Results 41 to 50 of about 748,129 (177)
Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning
Mathematics, 2023 Relational learning has attracted much attention from the machine learning community in recent years, and many real-world applications have been successfully formulated as relational learning problems.
Pedro Almagro-Blanco +2 more
doaj +1 more source
Particle Propagation Model for Dynamic Node Classification
IEEE Access, 2020 With the popularity of online social networks, researches on dynamic node classification have received further attention. Dynamic node classification also helps the rapid popularization of online social networks.
Wenzheng Li +4 more
doaj +1 more source
A Regularized Graph Neural Network Based on Approximate Fractional Order Gradients
Mathematics, 2022 Graph representation learning is a significant challenge in graph signal processing (GSP). The flourishing development of graph neural networks (GNNs) provides effective representations for GSP.
Zijian Liu +3 more
doaj +1 more source
IEEE AccessIn this paper, we address the problem of improving time-series classification performance in graph environments. With the recent increase in graph analytics, many studies analyzing time-series within the graph domain have been introduced.
Sanghun Lee +2 more
doaj +1 more source
Targeted Discrepancy Attacks: Crafting Selective Adversarial Examples in Graph Neural Networks
IEEE AccessIn this study, we present a novel approach to adversarial attacks for graph neural networks (GNNs), specifically addressing the unique challenges posed by graphical data.
Hyun Kwon, Jang-Woon Baek
doaj +1 more source
Node Classification of Network Threats Leveraging Graph-Based Characterizations Using Memgraph
ComputersThis research leverages Memgraph, an open-source graph database, to analyze graph-based network data and apply Graph Neural Networks (GNNs) for a detailed classification of cyberattack tactics categorized by the MITRE ATT&CK framework.
Sadaf Charkhabi +4 more
doaj +1 more source
Node Embedding over Temporal Graphs
, 2019 In this work, we present a method for node embedding in temporal graphs. We propose an algorithm that learns the evolution of a temporal graph's nodes and edges over time and incorporates this dynamics in a temporal node embedding framework for different
Guy, Ido, Radinsky, Kira, Singer, Uriel
core +1 more source
IEEE Access, 2023 Various graph neural networks (GNNs) have been proposed to solve node classification tasks in machine learning for graph data. GNNs use the structural information of graph data by aggregating the feature vectors of neighboring nodes.
Yuga Oishi, Ken Kaneiwa
doaj +1 more source
Mixture of Experts for Node Classification
Proceedings of the 2025 International Conference on Multimedia RetrievalNodes in the real-world graphs exhibit diverse patterns in numerous aspects, such as degree and homophily. However, most existent node predictors fail to capture a wide range of node patterns or to make predictions based on distinct node patterns, resulting in unsatisfactory classification performance.
Yu Shi +5 more
openaire +2 more sources
Graph Convolutional Network Design for Node Classification Accuracy Improvement
Mathematics, 2023 Graph convolutional networks (GCNs) provide an advantage in node classification tasks for graph-related data structures. In this paper, we propose a GCN model for enhancing the performance of node classification tasks.
Mohammad Abrar Shakil Sejan +5 more
doaj +1 more source