New Deep Neural Networks for Unsupervised Feature Learning on Graph Data

Graph data are ubiquitous in the real world, such as social networks, biological networks. To analyze graph data, a fundamental task is to learn node features to benefit downstream tasks, such as node classification, community detection. Inspired by the powerful feature learning capability of deep neural networks on various tasks, it is important and necessary to explore deep neural networks for feature learning on graphs. Different from the regular image and sequence data, graph data encode the complicated relational information between different nodes, which challenges the classical deep neural networks. Moreover, in real-world applications, the label of nodes in graph data is usually not available, which makes the feature learning on graphs more difficult. To address these challenging issues, this thesis is focusing on designing new deep neural networks to effectively explore the relational information for unsupervised feature learning on graph data. First, to address the sparseness issue of the relational information, I propose a new proximity generative adversarial network which can discover the underlying relational information for learning better node representations. Meanwhile, a new self-paced network embedding method is designed to address the unbalance issue of the relational information when learning node representations. Additionally, to deal with rich attributes associated to nodes, I develop a new deep neural network to capture various relational information in both topological structure and node attributes for enhancing network embedding. Furthermore, to preserve the relational information in the hidden layers of deep neural networks, I develop a novel graph convolutional neural network (GCN) based on conditional random fields, which is the first algorithm applying this kind of graphical models to graph neural networks in an unsupervised manner

SPI-GCN: A Simple Permutation-Invariant Graph Convolutional Network

A wide range of machine learning problems involve handling graph-structured data. Existing machine learning approaches for graphs, however, often imply computing expensive graph similarity measures, preprocessing input graphs, or explicitly ordering graph nodes. In this work, we present a novel and simple convolutional neural network architecture for supervised learning on graphs that is provably invariant to node permutation. The proposed architecture operates directly on arbitrary graphs and performs no node sorting. It also uses a simple multi-layer perceptron for prediction as opposed to conventional convolution layers commonly used in other deep learning approaches for graphs. Despite its simplicity, our architecture is competitive with state-of-the-art graph kernels and existing graph neural networks on benchmark graph classification data sets. Our approach clearly outperforms other deep learning algorithms for graphs on multiple multiclass classification tasks. We also evaluate our approach on a real-world original application in materials science, on which we achieve extremely reasonable results

Directed hypergraph neural network

To deal with irregular data structure, graph convolution neural networks have been developed by a lot of data scientists. However, data scientists just have concentrated primarily on developing deep neural network method for un-directed graph. In this paper, we will present the novel neural network method for directed hypergraph. In the other words, we will develop not only the novel directed hypergraph neural network method but also the novel directed hypergraph based semi-supervised learning method. These methods are employed to solve the node classification task. The two datasets that are used in the experiments are the cora and the citeseer datasets. Among the classic directed graph based semi-supervised learning method, the novel directed hypergraph based semi-supervised learning method, the novel directed hypergraph neural network method that are utilized to solve this node classification task, we recognize that the novel directed hypergraph neural network achieves the highest accuracies

Graph Neural Networks in TensorFlow and Keras with Spektral

In this paper we present Spektral, an open-source Python library for building graph neural networks with TensorFlow and the Keras application programming interface. Spektral implements a large set of methods for deep learning on graphs, including message-passing and pooling operators, as well as utilities for processing graphs and loading popular benchmark datasets. The purpose of this library is to provide the essential building blocks for creating graph neural networks, focusing on the guiding principles of user-friendliness and quick prototyping on which Keras is based. Spektral is, therefore, suitable for absolute beginners and expert deep learning practitioners alike. In this work, we present an overview of Spektral's features and report the performance of the methods implemented by the library in scenarios of node classification, graph classification, and graph regression.Comment: ICML 2020 - GRL+ Worksho