DeepGD: A Deep Learning Framework for Graph Drawing Using GNN
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Computer Graphics and Applications"
DOI: 10.1109/mcg.2021.3093908
Abstract: In the past decades, many graph drawing techniques have been proposed for generating aesthetically pleasing graph layouts. However, it remains a challenging task since different layout methods tend to highlight different characteristics of the graphs.… read more here.
Keywords: learning framework; graph drawing; graph; deep learning ... See more keywords
Toward Efficient Deep Learning for Graph Drawing (DL4GD).
Sign Up to like & getrecommendations! Published in 2022 at "IEEE transactions on visualization and computer graphics"
DOI: 10.1109/tvcg.2022.3222186
Abstract: Due to their great performance in many challenges, Deep Learning (DL) techniques keep gaining popularity in many fields. They have been adapted to process graph data structures to solve various complicated tasks such as graph… read more here.
Keywords: drawing dl4gd; graph; deep learning; graph drawing ... See more keywords
Event‐based Dynamic Graph Drawing without the Agonizing Pain
Sign Up to like & getrecommendations! Published in 2022 at "Computer Graphics Forum"
DOI: 10.1111/cgf.14615
Abstract: Temporal networks can naturally model real‐world complex phenomena such as contact networks, information dissemination and physical proximity. However, nodes and edges bear real‐time coordinates, making it difficult to organize them into discrete timeslices, without a… read more here.
Keywords: dynamic graph; event; graph drawing; event based ... See more keywords