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Results for Graph Classification

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12 papers · ranked by Valyu relevance

01
PPubMed10 Mar 2023
A deep graph convolutional neural network architecture for graph classification A deep graph convolutional neural network architecture for graph classification
Yuchen Zhou, Hongtao Huo, Zhiwen Hou, Fanliang Bu + 1 more
Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to 4 layers, which greatly limits the ability of GCN models to…
90relevance
02
PPubMed4 Feb 2021
Survey on graph embeddings and their applications to machine learning problems on graphs
Ilya Makarov, Dmitrii Kiselev, Nikita Nikitinsky, Lovro Subelj + 1 more
Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of…
82relevance
03
PPubMed9 Mar 2023
A Robust Automated Analog Circuits Classification Involving a Graph Neural Network and a Novel Data Augmentation Strategy
Ali Deeb, Abdalrahman Ibrahim, Mohamed Salem, Joachim Pichler + 6 more
'Sergii Tkachov' 'Anjeza Karaj' 'Fadi Al Machot' 'Kyamakya Kyandoghere' 'Marco Lanuzza' 'Lionel Trojman'] Analog mixed-signal (AMS) verification is one of the essential tasks in the development process of modern systems-on-chip (SoC). Most parts of the AMS verification flow are already automated, except for stimuli…
80relevance
04
CchemRxiv8 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Cory M. Simon, Xiaoli Fern
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are valuable as pollinators. Thus, candidate pesticides in development pipelines must be assessed for toxicity to bees. Leveraging a data set of 382 molecules with toxicity labels from…
79relevance
05
CchemRxiv10 Mar 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
78relevance
06
PPubMed22 Aug 2023
Learning and reasoning with graph data
Manfred Jaeger
Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields…
77relevance
07
PPubMed14 Oct 2011
Graph Constrained Discriminant Analysis: A New Method for the Integration of a Graph into a Classification Process Graph Integration for Microarray Data Analysis
Vincent Guillemot, Arthur Tenenhaus, Laurent Le Brusquet, Vincent Frouin + 1 more
'Vincent Frouin' 'Arkady B. Khodursky'] Integrating gene regulatory networks (GRNs) into the classification process of DNA microarrays is an important issue in bioinformatics, both because this information has a true biological interest and because it helps in the interpretation of the final classifier. We present a…
77relevance
08
CchemRxiv5 Apr 2022
Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…
77relevance
09
PPubMed8 Sep 2022
Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier
Serkan Ucer, Tansel Ozyer, Reda Alhajj
We propose a new type of supervised visual machine learning classifier, GSNAc, based on graph theory and social network analysis techniques. In a previous study, we employed social network analysis techniques and introduced a novel classification model (called Social Network Analysis-based Classifier-SNAc) which…
76relevance
10
PPubMed9 Apr 2021
An analysis of the graph processing landscape
Miguel E. Coimbra, Alexandre P. Francisco, Luís Veiga
The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. For the use-case of performing global computations over a graph, it is first ingested into a graph processing system from one…
69relevance
11
CchemRxiv15 Dec 2025
graphpancake: A Python package for representing organic molecules as molecular graphs utilizing electronic structure theory
Authors not listed
Computational methods for predictive modeling have been increasingly utilized in the early stages of drug discovery to supplement high-throughput screening. The advent of highly efficient and complex machine learning architectures necessitates new methods of collating the plethora of topological, geometrical, and…
67relevance
12
CchemRxiv18 Jul 2022
Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic
Alexander Smith, Spencer Runde, Alex Chew, Atharva Kelkar + 3 more
Molecular dynamics (MD) simulations are used in diverse scientific and engineering fields such as drug discovery, materials design, separations, biological systems, and reaction engineering. These simulations generate highly complex datasets that capture the 3D spatial positions, dynamics, and interactions of thousands…
65relevance

Results for Graph Classification

A deep graph convolutional neural network architecture for graph classification A deep graph convolutional neural network architecture for graph classification

Survey on graph embeddings and their applications to machine learning problems on graphs

A Robust Automated Analog Circuits Classification Involving a Graph Neural Network and a Novel Data Augmentation Strategy

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Learning and reasoning with graph data

Graph Constrained Discriminant Analysis: A New Method for the Integration of a Graph into a Classification Process Graph Integration for Microarray Data Analysis

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier

An analysis of the graph processing landscape

graphpancake: A Python package for representing organic molecules as molecular graphs utilizing electronic structure theory

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic

A deep graph convolutional neural network architecture for graph classification A deep graph convolutional neural network architecture for graph classification

Survey on graph embeddings and their applications to machine learning problems on graphs

A Robust Automated Analog Circuits Classification Involving a Graph Neural Network and a Novel Data Augmentation Strategy

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Learning and reasoning with graph data

Graph Constrained Discriminant Analysis: A New Method for the Integration of a Graph into a Classification Process Graph Integration for Microarray Data Analysis

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Explainable artificial intelligence through graph theory by generalized social network analysis-based classifier

An analysis of the graph processing landscape

graphpancake: A Python package for representing organic molecules as molecular graphs utilizing electronic structure theory

Topological Analysis of Molecular Dynamics Simulations using the Euler Characteristic