An Algorithm for Drawing General Undirected Graphs

By Tomihisa Kamada, S. Kawai. 1989

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Citations in the corpus

ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software (2014, background): “It is especially the case of the old Kamada Kawai [10] and more recently GEM [5].”
A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs (2006, methodology): “This software instantiates an algorithm proposed by Kamada and Kawai (1989): for any pair of nodes, the graph is drawn such that the graphical distance between nodes approximates the path length between them; thus, the hubs of the network, which have a short mean path length to other regions, will…”
A review of theory and practice in scientometrics (2015, background): “Kamada and Kawai (1989) were the first to reformulate the problem of achieving target distances in a network in terms of energy optimization.”
The Ubiquity of Small-World Networks (2011, methodology): “The original network is presented here flanked by an equivalent lattice and random network using the Kamada-Kawai model, an algorithm used to optimize the spatial representation of a graph based on the connections (Kamada and Kawai 1989).”
Energy Models for Graph Clustering (2007, unknown): “Most existing energy models and force models 1 for general undirected graphs (e.g. [16, 30, 20, 13]) have not been designed to ﬁnd clusters, but to produce readable visualizations.”
Efficient, High-Quality Force-Directed Graph Drawing (2006, methodology): “In the force model of Kamada and Kawai [7], which we will refer to as the spring model, springs are attached between any two pairs of vertices, with the ideal Efficient, High-Quality Force-Directed Graph Drawing 43”
Functional Brain Networks Develop from a “Local to Distributed” Organization (2009, methodology): “Found at: doi:10.1371/journal.pcbi.1000381.s007 (0.43 MB TIF) Video S1 Over age, the graph architecture matures from a ‘‘local’’ organization to a ‘‘distributed’’ organization.”
Pajek - Program for Large Network Analysis (1999, methodology): “…algorithms for automatic graph drawing were implemented: spring embedders based on minimisation of the total energy of the system (Kamada-Kawai [10] and Fruchterman-Reingold [6]), layouts determined by eigenvectors (Lanczos algorithm [3, 5]), drawing in layers (genealogies and other acyclic…”
Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer’s Disease (2009, methodology): “, 2005) and represented the relationships among regions using the Kamada–Kawai graphing algorithm (Kamada and Kawai, 1989).”
A Fast Adaptive Layout Algorithm for Undirected Graphs (1994, background): “In general, we can confirm the claim of Kamada/Kawai [9] that initial positions do not have great influence on the resulting pictures but on the number of rounds to be performed to reach equilibrium.”
Metrics for Graph Drawing Aesthetics (2002, methodology): “The following layout algorithms from the GraphLet s ystem have been used in these examples [1]: • GEM (spring): based on the algorithm by Frick [4], • DAG: based on the algorithm by Sugiyama and Misue [ 11], • EXT-DAG: an extension of the DAG algorithm, • ITS (iterative constraint spring, grid based”
Drawing Large Graphs with a Potential-Field-Based Multilevel Algorithm (2004, methodology): “In order to save running time, the multilevel algorithms [7, 9, 17] use the gridvariant method of [6] or variations of [12] that are comparatively inaccurate approximative variations of the original single-level algorithms [6, 12].”
Spring Embedders and Force Directed Graph Drawing Algorithms (2012, methodology): “For the ﬁne-scale relocations, the authors suggest using force-directed calculations as those of Eades [20], Fruchterman-Reingold [30], or Kamada-Kawai [42].”
Graph Visualization and Navigation in Information Visualization: A Survey (2000, background): “In some cases, their output can even behave well with respect to edge-crossing minimization without any supplementary efforts [47].”