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Results for node classification without features

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9 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
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
04
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
05
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
06
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
07
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
08
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
09
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

Accurately modeling biased random walks on weighted networks using node2vec+

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

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

Accurately modeling biased random walks on weighted networks using node2vec+

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

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

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

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

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

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

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

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+