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17 papers · ranked by Valyu relevance

01
PPubMed9 Jun 2023
Path-enhanced graph convolutional networks for node classification without features
Qingju Jiao, Peige Zhao, Hanjin Zhang, Yahong Han + 2 more
'Toqir Rana'] Most current graph neural networks (GNNs) are designed from the view of methodology and rarely consider the inherent characters of graph. Although the inherent characters may impact the performance of GNNs, very few methods are proposed to resolve the issue. In this work, we mainly focus on improving the…
87relevance
02
PPubMed30 Apr 2024
A simple and effective convolutional operator for node classification without features by graph convolutional networks
Qingju Jiao, Han Zhang, Jingwen Wu, Nan Wang + 3 more
'Yongge Liu' 'Xiao Luo'] Graph neural networks (GNNs), with their ability to incorporate node features into graph learning, have achieved impressive performance in many graph analysis tasks. However, current GNNs including the popular graph convolutional network (GCN) cannot obtain competitive results on the graphs…
87relevance
03
AarXiv2021
Node Classification Meets Link Prediction on Knowledge Graphs
Ralph Abboud, İsmail İlkan Ceylan
Node classification and link prediction are widely studied in graph representation learning. While both transductive node classification and link prediction operate over a single input graph, they have so far been studied separately. Node classification models take an input graph with node features and incomplete node…
75relevance
04
AarXiv15 Oct 2022
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Hejie Cui, Zijie Lu, Li Pan, Carl Yang
Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where the superior performance is mainly established when natural node features are available. However, it is not well understood how GNNs work without natural node features…
72relevance
05
AarXiv2017
An experimental study of graph-based semi-supervised classification with additional node information
Bertrand Lebichot, Marco Saerens
The volume of data generated by internet and social networks is increasing every day, and there is a clear need for efficient ways of extracting useful information from them. As those data can take different forms, it is important to use all the available data representations for prediction. In this paper, we focus our…
68relevance
06
AarXiv16 May 2020
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models
Benedek Rózemberczki, Rik Sarkar
In this paper, we propose a flexible notion of characteristic functions defined on graph vertices to describe the distribution of vertex features at multiple scales. We introduce FEATHER, a computationally efficient algorithm to calculate a specific variant of these characteristic functions where the probability…
64relevance
07
AarXiv13 Jan 2021
Quantifying the Alignment of Graph and Features in Deep Learning
Yifan Qian, Paul Expert, Tom Rieu, Pietro Panzarasa + 1 more
'Mauricio Barahona'] We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances…
64relevance
08
AarXiv9 Nov 2024
Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction
Masoud Kargar, Nasim Jelodari, Alireza Assadzadeh
graphs and graph convolutional networks for high-level feature extraction Authors: ['Masoud Kargar' 'Nasim Jelodari' 'Alireza Assadzadeh'] Graphs, comprising nodes and edges, visually depict relationships and structures, posing challenges in extracting high-level features due to their intricate connections Multiple…
63relevance
09
BbioRxiv15 Aug 2022
Accurately modeling biased random walks on weighted networks using *node2vec+*
Renming Liu, Matthew Hirn, Arjun Krishnan
Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased random walks. However, while many networks, including functional gene interaction…
63relevance
10
CchemRxiv19 Dec 2023
Graphormer-IR: Graph Transformers Can Predict Experimental IR Spectra Using Highly Specialized Attention
Cailum Stienstra, Liam Hebert, Patrick Thomas, Alexander Haack + 2 more
Given that Infrared (IR) spectroscopy is a crucial tool in various chemical and forensic domains, improved in silico methods for predicting experimental spectra are needed due to the time and accuracy limitations of ab initio methods. We employ Graphormer, a graph neural network (GNN) transformer, to predict IR spectra…
62relevance
11
PPubMed30 Jun 2022
Scalably Using Node Attributes and Graph Structure for Node Classification †
Arpit Merchant, Ananth Mahadevan, Michael Mathioudakis, Adam Lipowski
'Adam Lipowski'] The task of node classification concerns a network where nodes are associated with labels, but labels are known only for some of the nodes. The task consists of inferring the unknown labels given the known node labels, the structure of the network, and other known node attributes. Common node…
61relevance
12
BbioRxiv3 Nov 2022
The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study
Guangyu Wang, Limei Zhang, Lishan Qiao
Brain functional network (BFN) analysis has become a popular technique for identifying neurological/mental diseases. Due to the fact that BFN is a graph, graph convolutional network (GCN) can be naturally used in the classification of BFN. Different from traditional methods that directly use the adjacency matrices of…
59relevance
13
PPubMed24 Jul 2019
Improving IoT Predictions through the Identification of Graphical Features
Syeda Akter, Lawrence Holder
IoT sensor networks have an inherent graph structure that can be used to extract graphical features for improving performance in a variety of prediction tasks. We propose a framework that represents IoT sensor network data as a graph, extracts graphical features, and applies feature selection methods to identify the…
57relevance
14
AarXiv4 Apr 2025
A Hybrid Similarity-Aware Graph Neural Network with Transformer for Node Classification
Aman Singh, S. Dar, Ranveer Singh, Nagendra Kumar
Node classification has gained significant importance in graph deep learning with real-world applications such as recommendation systems, drug discovery, and citation networks. Graph Convolutional Networks and Graph Transformers have achieved superior performance in node classification tasks. However, the key concern…
57relevance
15
BbioRxiv20 Jun 2020
Identification of Neuronal Polarity by Node-Based Machine Learning
Chen-Zhi Su, Kuan-Ting Chou, Hsuan-Pei Huang, Chung-Chuan Lo + 1 more
Identifying the directions of signal flows in neural networks is one of the most important stages for understanding the intricate information dynamics of a living brain. Using a dataset of 213 projection neurons distributed in different regions of a Drosophila brain, we develop a powerful machine learning algorithm…
56relevance
16
BbioRxiv17 Jun 2024
Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking
Panagiotis Mandros, Ian Gallagher, Viola Fanfani, Chen Chen + 6 more
Advances in computational biology now enable the inference of increasingly accurate, genome-wide molecular interaction networks from multi-omic data collected across large cohorts. Comparing such networks across distinct biological states can identify biologically informative and potentially actionable higher-order…
55relevance
17
BbioRxiv15 Sep 2024
Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks
Lida Kanari, Stanislav Schmidt, Francesco Casalegno, Emilie Delattre + 7 more
The shape of neuronal morphologies plays a critical role in determining their dynamical properties and the functionality of the brain. With an abundance of neuronal morphology reconstructions, a robust definition of cell types is important to understand their role in brain functionality. However, an objective…
53relevance

