Results 1 to 10 of about 4,649,194 (210)
TransLiG: a de novo transcriptome assembler that uses line graph iteration. [PDF]
Genome 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 +4 more sources
Graphs whose line graphs are ring graphs [PDF]
AKCE 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
Projective dimension and regularity of the path ideal of the line graph [PDF]
, 2016 By generalizing the notion of the path ideal of a graph, we study some algebraic properties of some path ideals associated to a line graph. We show that the quotient ring of these ideals are always sequentially Cohen–Macaulay and also provide some exact ...
Guangjun Zhu
openalex +3 more sources
Modeling functional connectivity changes during an auditory language task using line graph neural networks [PDF]
Frontiers in Computational NeuroscienceFunctional 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
International Journal of Education in Mathematics Science and Technology, 2020 This study investigated the graphing skills and some affective states of middle school students about graphs by their gender, grade level, and the common graph types used in science courses.
Murat Bursal, Fuat Polat
openalex +3 more sources
Atomistic Line Graph Neural Network for improved materials property predictions [PDF]
npj 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 +1 more source
LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations [PDF]
Annual 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]
Physica 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]
Pattern 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]
Physical 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