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Search to aggregate neighborhood for graph neural network
[article]
2021
arXiv
pre-print
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Recent years have witnessed the popularity and success of graph neural networks (GNN) in various scenarios. ...
In this work, to obtain the data-specific GNN architectures and address the computational challenges facing by NAS approaches, we propose a framework, which tries to Search to Aggregate NEighborhood (SANE ...
Terms-graph neural network, neural architecture search, message passing
I. ...
arXiv:2104.06608v2
fatcat:xsjiezgfajhihhld5munmkbvqa
Evolutionary Architecture Search for Graph Neural Networks
[article]
2020
arXiv
pre-print
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However, very litter work has been done about Graph Neural Networks (GNN) learning on unstructured network data. ...
To the best of our knowledge, this is the first work to introduce and evaluate evolutionary architecture search for GNN models. ...
CONCLUSION In this paper, we aim to demonstrate the effectiveness of evolutionary neural architecture search for optimizing graph neural network models on graph-structured data. ...
arXiv:2009.10199v1
fatcat:k2h23byz2jfebbiw3mzckveblm
Simplifying Architecture Search for Graph Neural Network
[article]
2020
arXiv
pre-print
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To overcome these drawbacks, we propose the SNAG framework (Simplified Neural Architecture search for Graph neural networks), consisting of a novel search space and a reinforcement learning based search ...
Recent years have witnessed the popularity of Graph Neural Networks (GNN) in various scenarios. ...
neural networks (CNN) and recurrent neural networks (RNN). ...
arXiv:2008.11652v2
fatcat:7mgbknvplvcsxoh3mcs3ofhduy
RAW-GNN: RAndom Walk Aggregation based Graph Neural Network
[article]
2022
arXiv
pre-print
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It replaces the conventional neighborhoods with path-based neighborhoods and introduces a new path-based aggregator based on Recurrent Neural Networks. ...
Here, we introduce a novel aggregation mechanism and develop a RAndom Walk Aggregation-based Graph Neural Network (called RAW-GNN) method. ...
We integrate random walk sampling into graph neural networks and extend the conventional neighborhoods to k-hop path-based neighborhoods. ...
arXiv:2206.13953v1
fatcat:5ok76d4zhvb2zhrpft4olunoxi
Policy-GNN: Aggregation Optimization for Graph Neural Networks
[article]
2020
arXiv
pre-print
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Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors ...
It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. ...
Learning Graph Representations with Deep Neural Networks Due to the great success of convolution neural networks on image data, graph neural networks [5, 14, 20, 37] have been extensively studied for ...
arXiv:2006.15097v1
fatcat:b5h7cvwp5rh2xcinb7p4rg4cia
Geom-GCN: Geometric Graph Convolutional Networks
[article]
2020
arXiv
pre-print
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From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. ...
Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. ...
ACKNOWLEDGMENTS We thank the reviewers for their valuable feedback. This work was supported in part by National Natural Science ...
arXiv:2002.05287v2
fatcat:yf7xylymlzbgjadk726bvr3pfu
Rethinking Graph Neural Architecture Search from Message-passing
[article]
2021
arXiv
pre-print
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Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. ...
message-passing mechanism to construct powerful graph network search space. ...
Figure 1 shows an example of a graph neural network searched by GNAS. ...
arXiv:2103.14282v4
fatcat:rshdr2tqvbbm7ijborgvtjju7a
Graph Representation Learning for Road Type Classification
2021
Pattern Recognition
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2021 pre-print arXiv:2107.07791v1 fatcat:7v3h3rohivgerjzvmolv4qxc24Other Versions
We present a novel learning-based approach to graph representations of road networks employing state-of-the-art graph convolutional neural networks. ...
Furthermore, we propose a novel aggregation approach, Graph Attention Isomorphism Network, GAIN. ...
