GeniePath: Graph Neural Networks with Adaptive Receptive Paths

By Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song. AAAI Conference on Artificial Intelligence, 2018

350 citations recorded by the source corpus.

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away. Our method works in both transductive and inductive settings, and extensive experiments compared with competitive methods show that our approaches yield state-of-the-art results on large graphs.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Social Psychology

View Paper: https://www.semanticscholar.org/paper/127af6effc74f073ac2442f6d82c944f562e2c0f

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Citations in the corpus

The Klarna Product Page Dataset: A Realistic Benchmark for Web Representation Learning (2021, background): “Over the recent years, there has been an upsurge in papers about node classiﬁcation on learning low-dimensional representations with GCNs. GCNs have shown notable performance on social network graphs [39, 6, 5], citation networks [43, 9], and biochemical graphs [6, 39, 24].”
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning (2019, methodology): “To get rid of the architecture sampling process, DARTS [Liu et al. , 2018a] replace the feedback triggered by constant rewards in reinforcement learning with more efﬁcient gradient feedback from generic loss.”
Graph Few-shot Learning via Knowledge Transfer (2019, background): “Graph Neural Network Recently, a variety of graph neural network models (GNN) have been proposed to exploit the structures underlying graphs to benefit a variety of applications (Kipf and Welling 2017; Zhang et al. 2019; Tang et al. 2019; Huang et al. 2019; Liu et al. 2019; Gao, Wang, and Ji 2018).”
SGAS: Sequential Greedy Architecture Search (2019, neutral): “26 manual GeniePath [36] 97.”
Graph Transformer Networks (2019, background): “To improve performance or scalability, generalized convolution based on spectral convolution [4, 26], attention mechanism on neighbors [25, 33], subsampling [6, 7] and inductive representation for a large graph [14] have been studied.”
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (2020, methodology): “, we compare it with various GNN baselines under the semi-supervised learning setting. We select GCN [17], GAT [34], RGCN [31], and GraphSAGE [12] to represent general GNN models. We choose GeniePath [23], Player2Vec [48], SemiGNN [37], and GraphConsis [25] as four state-of-the-art GNN-based fraud d”
Simplifying Architecture Search for Graph Neural Network (2020, methodology): “All above plus SAGE-LSTM and GeniePath CONCAT , MAX , LSTM IDENTITY , ZERO node features.”
Unifying Graph Convolutional Neural Networks and Label Propagation (2020, methodology): “…Algorithm (LPA) (Zhu et al., 2005; Zhou et al., 2004; Zhang & Lee, 2007; Wang & Zhang, 2008; Karasuyama & Mamitsuka, 2013; Gong et al., 2017; Liu et al., 2019a); On the node side V , node features are propagated along edges and transformed through neural network layers, which is known as…”
Simple and Deep Graph Convolutional Networks (2020, methodology): “We compare GCNII with the following state-of-the-art methods: GraphSAGE (Hamilton et al., 2017), VR-GCN (Chen et al., 2018b), GaAN (Zhang et al., 2018), GAT (Veliˇckovi´c et al., 2018), JKNet (Xu et al., 2018), GeniePath (Liu et al., 2019), Cluster-GCN (Chiang et al., 2019).”
Graph Neural Networks in Recommender Systems: A Survey (2020, background): “There are only a few emerging works to adaptively decide the propagation step for each node in order to obtain a reasonable reception field [70, 95, 97].”
Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs (2018, background): “…for instance, (Kipf & Welling, 2016; Atwood & Towsley, 2016; Hamilton et al., 2017; Velick-ovic et al., 2017; Monti et al., 2017; Xu et al., 2018; Liu et al., 2018; Liben-Nowell & Kleinberg, 2007; van den Berg et al., 2017; Duvenaud et al., 2015; Gilmer et al., 2017; Ying et al., 2018; Xu et…”
AutoGraph: Automated Graph Neural Network (2020, neutral): “– GeniePath [12].”
Heterogeneous Graph Neural Networks for Malicious Account Detection (2018, methodology): “[17] propose GeniePath, which aims to adaptively filter each node’s receptive fields, compared with GCN that does convolution on pre-defined receptive fields.”
Bandit Samplers for Training Graph Neural Networks (2020, methodology): “For example, GCN-BS, GAT-BS and GP-BS denote the training approaches for GCN, GAT [20] and GeniePath [15] respectively.”
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks (2019, methodology): “te-of-the-art performance of testing accuracy reported in recent papers. PPI Reddit FastGCN [1] N/A 93.7 GraphSAGE [5] 61.2 95.4 VR-GCN [2] 97.8 96.3 GaAN [16] 98.71 96.36 GAT [14] 97.3 N/A GeniePath [10] 98.5 N/A Cluster-GCN 99.36 96.60 score). Using the proposed approach, we are able to successful”