Results 41 to 50 of about 441,353 (265)

Spectrum-based deep neural networks for fraud detection

, 2017
In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as
Li, Jun, Lu, Aidong, Wu, Xintao, Yuan, Shuhan   +3 more
core   +1 more source

Graph-based Neural Multi-Document Summarization

, 2017
We propose a neural multi-document summarization (MDS) system that incorporates sentence relation graphs. We employ a Graph Convolutional Network (GCN) on the relation graphs, with sentence embeddings obtained from Recurrent Neural Networks as input node
Meelu, Kshitijh, Pareek, Ayush, Radev, Dragomir, Srinivasan, Krishnan, Yasunaga, Michihiro, Zhang, Rui   +5 more
core   +1 more source

Cascade2vec: Learning Dynamic Cascade Representation by Recurrent Graph Neural Networks

IEEE Access, 2019
An information dissemination network (i.e., a cascade) with a dynamic graph structure is formed when a novel idea or message spreads from person to person.
Zhenhua Huang, Zhenyu Wang, Rui Zhang
doaj   +1 more source

Graph Neural Network for Traffic Flow Situation Prediction

Jisuanji kexue yu tansuo, 2021
Road network structure integrated traffic flow situation prediction is a highly nonlinear and complexly spatial-temporal dynamic correlation time-series data prediction problem. However, traditional traffic flow situation forecasting methods cannot model
JIANG Shan, DING Zhiming, XU Xinrun, YAN Jin
doaj   +1 more source

On Sampling Strategies for Neural Network-based Collaborative Filtering

, 2017
Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text.
Bordes Antoine, Chen Tianqi, Chen Ting, Collobert Ronan, den Oord Aaron Van, Gopalan Prem K, Kingma Diederik, Krizhevsky Alex, Lin Xiao, Mikolov T, Reddi Sashank J, Rendle Steffen, Salakhutdinov Ruslan, Zhang Xiang, Zhao Peilin, Zheng Yin   +15 more
core   +1 more source

Genetic attenuation of ALDH1A1 increases metastatic potential and aggressiveness in colorectal cancer

Molecular Oncology, EarlyView.
Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova, Zuzana Kozovska, Ondrej Pos, Kristina Pavlov, Sachin Gulati, Peter Makovicky, Kristina Jakic, Monika Burikova, Eva Sedlackova, Barbora Svitkova, Silvia Tyciakova, Vojtech Bystry, Nicolas Blavet, Boris Tichy, Matej Hrnciar, Jaroslav Budis, Miroslav Tomas, Peter Dubovan, Georgina Kolnikova, Veronika Repaska, Nikoleta Mojzesova, Eva Zomborska, Daniel Pindak, Michal Mego, Tomas Szemes, Miroslava Matuskova   +25 more
wiley   +1 more source

Research on few-shot text classification techniques based on text-level-graph neural networks [PDF]

Journal of Hebei University of Science and Technology
In order to solve the problem of poor accuracy of text classification in text graph neural network with small samples, a text level graph neural network-prototypical (LGNN-Proto) was designed.
Xiangcheng AN, Baozhu LIU, Jingwei GAN
doaj   +1 more source

Deep neural networks on graph signals for brain imaging analysis

, 2017
Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal data often degraded by complex, non-Gaussian noise. For reliable analysis of brain imaging data, it is important to extract discriminative, low-dimensional intrinsic representation
Cheung, Ngai-Man, Guo, Yiluan, Nejati, Hossein   +2 more
core   +1 more source

Torsion Graph Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric deep learning (GDL) models have demonstrated a great potential for the analysis of non-Euclidian data. They are developed to incorporate the geometric and topological information of non-Euclidian data into the end-to-end deep learning architectures. Motivated by the recent success of discrete Ricci curvature in graph neural network (GNNs), we
Cong Shen, Xiang Liu, Jiawei Luo, Kelin Xia   +3 more
openaire   +3 more sources

RIPK4 function interferes with melanoma cell adhesion and metastasis

Molecular Oncology, EarlyView.
RIPK4 promotes melanoma growth and spread. RIPK4 levels increase as skin lesions progress to melanoma. CRISPR/Cas9‐mediated deletion of RIPK4 causes melanoma cells to form less compact spheroids, reduces their migratory and invasive abilities and limits tumour growth and dissemination in mouse models.
Norbert Wronski, Sławomir Lasota, Ewelina Madej, Anna A. Brożyna, Małgorzata Szczygieł, Agnieszka Harazin‐Lechowska, Jan Czerbniak, Janusz Rys, Jaroslaw Czyz, Agnieszka Wolnicka‐Glubisz   +9 more
wiley   +1 more source