scholarly journals Some Results on More Flexible Versions of Graph Motif

2014 ◽  
Vol 56 (4) ◽  
pp. 612-629 ◽  
Author(s):  
Romeo Rizzi ◽  
Florian Sikora
Keyword(s):  
Graph Motif

scholarly journals Graph Motif Problems Parameterized by Dual

2020 ◽  
Vol 24 (3) ◽  
pp. 371-396
Author(s):  
Guillaume Fertin ◽  
Christian Komusiewicz
Keyword(s):  
Graph Motif

RANGI: A Fast List-Colored Graph Motif Finding Algorithm

2013 ◽  
Vol 10 (2) ◽  
pp. 504-513 ◽  
Author(s):  
Ali Gholami Rudi ◽  
Saeed Shahrivari ◽  
Saeed Jalili ◽  
Zahra Razaghi Moghadam Kashani
Keyword(s):  
Motif Finding ◽  
Colored Graph ◽  
Graph Motif ◽  
Motif Finding Algorithm

scholarly journals (E)-2-(3-Oxo-3-phenylprop-1-enyl)thiophene-3-carbaldehyde

IUCrData ◽  
2018 ◽  
Vol 3 (1) ◽  
Author(s):  
K. Elumalai ◽  
R. Raja ◽  
Jayachandran Karunakaran ◽  
Arasambattu K. Mohanakrishnan ◽  
K. Sakthi Murugesan
Keyword(s):  
Hydrogen Bonds ◽  
Dihedral Angle ◽  
Title Compound ◽  
Thiophene Ring ◽  
Asymmetric Unit ◽  
Envelope Conformation ◽  
Compound C ◽  
Independent Molecules ◽  
Graph Motif

The title compound, C14H10O2S, crystallizes with two independent molecules (A and B) in the asymmetric unit. They have very similar conformations with the thiophene ring having an envelope conformation in both molecules. In molecule A, the benzene and thiophene rings makes a dihedral angle of 11.01 (9)°. The corresponding angle in molecule B is 9.58 (9)°. In the crystal, molecules are linked via pairs of C—H...O hydrogen bonds, forming dimers with an R 2 2(18)set-graph motif. The dimers are linked via C—H...O hydrogen bonds, forming slabs lying parallel to (100).

On analyzing graphs with motif-paths

2021 ◽  
Vol 14 (6) ◽  
pp. 1111-1123
Author(s):  
Xiaodong Li ◽  
Reynold Cheng ◽  
Kevin Chen-Chuan Chang ◽  
Caihua Shan ◽  
Chenhao Ma ◽  
...  
Keyword(s):  
Link Prediction ◽  
Graph Clustering ◽  
Order Structure ◽  
Graph Analysis ◽  
High Order Structure ◽  
Node Ranking ◽  
Local Graph ◽  
Edge Based ◽  
Graph Motif ◽  
Better Than

Path-based solutions have been shown to be useful for various graph analysis tasks, such as link prediction and graph clustering. However, they are no longer adequate for handling complex and gigantic graphs. Recently, motif-based analysis has attracted a lot of attention. A motif, or a small graph with a few nodes, is often considered as a fundamental unit of a graph. Motif-based analysis captures high-order structure between nodes, and performs better than traditional "edge-based" solutions. In this paper, we study motif-path , which is conceptually a concatenation of one or more motif instances. We examine how motif-paths can be used in three path-based mining tasks, namely link prediction, local graph clustering and node ranking. We further address the situation when two graph nodes are not connected through a motif-path, and develop a novel defragmentation method to enhance it. Experimental results on real graph datasets demonstrate the use of motif-paths and defragmentation techniques improves graph analysis effectiveness.