Using generative models based on deep neural networks have also been extensively studied for graph generations. ...
doi:10.1016/j.patcog.2021.108174
fatcat:funo4akd45f2xmf3ro6m6vl4xu
Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations
[article]
2021
arXiv
pre-print
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Tens of different graph neural network variants have been proposed, most following a neighborhood aggregation scheme, where the node features are updated via aggregating features of its neighboring nodes ...
Graph neural networks are emerging as continuation of deep learning success w.r.t. graph data. ...
Model Structure With the generic neighborhood aggregation layer as eq.7 and READOUT layer as eq.8, we build our graph neural network HAG-Net for graph-level tasks by stacking multiple neighborhood aggregation ...
arXiv:2102.04064v1
fatcat:o6nchngbkvdyvmm5riksdmlmzi
Learning Better Representations for Neural Information Retrieval with Graph Information
2020
Proceedings of the 29th ACM International Conference on Information & Knowledge Management
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In this study, we aim to incorporate this rich information encoded in these two graphs into existing neural ranking models. ...
We present two graph-based neural ranking models (EmbRanker and AggRanker) to enrich learned text representations with graph information that captures rich users' interaction behavior information. ...
propagating the information of all the neighborhoods to the focal node by a graph neural network (GNN). ...
doi:10.1145/3340531.3411957
dblp:conf/cikm/LiR0MM0M20
fatcat:vurrhgyzpzau5epajhzyz5524a
ATPGNN: Reconstruction of Neighborhood in Graph Neural Networks with Attention-based Topological Patterns
2021
IEEE Access
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Graph Neural Networks (GNNs) have been applied in many fields of semi-supervised node classification for non-Euclidean data. ...
Third, we use some network embedding methods to get graph structure information of each node. ...
For the aggregation method of topology neighborhood, we adopt the basic settings of the graph neural network method used in our model. ...
doi:10.1109/access.2021.3050541
fatcat:pn5uyqnvr5dw3ord6hhytzlgqm
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
[article]
2019
arXiv
pre-print
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In this paper, we propose a Graph Neural Architecture Search method (GraphNAS for short) that enables automatic search of the best graph neural architecture based on reinforcement learning. ...
Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. ...
Introduction Graph Neural Networks (GNNs) have been popularly used for analyzing graph data such as social network data and biological data. ...
arXiv:1904.09981v2
fatcat:5pyltylpyrf7rhaqhrm32xlmuy
A review of recommendation system research based on bipartite graph
2021
MATEC Web of Conferences
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An review of the recommendation system of graphs summarizes the three characteristics of graph neural network processing bipartite graph data in the recommendation field: interchangeability, Multi-hop ...
The biggest contribution of the full paper is that it summarizes the general framework of graph neural network processing bipartite graph recommendation from the models with the best recommendation effect ...
The role of this layer is to learn a lowdimensional vector representation for the input of the graph neural network model. ...
doi:10.1051/matecconf/202133605010
fatcat:qyiiqjnev5eublqlmps4gcworq
GAIN: Graph Attention Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel graph neural network architecture, Graph Attention \& Interaction Network (GAIN), for inductive learning on graphs. ...
Graph neural network models generate node embeddings by merging nodes features with the aggregated neighboring nodes information. ...
To address this challenging problem, we propose a novel graph neural network model, Graph Attention & Interaction Network (GAIN). ...
arXiv:2011.01393v1
fatcat:ahcjc4bm5nhulhg3k5vpg5r3jm
Tree Decomposed Graph Neural Network
[article]
2021
arXiv
pre-print
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deeper graph neural networks. ...
Graph Neural Networks (GNNs) have achieved significant success in learning better representations by performing feature propagation and transformation iteratively to leverage neighborhood information. ...
Graph Neural Networks Typically, most graph neural networks (GNNs) can be decomposed into two operational procedures: (1) neighborhood propagation and aggregation, and (2) feature transformation. ...
arXiv:2108.11022v1
fatcat:5vnekq7t7zh2bkdrulg2zfgq6m
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