Results 1 to 10 of about 6,540,046 (323)

Atomistic Line Graph Neural Network for improved materials property predictions [PDF]

open access: yesNpj Computational Materials, 2021
Graph neural networks (GNN) have been shown to provide substantial performance improvements for atomistic material representation and modeling compared with descriptor-based machine learning models.
K. Choudhary, Brian L. DeCost
semanticscholar   +3 more sources

TransLiG: a de novo transcriptome assembler that uses line graph iteration. [PDF]

open access: yesGenome Biol, 2019
We present TransLiG, a new de novo transcriptome assembler, which is able to integrate the sequence depth and pair-end information into the assembling procedure by phasing paths and iteratively constructing line graphs starting from splicing graphs ...
Liu J, Yu T, Mu Z, Li G.
europepmc   +2 more sources

Graphs whose line graphs are ring graphs [PDF]

open access: yesAKCE International Journal of Graphs and Combinatorics, 2020
Given a graph H, a path of length at least two is called an H-path if meets H exactly in its ends. A graph G is a ring graph if each block of G which is not a bridge or a vertex can be constructed inductively by starting from a single cycle and then in ...
Mahdi Reza Khorsandi
doaj   +2 more sources

Modeling functional connectivity changes during an auditory language task using line graph neural networks [PDF]

open access: yesFrontiers in Computational Neuroscience
Functional connectivity (FC) refers to the activation correlation between different brain regions. FC networks as typically represented as graphs with brain regions of interest (ROIs) as nodes and functional correlation as edges.
Stein Acker   +9 more
doaj   +2 more sources

Line graphs of directed graphs I [PDF]

open access: yesTransactions on Combinatorics
We determine the forbidden induced subgraphs for the intersection of the classes of chordal bipartite graphs and line graphs of acyclic directed graphs. This is a first step towards finding the forbidden induced subgraphs for the class of line graphs of ...
Vaidyanathan Sivaraman, Daniel Slilaty
doaj   +4 more sources

Lower bounds for on-line graph colorings

open access: yesInternational Symposium on Algorithms and Computation, 2014
We propose two strategies for Presenter in on-line graph coloring games. The first one constructs bipartite graphs and forces any on-line coloring algorithm to use $2\log_2 n - 10$ colors, where $n$ is the number of vertices in the constructed graph ...
AA Diwan   +8 more
core   +2 more sources

LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations [PDF]

open access: yesAnnual Meeting of the Association for Computational Linguistics, 2021
This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task. Previous methods are typically node-centric and merely utilize different weight matrices to parameterize edge types, which 1) ignore the rich semantics
Ruisheng Cao   +5 more
semanticscholar   +1 more source

Line Graph Neural Networks for Link Weight Prediction [PDF]

open access: yesPhysica A: Statistical Mechanics and its Applications, 2023
In real-world networks, predicting the weight (strength) of links is as crucial as predicting the existence of the links themselves. Previous studies have primarily used shallow graph features for link weight prediction, limiting the prediction ...
Jinbi Liang, Cunlai Pu
semanticscholar   +1 more source

Line Graph Contrastive Learning for Link Prediction [PDF]

open access: yesPattern Recognition, 2022
Link prediction tasks focus on predicting possible future connections. Most existing researches measure the likelihood of links by different similarity scores on node pairs and predict links between nodes.
Zehua Zhang   +3 more
semanticscholar   +1 more source

Orbital design of flat bands in non-line-graph lattices via line-graph wave functions [PDF]

open access: yesPhysical review B, 2021
Line-graph (LG) lattices are known for having flat bands (FBs) from the destructive interference of Bloch wavefunctions encoded in pure lattice symmetry.
Hang Liu   +3 more
semanticscholar   +1 more source

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