A Deep Graph Neural Network-Based Mechanism for Social Recommendations

Guo, Zhiwei; Wang, Heng

doi:10.1109/tii.2020.2986316

Cited by 167 publications

(58 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Guo et al [33] hold that the feature space of social recommendation is composed of user features and item feature, the user feature is composed of inherent preference and social influence, and the item feature include attribute contents, attribute correlations, and attribute concatenation. They introduced a framework named GNN-SoR (Graph Neural Network-based Social Recommendation Framework) to exploit the correlations of item attributes.…”

Section: Recommendation Systemmentioning

confidence: 99%

Adversarial Machine Learning on Social Network: A Survey

Guo

2021

Front. Phys.

View full text Add to dashboard Cite

In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.

show abstract

Section: Recommendation Systemmentioning

confidence: 99%

Adversarial Machine Learning on Social Network: A Survey

Guo

2021

Front. Phys.

View full text Add to dashboard Cite

show abstract

“…GAT has stronger learning ability because the model can better capture correlation between node features [28]. 1 , ,…”

Section: Graph Neural Networkmentioning

confidence: 99%

“…Graph neural networks (GNNs) are becoming more and more influential in solving various challenges in many practical applications, such as social networks [1], paper citations [2], biological networks [3,4], product customer relationships [5], recommendation systems [1], and knowledge graphs [6], which data can be naturally represented as graph structures. The graph data structure is widely used to model data with complex connections between elements because of good expressive ability.…”

Section: Introductionmentioning

confidence: 99%

ADGraph: Accurate, Distributed Training on Large Graphs

Zhang¹,

Lai²,

Liu³

et al. 2021

Computer Science &Amp; Information Technology (CS &Amp; IT)

View full text Add to dashboard Cite

Graph neural networks (GNNs) have been emerging as powerful learning tools for recommendation systems, social networks and knowledge graphs. In these domains, the scale of graph data is immense, so that distributed graph learning is required for efficient GNNs training. Graph partition-based methods are widely adopted to scale the graph training. However, most of the previous works focus on scalability other than the accuracy and are not thoroughly evaluated on large-scale graphs. In this paper, we introduce ADGraph (accurate and distributed training on large graphs), exploring how to improve accuracy while keeping large-scale graph training scalability. Firstly, to maintain complete neighbourhood information of the training nodes after graph partitioning, we assign l-hop neighbours of the training nodes to the same partition. We also analyse the accuracy and runtime performance of graph training, with different l-hop settings. Secondly, multi-layer neighbourhood sampling is performed on each partition, so that the mini-batch generated can accurately train target nodes. We study the relationship between convergence accuracy and the sampled layers. We also find that partial neighbourhood sampling can achieve better performance than full neighbourhood sampling. Thirdly, to further overcome the generalization error caused by large-batch training, we choose to reduce batchsize after graph partitioned and apply the linear scaling rule in distributed optimization. We evaluate ADGraph using GraphSage and GAT models with ogbn-products and Reddit datasets on 32 GPUs. Experimental results show that ADGraph achieves better performance than the benchmark accuracy of GraphSage and GAT, while getting 24-29 times speedup on 32 GPUs.

show abstract

“…Many of these GNN models are inspired by traditional learning techniques. Inspired by the deep learning, [20] introduces a deep graph neural networkbased social recommendation framework(GNN-SoR), [21] introduces a deep learning-embedded social Internet of Things for an ambiguity-aware social recommendation. Inspired by autoencoder, [22] introduces a graph embeddingbased generative adversarial network.…”

Section: B Graph Convolution/poolingmentioning

confidence: 99%

An End-to-End Multiplex Graph Neural Network for Graph Representation Learning

et al. 2021

View full text Add to dashboard Cite

Research on graph classification tasks based on graph neural networks has attracted wide attention. The graphs to be classified may have various graph sizes (i.e., different numbers of nodes and edges) and have various graph properties (e.g., average node degree, diameter, and clustering coefficient). The diverse property of graphs has imposed significant challenges on existing graph learning techniques since diverse graphs have different best-fit hyperparameters. Consequently, it is unreasonable to learn graph representation from a set of diverse graphs by a unified graph neural network. Inspired by this, we design an end-to-end Multiplex Graph Neural Network (MxGNN) that learns graph representations with multiple GNNs, and combines them with a learnable method. The main challenge lies with the combination of multiple representation results. Our new findings show that the a priori graph properties do have an effect on the quality of representation learning, which can be used to guide the learning. Our experiments on graph classification with multiple data sets show that the performance of MxGNN is better than the existing graph representation learning methods.INDEX TERMS Graph neural networks, graph classification, graph representation learning.

show abstract

A Deep Graph Neural Network-Based Mechanism for Social Recommendations

Cited by 167 publications

References 27 publications

Adversarial Machine Learning on Social Network: A Survey

Adversarial Machine Learning on Social Network: A Survey

ADGraph: Accurate, Distributed Training on Large Graphs

An End-to-End Multiplex Graph Neural Network for Graph Representation Learning

Contact Info

Product

Resources

About