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23 papers · ranked by Valyu relevance

01
PPubMed27 Sep 2023
A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder
Lizhen Shao, Cong Fu, Xunying Chen
Background Autism spectrum disorder (ASD) is a serious developmental disorder of the brain. Recently, various deep learning methods based on functional magnetic resonance imaging (fMRI) data have been developed for the classification of ASD. Among them, graph neural networks, which generalize deep neural network models…
97relevance
02
AarXiv12 Aug 2022
Multiplex Heterogeneous Graph Convolutional Network
Pengyang Yu, Chaofan Fu, Yanwei Yu, Chao Huang + 2 more
'Junyu Dong'] Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex network between multi-typed…
96relevance
03
AarXiv29 Dec 2020
Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning
Yu Le, Leilei Sun, Bowen Du, Chuanren Liu + 2 more
—Heterogeneous graphs are pervasive in practical scenarios, where each graph consists of multiple types of nodes and edges. Representation learning on heterogeneous graphs aims to obtain low-dimensional node representations that could preserve both node attributes and relation information. However, most of the existing…
92relevance
04
PPubMed13 Jan 2023
Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism
Wei Peng, Rong Wu, Wei Dai, Ning Yu
Background Correctly identifying the driver genes that promote cell growth can significantly assist drug design, cancer diagnosis and treatment. The recent large-scale cancer genomics projects have revealed multi-omics data from thousands of cancer patients, which requires to design effective models to unlock the…
89relevance
05
CchemRxiv18 Jun 2025
Inverse Design of Complex Nanoparticle Heterostructures via Deep Learning on Heterogeneous Graphs
Authors not listed
Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. We report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core-shell upconverting nanoparticles (UCNPs). UCNPs, which have…
88relevance
06
BbioRxiv11 Mar 2025
Encoding Drug-Target-Pathway-Disease profiles for Drug-Cancer Association Prediction using Graph Transformer-Convolution Networks
Cheng Yu Tung, Jinn Moon Yang
New drug development is costly, time-consuming, and has a low success rate, leading to a decline in drug discovery efficiency over time. Drug repurposing has emerged as an effective alternative, applying existing, safe drugs to new diseases, thereby reducing development time and costs by bypassing preclinical…
87relevance
07
AarXiv2023
Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
Mingguo He, Zhewei Wei, Shikun Feng, Zhengjie Huang + 3 more
'Yu Sun' 'Dianhai Yu'] Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-based methods to aggregate information, i.e., manually selected meta-paths or some heuristic modules, lacking…
86relevance
08
PPubMed24 Jun 2022
Graph convolutional networks fusing motif-structure information
Bin Wang, LvHang Cheng, JinFang Sheng, ZhengAng Hou + 1 more
With the advent of the wave of big data, the generation of more and more graph data brings great pressure to the traditional deep learning model. The birth of graph neural network fill the gap of deep learning in graph data. At present, graph convolutional networks (GCN) have surpassed traditional methods such as…
84relevance
09
AarXiv2021
Label-Wise Graph Convolutional Network for Heterophilic Graphs
Enyan Dai, Zhimeng Guo, Suhang Wang
Graph Neural Networks (GNNs) have achieved remarkable performance in modeling graphs for various applications. However, most existing GNNs assume the graphs exhibit strong homophily in node labels, i.e., nodes with similar labels are connected in the graphs. They fail to generalize to heterophilic graphs where linked…
81relevance
10
BbioRxiv17 May 2022
Topology Adaptive Graph Convolution Network With Heterogeneous Entities For Predicting Adverse Events from Drug-Drug-Interactions
Srijani Bagchi, Anasua Sarkar, Ujjwal Maulik
In times like this, it is imperative to be cautious about the effects of drugs or vaccination doses on patients who are already suffering from other serious diseases. It’s not only the virus which can affect the body metabolisms, drugs to encounter the virus may also end up having unwanted negative effects. Therapeutic…
81relevance
11
AarXiv1 Jun 2021
Heterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation
Ryuhei Hamaguchi, Yasutaka Furukawa, Masaki Onishi, Ken Sakurada
This paper proposes a novel heterogeneous grid convolution that builds a graph-based image representation by exploiting heterogeneity in the image content, enabling adaptive, efficient, and controllable computations in a convolutional architecture. More concretely, the approach builds a data-adaptive graph structure…
80relevance
12
PPubMed4 Feb 2023
Unsupervised Embedding Learning for Large-Scale Heterogeneous Networks Based on Metapath Graph Sampling
Hongwei Zhong, Mingyang Wang, Xinyue Zhang, Yixiang Fang + 2 more
'Yongpan Sheng' 'Hao Wang'] How to learn the embedding vectors of nodes in unsupervised large-scale heterogeneous networks is a key problem in heterogeneous network embedding research. This paper proposes an unsupervised embedding learning model, named LHGI (Large-scale Heterogeneous Graph Infomax). LHGI adopts the…
79relevance
13
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…
74relevance
14
PPubMed20 May 2025
Petri graph neural networks advance learning higher order multimodal complex interactions in graph structured data
Alma Ademovic Tahirovic, David Angeli, Adnan Tahirovic, Goran Strbac
Graphs are widely used to model interconnected systems, offering powerful tools for data representation and problem-solving. However, their reliance on pairwise, single-type, and static connections limits their expressive capacity. Recent developments extend this foundation through higher-order structures, such as…
71relevance
15
BbioRxiv16 Apr 2019
Structural Learning of Proteins Using Graph Convolutional Neural Networks
Rafael Zamora-Resendiz, Silvia Crivelli
The exponential growth of protein structure databases has motivated the development of efficient deep learning methods that perform structural analysis tasks at large scale, ranging from the classification of experimentally determined proteins to the quality assessment and ranking of computationally generated protein…
71relevance
16
CchemRxiv2 Apr 2024
Graph Neural Networks for CO2 Solubility Predictions in Deep Eutectic Solvents
Edgar Ivan Sanchez Medina, Gabriel Hernandez Morales, Arturo Jimenez-Gutierrez, Victor M. Zavala
Deep Eutectic Solvents (DESs) are a promising class of solvents for CO2 capture. DESs are complex mixtures that can be designed to optimize CO2 solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for…
70relevance
17
BbioRxiv16 Nov 2020
Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks
Mustafa Coşkun, Mehmet Koyutürk
Link prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug-disease associations, and drug response in cancer. The general principle in link prediction is to use the topological characteristics and the attributes–if…
67relevance
18
BbioRxiv3 Nov 2021
DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer
Anjun Ma, Xiaoying Wang, Cankun Wang, Jingxian Li + 15 more
We present DeepMAPS (Deep learning-based Multi-omics Analysis Platform for Single-cell data) for biological network inference from single-cell multi-omics (scMulti-omics). DeepMAPS includes both cells and genes in a heterogeneous graph to simultaneously infer cell-cell, cell-gene, and gene-gene relations. The…
67relevance
19
CchemRxiv8 Aug 2024
LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction
Zachary Humphreys, Xenophon Evangelopoulos, Stavros Gerolymatos, Edward O. Pyzer-Knapp + 1 more
Graph neural networks have recently met huge success in various inference tasks including materials property prediction amongst many others. Nevertheless, having an inherently locally-based representation capacity as they do, global representation of materials' structures can only only be achieved by expanding the…
67relevance
20
CchemRxiv18 May 2022
Multi-fidelity machine learning models for improved high-throughput screening predictions
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
65relevance
21
BbioRxiv27 Nov 2018
Two distributed semi-supervised algorithms for mining of complex networks using Giraph
Erfan Farhangi Maleki, Nasser Ghadiri, Maryam Lotfi Shahreza, Zeinab Maleki
Heterogeneous complex networks are large graphs consisting of different types of nodes and edges. The process of mining and knowledge extraction from these networks is so complicated. Moreover, the scale of these networks is steadily increasing. Thus, scalable methods are required. In this paper, two distributed label…
62relevance
22
CchemRxiv16 May 2022
Neural Scaling of Deep Chemical Models
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
60relevance
23
CchemRxiv6 Oct 2025
Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty
Authors not listed
Accurate prediction of redox potentials of iron (Fe) complexes, in tandem with uncertainty quantification, is essential to advance technologies related to electro-deposition and energy storage by enabling reliable modeling, guiding experimental design, and improving the efficiency of material discovery. Since…
56relevance

