Combining Graph Convolutional Neural Networks and Label Propagation

2022 ◽  
Vol 40 (4) ◽  
pp. 1-27
Author(s):  
Hongwei Wang ◽  
Jure Leskovec
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Message Passing ◽  
Label Propagation ◽  
Unified Framework ◽  
Label Information ◽  
Message Passing Algorithms ◽  
Node Classification ◽  
Improved Performance ◽  
Node Labels

Label Propagation Algorithm (LPA) and Graph Convolutional Neural Networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification, but LPA propagates node label information across the edges of the graph, while GCN propagates and transforms node feature information. However, while conceptually similar, theoretical relationship between LPA and GCN has not yet been systematically investigated. Moreover, it is unclear how LPA and GCN can be combined under a unified framework to improve the performance. Here we study the relationship between LPA and GCN in terms of feature/label influence , in which we characterize how much the initial feature/label of one node influences the final feature/label of another node in GCN/LPA. Based on our theoretical analysis, we propose an end-to-end model that combines GCN and LPA. In our unified model, edge weights are learnable, and the LPA serves as regularization to assist the GCN in learning proper edge weights that lead to improved performance. Our model can also be seen as learning the weights of edges based on node labels, which is more direct and efficient than existing feature-based attention models or topology-based diffusion models. In a number of experiments for semi-supervised node classification and knowledge-graph-aware recommendation, our model shows superiority over state-of-the-art baselines.

scholarly journals Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification

2021 ◽  
Author(s):  
Yunsheng Shi ◽  
Zhengjie Huang ◽  
Shikun Feng ◽  
Hui Zhong ◽  
Wenjing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Message Passing ◽  
Supervised Classification ◽  
State Of The Art ◽  
Label Propagation ◽  
Superior Performance ◽  
Label Prediction ◽  
Label Information ◽  
Message Passing Algorithms ◽  
Prediction Strategy

Graph neural network (GNN) and label propagation algorithm (LPA) are both message passing algorithms, which have achieved superior performance in semi-supervised classification. GNN performs feature propagation by a neural network to make predictions, while LPA uses label propagation across graph adjacency matrix to get results. However, there is still no effective way to directly combine these two kinds of algorithms. To address this issue, we propose a novel Unified Message Passaging Model (UniMP) that can incorporate feature and label propagation at both training and inference time. First, UniMP adopts a Graph Transformer network, taking feature embedding and label embedding as input information for propagation. Second, to train the network without overfitting in self-loop input label information, UniMP introduces a masked label prediction strategy, in which some percentage of input label information are masked at random, and then predicted. UniMP conceptually unifies feature propagation and label propagation and is empirically powerful. It obtains new state-of-the-art semi-supervised classification results in Open Graph Benchmark (OGB).

scholarly journals Graph convolutional neural networks with node transition probability-based message passing and DropNode regularization

2021 ◽  
Vol 174 ◽  
pp. 114711
Author(s):  
Tien Huu Do ◽  
Duc Minh Nguyen ◽  
Giannis Bekoulis ◽  
Adrian Munteanu ◽  
Nikos Deligiannis
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Message Passing ◽  
Transition Probability

scholarly journals The interplay between communities and homophily in semi-supervised classification using graph neural networks

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Hussain Hussain ◽  
Tomislav Duricic ◽  
Elisabeth Lex ◽  
Denis Helic ◽  
Roman Kern
Keyword(s):  
Neural Networks ◽  
Community Structure ◽  
Model Selection ◽  
Graph Structure ◽  
Information Theoretic ◽  
Node Classification ◽  
Selection For ◽  
Graph Neural Networks ◽  
Node Labels ◽  
The Impact

AbstractGraph Neural Networks (GNNs) are effective in many applications. Still, there is a limited understanding of the effect of common graph structures on the learning process of GNNs. To fill this gap, we study the impact of community structure and homophily on the performance of GNNs in semi-supervised node classification on graphs. Our methodology consists of systematically manipulating the structure of eight datasets, and measuring the performance of GNNs on the original graphs and the change in performance in the presence and the absence of community structure and/or homophily. Our results show the major impact of both homophily and communities on the classification accuracy of GNNs, and provide insights on their interplay. In particular, by analyzing community structure and its correlation with node labels, we are able to make informed predictions on the suitability of GNNs for classification on a given graph. Using an information-theoretic metric for community-label correlation, we devise a guideline for model selection based on graph structure. With our work, we provide insights on the abilities of GNNs and the impact of common network phenomena on their performance. Our work improves model selection for node classification in semi-supervised settings.

