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Spring Embedders and Force Directed Graph Drawing Algorithms

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arxiv 1201.3011 v1 pith:DBUZGPSK submitted 2012-01-14 cs.CG cs.DMcs.DS

Stephen G. Kobourov This is my paper · ORCID
classification cs.CGcs.DMcs.DS
keywords algorithmsgraphsgraphembedderslayoutsmethodsspringtend
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Force-directed algorithms are among the most flexible methods for calculating layouts of simple undirected graphs. Also known as spring embedders, such algorithms calculate the layout of a graph using only information contained within the structure of the graph itself, rather than relying on domain-specific knowledge. Graphs drawn with these algorithms tend to be aesthetically pleasing, exhibit symmetries, and tend to produce crossing-free layouts for planar graphs. In this survey we consider several classical algorithms, starting from Tutte's 1963 barycentric method, and including recent scalable multiscale methods for large and dynamic graphs.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Contrastive learning of extragalactic stellar streams. Sculpting a latent space of representations with DES DR2 photometry

    astro-ph.GA 2026-01 conditional novelty 6.0 of 10

    Applying NNCLR contrastive learning to DES DR2 galaxy cutouts yields embeddings that cluster major-merger galaxies but not stellar streams; a tiered sigmoid scaling redirects the network's saliency toward low-surface-...

  2. Lower Ricci Curvature for Hypergraphs

    cs.LG 2025-06 conditional novelty 6.0 of 10

    Hypergraph lower Ricci curvature (HLRC) is a new closed-form, bounded curvature score for hyperedges that separates intra-community from bridge-like hyperedges.

  3. Chronotome: Real-Time Topic Modeling for Streaming Embedding Spaces

    cs.HC 2025-09 conditional novelty 5.0 of 10

    A visualization tool that combines force-based embedding layout with per-timestep clustering to let users track how semantic topics evolve over time in streaming data.

  4. Topolow: Force-Directed Euclidean Embedding of Dissimilarity Data with Robustness Against Non-Metricity and Sparsity

    cs.CG 2025-08 conditional novelty 5.0 of 10

    Topolow embeds sparse, non-metric dissimilarity data into Euclidean space via a stochastic, gradient-free spring-particle optimization and reports lower reconstruction stress than classical and iterative MDS.

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