Multi-hop Attention Graph Neural Network ;Guangtao Wang,Zhitao Ying,Jing Huang,Jure Leskovec 这篇论文投稿了 ICLR2021,被拒了,Multi-hop Attention Graph Neural Network 后来中了 IJCAI 2021Multi-hop Attention Graph Neural Networks Motivation 现有的GAT等模型在一个layer中只能聚合邻居的信息,无法聚合更多的...
因此本文的作者提出了Multi-hop Attention Graph Neural Network (MAGNA),將多跳上下文資訊納入注意力計算的原則方法,使GNN的每一層都能進行遠端互動。為了計算非直接連線的節點之間的注意力,MAGNA將注意力分數分散到整個網路中,從而增加了GNN每一層的感受域。這樣,網路聚合的資訊增加,並且有注意力引數作為特徵選擇的...
目前GNNs通过利用self-attention机制已经取得较好的效果。但目前的注意力机制都只是考虑到相连的节点,却不能利用到能提供图结构上下文信息的多跳邻居(multi-hop neighbors)。因此提出 Direct multi-hop Attention based Graph neural Network (DAGN),在注意力机制中加入多跳信息,从邻居节点扩展到非邻居节点,增加每一层网...
Multi-hop connections between the graph nodes are modeled by using the Markov chain process. After performing multi-hop graph attention, MGA re-converts the graph into an updated feature map and transfers it to the next convolutional layer. We combined the MGA module...
To mitigate the issues mentioned above, this paper proposes anEdge-featuredMulti-hopAttention Graph Neural Network forIntrusionDetectionSystem (EMA-IDS), aiming to improve detection performance by capturing more features from data flows. Our method enhances computational efficiency through attention ...
Multi-hop attention graph neural network. Preprint at https://arxiv.org/abs/2009.14332 (2020). Lu, W. et al. TANKBind: trigonometry-aware neural networks for drug–protein binding structure prediction. Preprint at https://www.biorxiv.org/content/10.1101/2022.06.06.495043v1 (2022). Lewis, G...
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention arXiv 2023-03-28 Github Demo MultiInstruct: Improving Multi-Modal Zero-Shot Learning via Instruction Tuning arXiv 2022-12-21 - - Multimodal In-Context Learning TitleVenueDateCodeDemo MIMIC-IT: Multi-Modal In-Contex...
NA-KGR [36] addresses the issue of excessive noise introduced in inference tasks by stacking multiple layers of graph neural networks on a graph. It utilizes the weighted nature of graph attention to encode the neighboring entities most likely to impact inference through a two-stage graph ...
Attention-based graph neural networks are also widely used in traffic flow prediction. An example is the spatial–temporal graph attention network (AST-GAT), which uses multi-head graph attention to capture the spatial correlation between segments of a road traffic network [23]. In general, the...
The stacked GNN designs a simple graph attention network, which uses the attention mechanism to re-aggregate multi-hop information of all nodes. As shown in Figure 4, we assign different weights to neighbors of each node during secondary aggregation to distinguish their importance [30]. The ...