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AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [article]

Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, Jian Pei
2020 pre-print
We tackle the challenge and propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN).  ...  Graph Convolutional Networks (GCNs) have gained great popularity in tackling various analytics tasks on graph and network data.  ...  This paper tackles the challenge and proposes an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN).  ... 
doi:10.1145/3394486.3403177 arXiv:2007.02265v1 fatcat:2k5r6qugzncwjeoq2drh2qgrpy

All-Cause Death Prediction Method for CHD Based on Graph Convolutional Networks

Yutao Xue, Kaizhi Chen, Huizhong Lin, Shangping Zhong, Thippa Reddy G
2022 Computational Intelligence and Neuroscience  
We used an adaptive multi-channel graph convolutional neural network (AM-GCN) model to extract graph embeddings from topology, node features, and their combinations through graph convolution.  ...  Then, the adaptive importance weights of the extracted embeddings are learned by using an attention mechanism.  ...  To better learn graph embedding information, we refer to and extend the adaptive multichannel graph convolutional neural network (AM-GCN) [24] architecture. ere are three main channels in this model.  ... 
doi:10.1155/2022/2389560 pmid:35898766 pmcid:PMC9313992 fatcat:haa56gmemvfelkl3lqd4zdtx74

DP-GCN: Node Classification Based on Both Connectivity and Topology Structure Convolutions for Risky Seller Detection [article]

Chen Zhe, Aixin Sun
2021 arXiv   pre-print
Motivated by business need, we present a dual-path graph convolution network, named DP-GCN, for node classification. DP-GCN considers both node connectivity and topology structure similarity.  ...  model consists of three main modules: (i) a C-GCN module to capture connectivity relationships between nodes, (ii) a T-GCN module to capture topology structure similarities between nodes, and (iii) a multi-head  ...  In Advances in Neural Informa- gcn: Adaptive multi-channel graph convolutional networks.  ... 
arXiv:2112.04757v1 fatcat:w2fozgyeqjfv5bsh7gi4ohg564

MuchSUM: Multi-channel Graph Neural Network for Extractive Summarization

Qianren Mao, Hongdong Zhu, Junnan Liu, Cheng Ji, Hao Peng, Jianxin Li, Lihong Wang, Zheng Wang
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
MuchSUM is a multi-channel graph convolutional network designed to explicitly incorporate multiple salient summary-worthy features.  ...  Then, a cross-channel convolution operation is designed to distill the common graph representations shared by different channels.  ...  We present MuchSUM, a multi-channel convolutional graph neural network (GNN) for modeling summarization graphs.  ... 
doi:10.1145/3477495.3531906 fatcat:zes7tj5tabdqrpvfv4qbfan7me

MI-GCN: Node Mutual Information-based Graph Convolutional Network

Lei Tian, Huaming Wu
2022 Companion Proceedings of the Web Conference 2022  
In order to overcome this issue, we propose a novel node Mutual Information-based Graph Convolutional Network (MI-GCN) for semi-supervised node classification.  ...  Graph Neural Networks (GNNs) have been widely used in various processing tasks for processing graphs and complex network data.  ...  a fixed proportion of edges at each training time. • AM-GCN [28] proposed an adaptive multi-channel graph convolutional network to simultaneously extract node embeddings from node features, topology  ... 
doi:10.1145/3487553.3524711 fatcat:oliv2ieui5e73fbta7satoijnq

Dynamic Graph Learning-Neural Network for Multivariate Time Series Modeling [article]

Zhuoling Li, Gaowei Zhang, Lingyu Xu, Jie Yu
2021 arXiv   pre-print
Existing graph neural networks (GNN) typically model multivariate relationships with a pre-defined spatial graph or learned fixed adjacency graph.  ...  In this paper, we propose a novel framework, namely static- and dynamic-graph learning-neural network (SDGL).  ...  Now, researchers work to discover the optimal graph structure from data to improve the performance of GNN (Wu et al. 2019) . AM-GCN Figure 2 : The framework of SDGL.  ... 
arXiv:2112.03273v1 fatcat:l5qe3p4c5zgbxnbxlxdxmqqzmi

Self-Supervised Deep Graph Embedding with High-Order Information Fusion for Community Discovery [article]

Shuliang Xu, Shenglan Liu, Lin Feng
2021 arXiv   pre-print
The proposed algorithm uses self-supervised mechanism and different high-order information of graph to train multiple deep graph convolution neural networks.  ...  The outputs of multiple graph convolution neural networks are fused to extract the representations of nodes which include the attribute and structure information of a graph.  ...  Wang et al. propose an adaptive multi-channel graph attentional convolutional network [39] called as AM-GCN.  ... 
arXiv:2102.03302v2 fatcat:yejgzzmidze7lnlpwezsgkgyi4

Multi-view graph structure learning using subspace merging on Grassmann manifold [article]

Razieh Ghiasi, Hossein Amirkhani, Alireza Bosaghzadeh
2022 arXiv   pre-print
In this paper, we introduce a new graph structure learning approach using multi-view learning, named MV-GSL (Multi-View Graph Structure Learning), in which we aggregate different graph structure learning  ...  For example, Graph Neural Networks (GNNs) have achieved considerable successes in various tasks such as node classification, graph classification, and link prediction.  ...  Therefore, they have proposed a multi-channel method called AM-GCN for combining this information.  ... 
arXiv:2204.05258v1 fatcat:n7goq5t33vgknlmnbtcwylja4a

Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks [article]

Shuhao Shi, Jian Chen, Kai Qiao, Shuai Yang, Linyuan Wang, Bin Yan
2022 arXiv   pre-print
Our proposed Dual-Channel Consistency based Graph Convolutional Networks (DCC-GCN) uses dual-channel to extract embeddings from node features and topological structures, and then achieves reliable low-confidence  ...  The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory.  ...  Am-gcn: Adaptive multi-channel graph convolutional networks, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.  ... 
arXiv:2205.03753v1 fatcat:sycvxs6w2vb5xinjlbjbrjucpi

TeKo: Text-Rich Graph Neural Networks with External Knowledge [article]

Zhizhi Yu, Di Jin, Jianguo Wei, Ziyang Liu, Yue Shang, Yun Xiao, Jiawei Han, Lingfei Wu
2022 arXiv   pre-print
Graph Neural Networks (GNNs) have gained great popularity in tackling various analytical tasks on graph-structured data (i.e., networks).  ...  We further design a reciprocal convolutional mechanism for the constructed heterogeneous semantic network, enabling network structure and textual semantics to collaboratively enhance each other and learn  ...  Neither AM-GCN nor Geom-GCN is so competitive here.  ... 
arXiv:2206.07253v1 fatcat:ru5mxojidjefxgcicsmlgas52e

Effective Graph Learning with Adaptive Knowledge Exchange [article]

Liang Zeng, Jin Xu, Zijun Yao, Yanqiao Zhu, Jian Li
2021 arXiv   pre-print
Graph Neural Networks (GNNs), due to their capability to learn complex relations (edges) among attributed objects (nodes) within graph datasets, have already been widely used in various graph mining tasks  ...  In this paper, we introduce a novel GNN learning framework, called AKE-GNN (Adaptive-Knowledge-Exchange GNN), which adaptively exchanges diverse knowledge learned from multiple graph views generated by  ...  AM-GCN [24] proposes an adaptive multi-channel GNN and constructs two topology and feature graphs to integrate topological structures and node features.  ... 
arXiv:2106.05455v2 fatcat:k4byiokosfen7f4rmot6j5uipe

Relational Graph Neural Network Design via Progressive Neural Architecture Search [article]

Ailing Zeng, Minhao Liu, Zhiwei Liu, Ruiyuan Gao, Jing Qin, Qiang Xu
2022 arXiv   pre-print
We propose a novel solution to addressing a long-standing dilemma in the representation learning of graph neural networks (GNNs): how to effectively capture and represent useful information embedded in  ...  AM-GCN [39] , HWGCN [23] , MultiHop [54] ) try to learn adaptive attention scores when aggregating neighboring nodes from different hops.  ...  The following formula presents the mathematical form of message passing in a graph convolutional network (GCN) [15] .  ... 
arXiv:2105.14490v4 fatcat:7qzj7xuwpnbubfpwempt6a3m3q

Decoupling the Depth and Scope of Graph Neural Networks [article]

Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen
2022 arXiv   pre-print
State-of-the-art Graph Neural Networks (GNNs) have limited scalability with respect to the graph and model sizes.  ...  On large graphs, increasing the model depth often means exponential expansion of the scope (i.e., receptive field).  ...  AM-GCN: Adaptive multi-channel graph convolutional networks.  ... 
arXiv:2201.07858v1 fatcat:lzoilhqdrnbefntai4onjjoie4

Deep Graph Neural Networks with Shallow Subgraph Samplers [article]

Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, Ren Chen
2022 arXiv   pre-print
While Graph Neural Networks (GNNs) are powerful models for learning representations on graphs, most state-of-the-art models do not have significant accuracy gain beyond two to three layers.  ...  A properly sampled subgraph may exclude irrelevant or even noisy nodes, and still preserve the critical neighbor features and graph structures.  ...  Other related methods such as GDC (Klicpera et al., 2019b) and AM-GCN (Wang et al., 2020) reconstructs the adjacency matrix in each GNN layer to short-cut important multi-hop neighbors.  ... 
arXiv:2012.01380v3 fatcat:qbztim226rc2vhpnri7hdzzbou

Multiplex Heterogeneous Graph Convolutional Network [article]

Pengyang Yu, Chaofan Fu, Yanwei Yu, Chao Huang, Zhongying Zhao, Junyu Dong
2022 pre-print
To tackle this challenge, this work proposes a Multiplex Heterogeneous Graph Convolutional Network (MHGCN) for heterogeneous network embedding.  ...  Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous network data, ranging from link prediction to node classification.  ...  propagation between graph layers. • AM-GCN [32] -AM-GNN is a state-of-the-art graph convolutional network, which is an adaptive multi-channel graph convolutional networks for semi-supervised classification  ... 
doi:10.1145/3534678.3539482 arXiv:2208.06129v1 fatcat:dntwp2nqrrdmpcelfdt2iw7u2y
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