On Structural Parameterizations of Graph Motif and Chromatic Number

2017 ◽  
pp. 118-129 ◽  
Author(s):  
Bireswar Das ◽  
Murali Krishna Enduri ◽  
Neeldhara Misra ◽  
I. Vinod Reddy
Keyword(s):  
Chromatic Number ◽  
Graph Motif

scholarly journals 2-Benzoyl-4-chloroaniline thiosemicarbazone

2014 ◽  
Vol 70 (6) ◽  
pp. o680-o680
Author(s):  
Katlen C. T. Bandeira ◽  
Leandro Bresolin ◽  
Ueslei Z. Lehmann ◽  
Priscilla J. Zambiazi ◽  
Adriano Bof de Oliveira
Keyword(s):  
Hydrogen Bonds ◽  
Dihedral Angle ◽  
Title Compound ◽  
Two Dimensional ◽  
Aromatic Rings ◽  
Compound C ◽  
Dimensional Network ◽  
Graph Motif

In the title compound, C14H13ClN4S, obtained from a reaction of 2-benzoyl-4-chloroaniline with thiosemicarbazide in ethanol, the dihedral angle between the aromatic rings is 81.31 (13)°. In the crystal, the molecules are linked by three N—H...S hydrogen bonds, forming centrosymmetric rings with set-graph motifR22(8) andR22(18), and resulting in the formation of a two-dimensional network lying parallel to (010).

A PARALLEL SCHEME FOR COMPARING TRANSCRIPTION FACTOR BINDING SITES MATRICES

2010 ◽  
Vol 08 (03) ◽  
pp. 485-502 ◽  
Author(s):  
SOLENNE CARAT ◽  
RÉMI HOULGATTE ◽  
JÉRÉMIE BOURDON
Keyword(s):  
Transcription Factor ◽  
Regulatory Networks ◽  
Motif Discovery ◽  
Specific Pattern ◽  
Data Sets ◽  
Original Graph ◽  
Motif Model ◽  
Parallel Scheme ◽  
Eukaryotic Organism ◽  
Graph Motif

Gene regulation implies many mechanisms. Their identification is a crucial task to construct regulatory networks, and is necessary to understand the pathology in many cases. This requires the identification of transcription factors that play a role in regulation. Numerous motif discovery tools are now available. Combining efficiently their results appears useful for comparing and clustering these motifs in order to reduce redundancies and to identify the corresponding transcription factor. We develop a method that produces, compares and clusters a set of motifs and identifies some close motifs in databases like JASPAR and the public version of Transfac. Unlike previous comparison methods, where each matrix column is compared independently, we have developed a global method to compare motifs that also helps to reduce the number of false positives. We also propose an original graph motif model that generalizes the classical position specific pattern matrices. Finally, we present an application of our method to study ChIP-chip data sets in the context of an eukaryotic organism.

scholarly journals A multiplex visibility graph motif‐based convolutional neural network for characterizing sleep stages using EEG signals

2020 ◽  
Vol 6 (4) ◽  
pp. 355-363
Author(s):  
Qing Cai ◽  
Jianpeng An ◽  
Zhongke Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sleep Stage ◽  
Network Motif ◽  
Visibility Graph ◽  
Sleep Stages ◽  
Dynamic Features ◽  
Eeg Signals ◽  
Graph Motif ◽  
Electroencephalogram Eeg

Sleep is an essential integrant in everyone’s daily life; therefore, it is an important but challenging problem to characterize sleep stages from electroencephalogram (EEG) signals. The network motif has been developed as a useful tool to investigate complex networks. In this study, we developed a multiplex visibility graph motif‐based convolutional neural network (CNN) for characterizing sleep stages using EEG signals and then introduced the multiplex motif entropy as the quantitative index to distinguish the six sleep stages. The independent samples t‐test shows that the multiplex motif entropy values have significant differences among the six sleep stages. Furthermore, we developed a CNN model and employed the multiplex motif sequence as the input of the model to classify the six sleep stages. Notably, the classification accuracy of the six‐state stage detection was 85.27%. Results demonstrated the effectiveness of the multiplex motif in characterizing the dynamic features underlying different sleep stages, whereby they further provide an essential strategy for future sleep‐stage detection research.

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