A Graph Neural Network Framework for Social Recommendations

Fan, Wenqi; Ma, Yao; Li, Qing; Wang, Jianping; Cai, Guoyong; Tang, Jiliang; Yin, Dawei

doi:10.1109/tkde.2020.3008732

Cited by 107 publications

(43 citation statements)

References 51 publications

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“…Unlike matrix factorization methods, graph neural network methods infer node embeddings directly from graphs and demonstrate the efectiveness from recent social recommendation work [24,57,58]. GraphRec [9] and GraphRec+ [10] uses graph attention networks to learn user and item embeddings for recommendation. [44] utilizes dynamic graph attention networks to capture the dynamic user's interest from the social dimension.…”

Section: Social Recommendationmentioning

confidence: 99%

“…In this way, we assume that users with social links also share similar item interests. Therefore, we could simultaneously aggregate social information and user-item interactions [9,10,56,61] to alleviate the cold-start issue. SocialGCN [55,56] employs the Graph Convolutional Network (GCN) to enhance user embedding by simulating how the recursive social diffusion process influences users.…”

Section: Introductionmentioning

confidence: 99%

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Federated Social Recommendation with Graph Neural Network

Liu

Yang

Fan

et al. 2022

ACM Trans. Intell. Syst. Technol.

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Recommender systems have become prosperous nowadays, designed to predict users’ potential interests in items by learning embeddings. Recent developments of the Graph Neural Networks (GNNs) also provide recommender systems with powerful backbones to learn embeddings from a user-item graph. However, only leveraging the user-item interactions suffers from the cold-start issue due to the difficulty in data collection. Hence, current endeavors propose fusing social information with user-item interactions to alleviate it, which is the social recommendation problem. Existing work employs GNNs to aggregate both social links and user-item interactions simultaneously. However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns. Additionally, according to strict privacy protection under General Data Protection Regulation, centralized data storage may not be feasible in the future, urging a decentralized framework of social recommendation. As a result, we design a federated learning recommender system for the social recommendation task, which is rather challenging because of its heterogeneity, personalization, and privacy protection requirements. To this end, we devise a novel framework Fe drated So cial recommendation with G raph neural network ( FeSoG ). Firstly, FeSoG adopts relational attention and aggregation to handle heterogeneity. Secondly, FeSoG infers user embeddings using local data to retain personalization. Last but not least, the proposed model employs pseudo-labeling techniques with item sampling to protect the privacy and enhance training. Extensive experiments on three real-world datasets justify the effectiveness of FeSoG in completing social recommendation and privacy protection. We are the first work proposing a federated learning framework for social recommendation to the best of our knowledge.

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Section: Social Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Federated Social Recommendation with Graph Neural Network

Liu

Yang

Fan

et al. 2022

ACM Trans. Intell. Syst. Technol.

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show abstract

“…Specifically, this method models contextdependent social influence with a graph-attention neural network. -GraphRec+ [5] (AC): An extension of GraphRec, with additional information of the item-item graph, can learn better user and item representations in social recommendations.…”

Section: Experimental Settingsmentioning

confidence: 99%

Graph Neural Networks with Dynamic and Static Representations for Social Recommendation

Lin¹,

Chen²,

Wang³

2022

Preprint

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Recommender systems based on graph neural networks receive increasing research interest due to their excellent ability to learn a variety of side information including social networks. However, previous works usually focus on modeling users, not much attention is paid to items. Moreover, the possible changes in the attraction of items over time, which is like the dynamic interest of users are rarely considered, and neither do the correlations among items. To overcome these limitations, this paper proposes graph neural networks with dynamic and static representations for social recommendation (GNN-DSR), which considers both dynamic and static representations of users and items and incorporates their relational influence. GNN-DSR models the short-term dynamic and long-term static interactional representations of the user's interest and the item's attraction, respectively. Furthermore, the attention mechanism is used to aggregate the social influence of users on the target user and the correlative items' influence on a given item. The final latent factors of user and item are combined to make a prediction. Experiments on three real-world recommender system datasets validate the effectiveness of GNN-DSR.

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“…This vulnerability may lead to severe consequences in specific applications. For example, GNNs have been widely used in the recommender systems of many e-commerce platforms [18], [19], [20]. In these platforms, malicious attackers may employ some vicious accounts to interact with the faddish commodity and unpopular commodity synergistically.…”

Section: Introductionmentioning

confidence: 99%

Robust Unsupervised Graph Representation Learning via Mutual Information Maximization

Wang¹,

Luo²,

Li³

et al. 2022

Preprint

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Recent studies have shown that GNNs are vulnerable to adversarial attack. Thus, many approaches are proposed to improve the robustness of GNNs against adversarial attacks. Nevertheless, most of these methods measure the model robustness based on label information and thus become infeasible when labels information is not available. Therefore, this paper focuses on robust unsupervised graph representation learning. In particular, to quantify the robustness of GNNs without label information, we propose a robustness measure, named graph representation robustness (GRR), to evaluate the mutual information between adversarially perturbed node representations and the original graph. There are mainly two challenges to estimate GRR: 1) mutual information estimation upon adversarially attacked graphs; 2) high complexity of adversarial attack to perturb node features and graph structure jointly in the training procedure. To tackle these problems, we further propose an effective mutual information estimator with subgraph-level summary and an efficient adversarial training strategy with only feature perturbations. Moreover, we theoretically establish a connection between our proposed GRR measure and the robustness of downstream classifiers, which reveals that GRR can provide a lower bound to the adversarial risk of downstream classifiers. Extensive experiments over several benchmarks demonstrate the effectiveness and superiority of our proposed method.

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A Graph Neural Network Framework for Social Recommendations

Cited by 107 publications

References 51 publications

Federated Social Recommendation with Graph Neural Network

Federated Social Recommendation with Graph Neural Network

Graph Neural Networks with Dynamic and Static Representations for Social Recommendation

Robust Unsupervised Graph Representation Learning via Mutual Information Maximization

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