Results for node classification without features

Path-enhanced graph convolutional networks for node classification without features

A simple and effective convolutional operator for node classification without features by graph convolutional networks

Node Classification Meets Link Prediction on Knowledge Graphs

On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs

An experimental study of graph-based semi-supervised classification with additional node information

Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models

Quantifying the Alignment of Graph and Features in Deep Learning

Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction

Accurately modeling biased random walks on weighted networks using node2vec+

Graphormer-IR: Graph Transformers Can Predict Experimental IR Spectra Using Highly Specialized Attention

Scalably Using Node Attributes and Graph Structure for Node Classification †

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

Improving IoT Predictions through the Identification of Graphical Features

A Hybrid Similarity-Aware Graph Neural Network with Transformer for Node Classification

Identification of Neuronal Polarity by Node-Based Machine Learning

Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

Path-enhanced graph convolutional networks for node classification without features

A simple and effective convolutional operator for node classification without features by graph convolutional networks

Node Classification Meets Link Prediction on Knowledge Graphs

On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs

An experimental study of graph-based semi-supervised classification with additional node information

Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models

Quantifying the Alignment of Graph and Features in Deep Learning

Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction

Accurately modeling biased random walks on weighted networks using node2vec+

Graphormer-IR: Graph Transformers Can Predict Experimental IR Spectra Using Highly Specialized Attention

Scalably Using Node Attributes and Graph Structure for Node Classification †

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

Improving IoT Predictions through the Identification of Graphical Features

A Hybrid Similarity-Aware Graph Neural Network with Transformer for Node Classification

Identification of Neuronal Polarity by Node-Based Machine Learning

Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

Results for node classification without features

Path-enhanced graph convolutional networks for node classification without features

A simple and effective convolutional operator for node classification without features by graph convolutional networks

Node Classification Meets Link Prediction on Knowledge Graphs

On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs

An experimental study of graph-based semi-supervised classification with additional node information

Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models

Quantifying the Alignment of Graph and Features in Deep Learning

Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction

Accurately modeling biased random walks on weighted networks using *node2vec+*

Graphormer-IR: Graph Transformers Can Predict Experimental IR Spectra Using Highly Specialized Attention

Scalably Using Node Attributes and Graph Structure for Node Classification †

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

Improving IoT Predictions through the Identification of Graphical Features

A Hybrid Similarity-Aware Graph Neural Network with Transformer for Node Classification

Identification of Neuronal Polarity by Node-Based Machine Learning

Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

Path-enhanced graph convolutional networks for node classification without features

A simple and effective convolutional operator for node classification without features by graph convolutional networks

Node Classification Meets Link Prediction on Knowledge Graphs

On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs

An experimental study of graph-based semi-supervised classification with additional node information

Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric Models

Quantifying the Alignment of Graph and Features in Deep Learning

Multistage non-deterministic classification using secondary concept graphs and graph convolutional networks for high-level feature extraction

Accurately modeling biased random walks on weighted networks using *node2vec+*

Graphormer-IR: Graph Transformers Can Predict Experimental IR Spectra Using Highly Specialized Attention

Scalably Using Node Attributes and Graph Structure for Node Classification †

The Effect of Node Features on GCN-based Brain Network Classification: An Empirical Study

Improving IoT Predictions through the Identification of Graphical Features

A Hybrid Similarity-Aware Graph Neural Network with Transformer for Node Classification

Identification of Neuronal Polarity by Node-Based Machine Learning

Large Scale and Stable Graph Differential Analysis via Multi-Layer Node Embeddings and Ranking

Deep learning for classifying neuronal morphologies: combining topological data analysis and graph neural networks

Accurately modeling biased random walks on weighted networks using node2vec+

Accurately modeling biased random walks on weighted networks using node2vec+