout of each graph skeleton is used to guide the positioning of its belonging vertices to their expected position, in order to retain a balanced distribution.

  • category-level

  • component-level : we drag components sharing some vertexts close to each other and align cells containing subgrtphs

  • we report the running time
  • Table 2 summarizes the properties of our datasets.
    • number of vertexts, edges, clusters, and graph densities
  • subscrit D : after vertext duplication
  • used same graph density function
  1. Measuring space coverage and time complexity
  • fragmented empty space is not fully utilized...
  • fully usese the screen as preferred
  • we inroduce two coverage measures
    • M_N : coefficient of variation of distances of the vertexts to their k nearest neighbors -> to examine if each vertext has equal distances to its neighbors
    • M_V : corresponds to the number of pixels of its corresponding Voronoi cell.
  • both measures show the area assigned to each vertext is more balanced in our approach.
  • this tendency increases as the data size increasese.
  1. Interview with experts in biology
  • six domain experts, who are experienced with manualy creating pathway diagrams, and discussed our selected aesthetic criteria and the quality of the results with them.

(1) : explaining how to read visualization (datasets and color coding)