Graph Few-Shot Learning via Knowledge Transfer

Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest recently, which update the representation of each node by aggregating information of its neighbors. However, most GNNs have shallow layers with a limited receptive field and may not achieve satisfactory performance especially when the number of labeled nodes is quite small. To address this challenge, we innovatively propose a graph few-shot learning (GFL) algorithm that incorporates prior knowledge learned from auxiliary graphs to improve classification accuracy on the target graph. Specifically, a transferable metric space characterized by a node embedding and a graph-specific prototype embedding function is shared between auxiliary graphs and the target, facilitating the transfer of structural knowledge. Extensive experiments and ablation studies on four real-world graph datasets demonstrate the effectiveness of our proposed model and the contribution of each component.

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Revisiting Graph Neural Networks for Node Classification in Heterogeneous Graphs

2021 IEEE International Conference on Multimedia and Expo (ICME) ◽

10.1109/icme51207.2021.9428354 ◽

2021 ◽

Author(s):

Ye Tao ◽

Ying Li ◽

Zhonghai Wu

Keyword(s):

Neural Networks ◽

Node Classification ◽

Graph Neural Networks

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A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers

Computational Intelligence and Neuroscience ◽

10.1155/2020/8817849 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Wei Wang ◽

Yiyang Hu ◽

Ting Zou ◽

Hongmei Liu ◽

Jin Wang ◽

...

Keyword(s):

Neural Networks ◽

Computational Complexity ◽

Embedded Systems ◽

Receptive Field ◽

Image Classification ◽

Classification Accuracy ◽

Deep Neural Networks ◽

Classification Approach ◽

Dilated Convolution ◽

Convolution Filters

Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilated convolutions are introduced into a specific convolutional layer of the MobileNet model. Without increasing the number of parameters, dilated convolutions are used to increase the receptive field of the convolution filters to obtain better classification accuracy. The experiments were performed on the Caltech-101, Caltech-256, and Tubingen animals with attribute datasets, respectively. The results show that Dilated-MobileNets can obtain up to 2% higher classification accuracy than MobileNet.

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

Applied Network Science ◽

10.1007/s41109-021-00423-1 ◽

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.

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Automatic Assessment of Buildings Location Fitness for Solar Panels Installation Using Drones and Neural Network

CivilEng ◽

10.3390/civileng2040056 ◽

2021 ◽

Vol 2 (4) ◽

pp. 1052-1064

Author(s):

Ammar Alzarrad ◽

Chance Emanuels ◽

Mohammad Imtiaz ◽

Haseeb Akbar

Keyword(s):

Neural Network ◽

Neural Networks ◽

Solar Energy ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Solar Panels ◽

Automatic Assessment ◽

Surrounding Environment ◽

Proposed Model ◽

The City

Solar panel location assessment is usually a time-consuming manual process, and many criteria should be taken into consideration before deciding. One of the most significant criteria is the building location and surrounding environment. This research project aims to propose a model to automatically identify potential roof spaces for solar panels using drones and convolutional neural networks (CNN). Convolutional neural networks (CNNs) are used to identify buildings’ roofs from drone imagery. Transfer learning on the CNN is used to classify roofs of buildings into two categories of shaded and unshaded. The CNN is trained and tested on separate imagery databases to improve classification accuracy. Results of the current project demonstrate successful segmentation of buildings and identification of shaded roofs. The model presented in this paper can be used to prioritize the buildings based on the likelihood of getting benefits from switching to solar energy. To illustrate an implementation of the presented model, it has been applied to a selected neighborhood in the city of Hurricane in West Virginia. The research results show that the proposed model can assist investors in the energy and building sectors to make better and more informed decisions.

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UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/353 ◽

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.

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