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Results for node classification without features

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5 papers · ranked by Valyu relevance

01
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…
66relevance
02
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…
60relevance
03
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
04
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
05
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

Accurately modeling biased random walks on weighted networks using node2vec+

Identification of Neuronal Polarity by Node-Based Machine Learning

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

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+

Identification of Neuronal Polarity by Node-Based Machine Learning

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

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

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

Identification of Neuronal Polarity by Node-Based Machine Learning

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

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+*

Identification of Neuronal Polarity by Node-Based Machine Learning

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

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+