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HDGI: An Unsupervised Graph Neural Network for Representation Learning in Heterogeneous Graph Yuxiang Ren,1 Bo Liu,2 Chao Huang,2 Peng Dai,2 Liefeng Bo,2 Jiawei Zhang,1 1Florida State University, IFM Lab 2JD Finance America Corporation, AI lab yuxiang@ifmlab.org, kfliubo@gmail.com, chuang7@nd.edu, peng.dai@jd.com, liefeng.bo@jd.com, jiawei@ifmlab.org

Unsupervised Learning with Graph Neural Networks. Thomas Kipf Universiteit van Amsterdam. Many aspects of our world can be understood in terms of systems ...

Unsupervised pedestrian trajectory prediction with graph neural networks Mingkun Wang*1, Dianxi Shi *2,3, Naiyang Guan , Tao Zhang4, Liujing Wang 1, Ruoxiang Li 1College of Computer, National ...

Index Terms—graph neural network, network embedding, unsupervised learning, node clustering I. INTRODUCTION Graph Neural Networks (GNNs) [1], [2] have become a hot topic in deep learning for their potentials in modeling irregular data. GNNs have been widely used and achieved state-of-the-art performance in many fields, such as computer vision,

Most of the existing Graph Neural Networks (GNNs) are deliberately designed for ... Toward Unsupervised Graph Neural Network: Interactive Clustering and ...

Figure 1: A heterogeneous bibliographic network. HDGI is a novel unsupervised graph neural network with the attention mechanism. It handles graph heterogeneity by utilizing an attention mechanism on meta-paths and deals with unsupervised setting by applying mutual infor-mation maximization. Our experiments demonstrate that the representations

Graph Neural Networks: Graph Neural Networks (GNNs). [1], [2] aim at applying the expressive representation power of deep learning to irregular data, i.e, ...

A. Unsupervised Graph Neural Networks In this subsection, we derive unsupervised graph neural network from the semi-supervised one in Section II-B1. By integrating the probability interpretation of tiy (Eq. (6)) into the loss function of semi-supervised node classification (Eq.

Next, unsupervised graph neural networks are elaborated. Finally, exiting methods, which combine clustering and embedding, are introduced. Graph Neural Networks: Graph Neural Networks (GNNs) [1], [2] aim at applying the expressive representation power of deep learning to irregular data, i.e, graphs.

C. Graph neural networks Graph neural networks are introduced in 2009 [21] as a tool for processing graph representation of data (citations, social networks, images, etc). After that graph convolutional networks appeared as an evolution [22], [23]. Now they are widely used for different tasks and most valuable approaches

We propose InfoGraph, an unsupervised graph representation learning method based ... Concurrently to this work, information maximizing graph neural networks ...

In this talk: using graph neural networks for unsupervised learning. ○ Very important direction: most graphs in the wild are unlabeled! ○ ...

Graph Neural Networks (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs. GNNs are neural ...

... Graph Convolutional Networks: Unsupervised Learning Meets Semi-Supervised Learning ... In recent years, graph neural networks (Bruna et al. 2014;.

List of unsupervised (self-supervised) graph neural network (GNN) methods. The purpose of this repository is to provide a literature survey of ...

Secondly, graph neural networks are recent deep learning models designed specifically for data with graph structure, such as social networks and knowledge graphs. See Appendix B for some brief introduction and refer to the surveys [30, 61, 57] for more details. Since molecules can be viewed as structured graphs, various graph neural networks have

Abstract: Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving ...

HDGI: An Unsupervised Graph Neural Network for Representation Learning in Heterogeneous Graph Yuxiang Ren,1 Bo Liu,2 Chao Huang,2 Peng Dai,2 Liefeng Bo,2 Jiawei Zhang,1 1Florida State University, IFM Lab 2JD Finance America Corporation, AI lab yuxiang@ifmlab.org, kfliubo@gmail.com, chuang7@nd.edu, peng.dai@jd.com, liefeng.bo@jd.com, jiawei ...