4.8 Article

Hierarchical Prototype Networks for Continual Graph Representation Learning

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

Graph Embedding Using Frequency Filtering

Hoda Bahonar et al.

Summary: The method uses Frequency Filtering Embedding and pseudo-Fourier operators to adapt to the properties of each graph dataset, improve classification accuracy, and completely resolves the cospectrality problem in tested datasets.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

Article Computer Science, Artificial Intelligence

Topology-Aware Graph Pooling Networks

Hongyang Gao et al.

Summary: This work introduces a topology-aware pooling (TAP) layer that explicitly considers graph topology for more accurate node selection. The TAP layer incorporates both local and global voting processes to generate ranking scores for each node, resulting in improved performance in graph classification tasks compared to previous methods.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

Article Computer Science, Artificial Intelligence

Large Graph Clustering With Simultaneous Spectral Embedding and Discretization

Zhen Wang et al.

Summary: This paper proposes a new method for graph clustering to solve challenging problems in spectral clustering methods by simultaneously performing spectral embedding and spectral rotation. By deriving a low-dimensional representation matrix from a graph using label propagation, a double-stochastic and positive semidefinite similarity matrix can be reconstructed, accelerating the algorithm. Experimental results demonstrate excellent performance of the method in terms of time cost and accuracy.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

Article Computer Science, Artificial Intelligence

Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds

Huan Lei et al.

Summary: A spherical kernel is proposed for efficient graph convolution of 3D point clouds, systematically quantizing the local 3D space to identify geometric relationships and applying to graph neural networks for point cloud classification and semantic segmentation.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

Proceedings Paper Computer Science, Information Systems

Streaming Graph Neural Networks

Yao Ma et al.

PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20) (2020)

Article Information Science & Library Science

Microsoft Academic Graph: When experts are not enough

Kuansan Wang et al.

QUANTITATIVE SCIENCE STUDIES (2020)

Article Computer Science, Artificial Intelligence

Graph Edge Convolutional Neural Networks for Skeleton-Based Action Recognition

Xikun Zhang et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Proceedings Paper Computer Science, Artificial Intelligence

DeepGCNs: Can GCNs Go as Deep as CNNs?

Guohao Li et al.

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019) (2019)

Proceedings Paper Computer Science, Artificial Intelligence

Large Scale Incremental Learning

Yue Wu et al.

2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019) (2019)

Proceedings Paper Computer Science, Theory & Methods

Meta-GNN: On Few-shot Node Classification in Graph Meta-learning

Fan Zhou et al.

PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT (CIKM '19) (2019)

Article Computer Science, Artificial Intelligence

Learning without Forgetting

Zhizhong Li et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2018)

Proceedings Paper Computer Science, Artificial Intelligence

NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks

Wenchao Yu et al.

KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2018)

Article Multidisciplinary Sciences

Overcoming catastrophic forgetting in neural networks

James Kirkpatricka et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2017)

Article Behavioral Sciences

Brain activity in using heuristic prototype to solve insightful problems

Tong Dandan et al.

BEHAVIOURAL BRAIN RESEARCH (2013)

Article Computer Science, Artificial Intelligence

Collective Classification in Network Data

Prithviraj Sen et al.

AI MAGAZINE (2008)

Article Neurosciences

Dissociable Prototype Learning Systems: Evidence from Brain Imaging and Behavior

Dagmar Zeithamova et al.

JOURNAL OF NEUROSCIENCE (2008)

Article Computer Science, Information Systems

The link-prediction problem for social networks

David Liben-Nowell et al.

JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY (2007)