Results 31 to 40 of about 263,942 (258)
Structural Hierarchy-Enhanced Network Representation Learning
Applied Sciences, 2020 Network representation learning (NRL) is crucial in generating effective node features for downstream tasks, such as node classification (NC) and link prediction (LP).
Cheng-Te Li, Hong-Yu Lin
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Graph Sanitation with Application to Node Classification
Proceedings of the ACM Web Conference 2022, 2022 The past decades have witnessed the prosperity of graph mining, with a multitude of sophisticated models and algorithms designed for various mining tasks, such as ranking, classification, clustering and anomaly detection. Generally speaking, the vast majority of the existing works aim to answer the following question, that is, given a graph, what is ...
Zhe Xu 0007, Boxin Du, Hanghang Tong
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Sub-optimal Graph Matching by Node-to-Node Assignment Classification [PDF]
, 2019 In the recent years, Graph Edit Distance has awaken interest in the scientific community and some new graph-matching algorithms that compute it have been presented. Nevertheless, these algorithms usually cannot be used in real applications due to runtime restrictions.
Xavier Cortés +2 more
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FinFD-GCN: Using Graph Convolutional Networks for Fraud Detection in Financial Data [PDF]
Journal of Artificial Intelligence and Data MiningIn recent years, new technologies have brought new innovations into the financial and commercial world, giving fraudsters many ways to commit fraud and cost companies big time.
Mohamad Mahdi Yadegar, Hossein Rahmani
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Evolved explainable classifications for lymph node metastases [PDF]
Neural Networks, 2022 A novel evolutionary approach for Explainable Artificial Intelligence is presented: the "Evolved Explanations" model (EvEx). This methodology consists in combining Local Interpretable Model Agnostic Explanations (LIME) with Multi-Objective Genetic Algorithms to allow for automated segmentation parameter tuning in image classification tasks.
Iam Palatnik de Sousa +2 more
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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
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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
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Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree
Applied Sciences, 2022 Graph convolutional networks (GCNs) have been successfully applied to learning tasks on graph-structured data. However, most traditional GCNs based on graph convolutions assume homophily in graphs, which leads to a poor performance when dealing with ...
Rui Zhang, Xin Li
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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
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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
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