scholarly journals UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks

2021 ◽  
Author(s):  
Jing Huang ◽  
Jie Yang
Keyword(s):  
Neural Networks ◽  
Message Passing ◽  
State Of The Art ◽  
Representation Learning ◽  
Graph Representation ◽  
Challenging Problem ◽  
Unified Framework ◽  
Real World Datasets ◽  
Graph Neural Networks ◽  
Research Domains

Hypergraph, an expressive structure with flexibility to model the higher-order correlations among entities, has recently attracted increasing attention from various research domains. Despite the success of Graph Neural Networks (GNNs) for graph representation learning, how to adapt the powerful GNN-variants directly into hypergraphs remains a challenging problem. In this paper, we propose UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs. In this framework, meticulously-designed architectures aiming to deepen GNNs can also be incorporated into hypergraphs with the least effort. Extensive experiments have been conducted to demonstrate the effectiveness of UniGNN on multiple real-world datasets, which outperform the state-of-the-art approaches with a large margin. Especially for the DBLP dataset, we increase the accuracy from 77.4% to 88.8% in the semi-supervised hypernode classification task. We further prove that the proposed message-passing based UniGNN models are at most as powerful as the 1-dimensional Generalized Weisfeiler-Leman (1-GWL) algorithm in terms of distinguishing non-isomorphic hypergraphs. Our code is available at https://github.com/OneForward/UniGNN.

A CONVblock for Convolutional Neural Networks

2021 ◽  
pp. 100-113
Author(s):  
Hmidi Alaeddine ◽  
Malek Jihene
Keyword(s):  
Neural Networks ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Experimental Results ◽  
Classification Models ◽  
Deep Architecture ◽  
Improved Performance ◽  
Convolution Filters ◽  
Classification Database

The reduction in the size of convolution filters has been shown to be effective in image classification models. They make it possible to reduce the calculation and the number of parameters used in the operations of the convolution layer while increasing the efficiency of the representation. The authors present a deep architecture for classification with improved performance. The main objective of this architecture is to improve the main performances of the network thanks to a new design based on CONVblock. The proposal is evaluated on a classification database: CIFAR-10 and MNIST. The experimental results demonstrate the effectiveness of the proposed method. This architecture offers an error of 1.4% on CIFAR-10 and 0.055% on MNIST.

scholarly journals Dual Self-Paced Graph Convolutional Network: Towards Reducing Attribute Distortions Induced by Topology

2019 ◽  
Author(s):  
Liang Yang ◽  
Zhiyang Chen ◽  
Junhua Gu ◽  
Yuanfang Guo
Keyword(s):  
Neural Networks ◽  
Learning Strategies ◽  
Convolutional Neural Networks ◽  
Training Process ◽  
Training Set ◽  
Convolutional Network ◽  
Attribute Information ◽  
Topology Information ◽  
Node Classification ◽  
The Impact

The success of graph convolutional neural networks (GCNNs) based semi-supervised node classification is credited to the attribute smoothing (propagating) over the topology. However, the attributes may be interfered by the utilization of the topology information. This distortion will induce a certain amount of misclassifications of the nodes, which can be correctly predicted with only the attributes. By analyzing the impact of the edges in attribute propagations, the simple edges, which connect two nodes with similar attributes, should be given priority during the training process compared to the complex ones according to curriculum learning. To reduce the distortions induced by the topology while exploit more potentials of the attribute information, Dual Self-Paced Graph Convolutional Network (DSP-GCN) is proposed in this paper. Specifically, the unlabelled nodes with confidently predicted labels are gradually added into the training set in the node-level self-paced learning, while edges are gradually, from the simple edges to the complex ones, added into the graph during the training process in the edge-level self-paced learning. These two learning strategies are designed to mutually reinforce each other by coupling the selections of the edges and unlabelled nodes. Experimental results of transductive semi-supervised node classification on many real networks indicate that the proposed DSP-GCN has successfully reduced the attribute distortions induced by the topology while it gives superior performances with only one graph convolutional layer.

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