Results 41 to 50 of about 2,936,543 (297)
A Dynamic Convolutional Network-Based Model for Knowledge Graph Completion
Information, 2022 Knowledge graph embedding can learn low-rank vector representations for knowledge graph entities and relations, and has been a main research topic for knowledge graph completion.
Haoliang Peng, Yue Wu
doaj +1 more source
Neighbor Contrastive Learning on Learnable Graph Augmentation [PDF]
AAAI Conference on Artificial Intelligence, 2023 Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. However, the existing GCL methods mostly adopt human-designed graph augmentations, which are sensitive to various graph ...
X. Shen +4 more
semanticscholar +1 more source
AAAI Conference on Artificial Intelligence, 2020 Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this problem challenging ...
Chao Song +3 more
semanticscholar +1 more source
HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer [PDF]
The Web Conference, 2023 Recent studies have highlighted the limitations of message-passing based graph neural networks (GNNs), e.g., limited model expressiveness, over-smoothing, over-squashing, etc.
Qiheng Mao +3 more
semanticscholar +1 more source
TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification [PDF]
The Web Conference, 2023 Encrypted traffic classification is receiving widespread attention from researchers and industrial companies. However, the existing methods only extract flow-level features, failing to handle short flows because of unreliable statistical properties, or ...
Haozhen Zhang +6 more
semanticscholar +1 more source
Bilinear Graph Neural Network with Neighbor Interactions
, 2020 Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. Existing efforts on GNN have largely defined the graph convolution as a weighted sum of the features of the connected nodes to form the ...
Feng, Fuli +6 more
core +1 more source
A Design Methodology for Space-Time Adapter [PDF]
, 2007 This paper presents a solution to efficiently explore the design space of communication adapters. In most digital signal processing (DSP) applications, the overall architecture of the system is significantly affected by communication architecture, so the
Chavet, Cyrille +3 more
core +2 more sources
Leverage Lexical Knowledge for Chinese Named Entity Recognition via Collaborative Graph Network
Conference on Empirical Methods in Natural Language Processing, 2019 The lack of word boundaries information has been seen as one of the main obstacles to develop a high performance Chinese named entity recognition (NER) system.
Dianbo Sui +4 more
semanticscholar +1 more source
Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud [PDF]
Computer Vision and Pattern Recognition, 2020 In this paper, we propose a graph neural network to detect objects from a LiDAR point cloud. Towards this end, we encode the point cloud efficiently in a fixed radius near-neighbors graph. We design a graph neural network, named Point-GNN, to predict the
Weijing Shi, R. Rajkumar
semanticscholar +1 more source
Entropy, 2022 Users participate in multiple social networks for different services. User identity linkage aims to predict whether users across different social networks refer to the same person, and it has received significant attention for downstream tasks such as ...
Hao Gao +4 more
doaj +1 more source