Results for heterogeneous graph convolution network

A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder

Multiplex Heterogeneous Graph Convolutional Network

Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning

Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism

Inverse Design of Complex Nanoparticle Heterostructures via Deep Learning on Heterogeneous Graphs

Encoding Drug-Target-Pathway-Disease profiles for Drug-Cancer Association Prediction using Graph Transformer-Convolution Networks

Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials

Graph convolutional networks fusing motif-structure information

Label-Wise Graph Convolutional Network for Heterophilic Graphs

Topology Adaptive Graph Convolution Network With Heterogeneous Entities For Predicting Adverse Events from Drug-Drug-Interactions

Heterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation

Unsupervised Embedding Learning for Large-Scale Heterogeneous Networks Based on Metapath Graph Sampling

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

Petri graph neural networks advance learning higher order multimodal complex interactions in graph structured data

Structural Learning of Proteins Using Graph Convolutional Neural Networks

Graph Neural Networks for CO2 Solubility Predictions in Deep Eutectic Solvents

Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks

DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

Multi-fidelity machine learning models for improved high-throughput screening predictions

Two distributed semi-supervised algorithms for mining of complex networks using Giraph

Neural Scaling of Deep Chemical Models

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty

A heterogeneous graph convolutional attention network method for classification of autism spectrum disorder

Multiplex Heterogeneous Graph Convolutional Network

Hybrid Micro/Macro Level Convolution for Heterogeneous Graph Learning

Identifying cancer driver genes based on multi-view heterogeneous graph convolutional network and self-attention mechanism

Inverse Design of Complex Nanoparticle Heterostructures via Deep Learning on Heterogeneous Graphs

Encoding Drug-Target-Pathway-Disease profiles for Drug-Cancer Association Prediction using Graph Transformer-Convolution Networks

Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials

Graph convolutional networks fusing motif-structure information

Label-Wise Graph Convolutional Network for Heterophilic Graphs

Topology Adaptive Graph Convolution Network With Heterogeneous Entities For Predicting Adverse Events from Drug-Drug-Interactions

Heterogeneous Grid Convolution for Adaptive, Efficient, and Controllable Computation

Unsupervised Embedding Learning for Large-Scale Heterogeneous Networks Based on Metapath Graph Sampling

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

Petri graph neural networks advance learning higher order multimodal complex interactions in graph structured data

Structural Learning of Proteins Using Graph Convolutional Neural Networks

Graph Neural Networks for CO2 Solubility Predictions in Deep Eutectic Solvents

Proximity Measures as Graph Convolution Matrices for Link Prediction in Biological Networks

DeepMAPS: Single-cell biological network inference using heterogeneous graph transformer

LiCOMPGNN - Lightweight Contextual Message Passing for Crystalline Materials Property Prediction

Multi-fidelity machine learning models for improved high-throughput screening predictions

Two distributed semi-supervised algorithms for mining of complex networks using Giraph

Neural Scaling of Deep Chemical Models

Automated Learning of GNN Ensembles for Predicting Redox Potentials with Uncertainty