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

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

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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.

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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

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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.

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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

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

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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

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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

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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

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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.

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