Robuta

02/20/23 - Air quality prediction is a typical spatio-temporal modeling problem, which always uses different components to handle spatial and...

Abstract page for arXiv paper 2209.09145: IGraph/M: graph theory and network analysis for Mathematica

07/27/22 - Most sequential recommendation (SR) systems employing graph neural networks (GNNs) only model a user's interaction sequence as a f...

By exploiting the graph attention network, the authors of this study learn robust node representations of graphs in hyperbolic spaces, utilizing the gyrovector...

Recent studies have demonstrated that following learning tasks, changes in the resting state activity of the brain shape regional connections in functionally...

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science,...

In this article, we describe training and validation of a machine learning model for the prediction of organic compound normal boiling points.

The task of human action recognition (HAR) based on skeleton data is a challenging yet crucial technique owing to its wide-ranging applications in numerous...

Sep 4, 2025 - MLCommons announces new RGAT benchmark to MLPerf Inference v5.0 - addresses performance tests for graph-structured data and applications.

A visualization of interconnected XMPP domains

So far, numerous learned models have been pressed to use in microwave imaging problems. These models however, are oblivious to the imaging geometry. It has alwa

In 2020, the world is facing a Covid-19 virus pandemic. The fields of epidemiology and networks are needed in dealing with its spread. Individual (contact)...

RAGC: Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification (ICCVW 2019) - imatge-upc/ragc

09/03/19 - Colorectal cancer (CRC) grading is typically carried out by assessing the degree of gland formation within histology images. To do...

Bibliographic details on AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query.

Accurate electricity consumption forecasting is essential for power scheduling.

Fairness in graph neural networks has been actively studied recently. However, existing works often do not explicitly consider the role of message passing in...

Graph convolution networks (GCNs) have been widely used in the field of skeleton-based human action recognition. However, it is still difficult to improve re...

Many existing recommender systems (RSs) assume user behavior is governed solely by their interests. However, the peer effect often influences individual...

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science,...

Abstract page for arXiv paper 2504.03953v1: TGraphX: Tensor-Aware Graph Neural Network for Multi-Dimensional Feature Learning

Sequential recommendation uses contrastive learning to randomly augment user sequences and alleviate the data sparsity problem. However, there is no guarantee...

Rapid fire progression, such as flashover, has been one of the leading causes for firefighter deaths and injuries in residential building environments.

Quan Tu, Chongyang Tao, Rui Yan. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation...

Nov 24, 2025 - Imagen Network introduces a breakthrough personalization engine that models user behavior on-chain to deliver adaptive, creator-driven digital experiences....

Rating platforms provide users with useful information on products or other users. However, fake ratings are sometimes generated by fraudulent users.

Learn how Imec - a leading nano-electronics research institute - uses the Graph functionality of the Elastic Stack and MATLAB to perform manufacturing supply...

For skeleton-based action recognition, graph convolutional networks (GCN) have absolute advantages. Existing state-of-the-art (SOTA) methods tended to focus on...

04/07/23 - With the development of the Internet of Things (IoT), network intrusion detection is becoming more complex and extensive. It is es...

The proposed interactive segmentation method based on the GCN can accurately segment the prostate from MR images. Our method has a variety of applications in...

09/08/18 - Graph Neural Networks (GNN) are a promising technique for bridging differential programming and combinatorial domains. GNNs employ...

We propose a multimodal graph convolutional network to integrate functional and structural connectivity to predict multidimensional phenotypic...

Many graphs or networks are heterogeneous by nature, involving various vertex types and relation types. Most graph learning models for heterogeneous graphs...

We propose a novel approach to learn relational policies for classical planning based on learning to rank actions. We introduce a new graph representation that...