1. infoVis 2012 Kasper Dinkla, Michel A. Westenberg, and Jarke J. van Wijk

2.  Introduction  Related work ◦ Node-Link Based Representations ◦ ZAME: Interactive Large-Scale Graph Visualization  TimeMatrix  User Study  Conclusion

3.  Gene Regulatory Network (GRN) ◦ Low in-degree  Every gene is regulated by only a few other genes. Therefore, all nodes of the network have a low in- degree. ◦ Scale-free out-degree  There are few genes that regulate many others, and many genes that regulate few others. Therefore, the network’s out-degree distribution follows a power law. ◦ Few cycles  The network has few cycles because genes rarely (indirectly) regulate each other both ways.

4.  node-link diagrams (low edge to node ratio) ◦ high number of intersecting edges  adjacency matrices (high edge to node ratio) ◦ space-inefficient, sparse network green: promotion red: inhibition orange: both blue: unspecified

5.  Compressed Adjacency Matrices (CAM) ◦ Compactness  This enables a detailed overview of the entire network. ◦ Localization of motifs  This enables quick detection of subnetworks of interest. ◦ Consistent arrangement  This facilitates interaction while preserving visual orientation.

9.  the conversion of a network to a CAM is not trivial and consists of six steps: 1.the network is decomposed into weakly connected components 2.nodes with identical neighborhoods are grouped 3.strongly connected components are detected and grouped to form a DAG 4.the nodes of the DAG are partitioned into layers 5.the layers are turned into blocks that form the backbone of the CAM 6.the blocks are concatenated to form a cascade from which node positions

10. directed graph G = (V,E) V : the set of vertices (nodes) E : the set of directed edges between vertices of G

11. G I = (V I,E I ) of G, V I : represent non-overlapping subsets of V with identical neighborhoods E I : the set of directed edges of G I

14.  Layers map directly to blocks ◦ a block B i is derived from its corresponding layer L i  H i and V i, that specify the horizontal and vertical ordering of L i ’s vertices in the CAM

15. partitioning the vertices of L i into five classes:  Leaf (P L ) ◦ Vertex in L i without successors  Short root (P SR ) ◦ Vertex in L i that has a successor but no predecessors and all successors are leaves in L i+1  Long root (P LR ) ◦ Vertex in L i that has a successor but no predecessors and is not a short root  Short hub (P SH ) ◦ Vertex in L i that has a predecessor and successor, and all successors are leaves in L i+1  Long hub (P LH ) ◦ Vertex in L i that has a predecessor and successor, but is not a short hub

28.  categorizing visual analytic tasks in temporal social network analysis (Tasks 1, 2, and 3),  proposing an adjacency-matrix-based visual representation (TimeMatrix) for analyzing temporal graphs that complement node-link temporal graph visualization techniques, and  supplementing TimeMatrix with interaction techniques supporting highly interactive visual exploration of real-world social networks across multiple levels of analysis.