Graph Neural Networks for Friend Ranking in Large-scale Social Platforms

Ashok, S.; Liu, Yozen; Yu, Jun; Shah, Neil

doi:10.1145/3442381.3450120

Cited by 46 publications

(14 citation statements)

References 30 publications

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“…Such factors influence network formation, sparsity, and thus GNN inference quality simply due to network topology [33]. Given these acknowledged issues, GNNs are still used in applications including ranking [27], recommendation [15], engagement prediction [32], traffic modeling [17], search and discovery [37] and more, and when unchecked, suffer traditional machine learning unfairness issues [6].…”

Section: F Broader Impactmentioning

confidence: 99%

Is Homophily a Necessity for Graph Neural Networks?

Ma¹,

Liu²,

Shah³

et al. 2021

Preprint

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Graph neural networks (GNNs) have shown great prowess in learning representations suitable for numerous graph-based machine learning tasks. When applied to semi-supervised node classification, GNNs are widely believed to work well due to the homophily assumption ("like attracts like"), and fail to generalize to heterophilous graphs where dissimilar nodes connect. Recent works design new architectures to overcome such heterophily-related limitations, citing poor baseline performance and new architecture improvements on a few heterophilous graph benchmark datasets as evidence for this notion. In our experiments, we empirically find that standard graph convolutional networks (GCNs) can actually achieve better performance than such carefully designed methods on some commonly used heterophilous graphs. This motivates us to reconsider whether homophily is truly necessary for good GNN performance. We find that this claim is not quite true, and in fact, GCNs can achieve strong performance on heterophilous graphs under certain conditions. Our work carefully characterizes these conditions, and provides supporting theoretical understanding and empirical observations. Finally, we examine existing heterophilous graphs benchmarks and reconcile how the GCN (under)performs on them based on this understanding.Preprint. Under review.

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Section: F Broader Impactmentioning

confidence: 99%

Is Homophily a Necessity for Graph Neural Networks?

Ma¹,

Liu²,

Shah³

et al. 2021

Preprint

Self Cite

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“…The high-order interactions of user 𝑢 1 can be naturally extended to a tree-likeness structure in Figure 1(b) based on the interactions in Figure 1(a) . This is reasonable as the receptive field tends to be exponentially larger in the higher orders [16,24].…”

Section: Introductionmentioning

confidence: 73%

Geometric Interaction Augmented Graph Collaborative Filtering

Zhang¹,

Li²,

Wang³

et al. 2022

Preprint

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Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests. Conventionally, the embeddings of users and items are defined in the Euclidean spaces, along with the propagation on the interaction graphs. Meanwhile, recent works point out that the high-order interactions naturally form up the tree-likeness structures, which the hyperbolic models thrive on. However, the interaction graphs inherently exhibit the hybrid and nested geometric characteristics, while the existing single geometry-based models are inadequate to fully capture such sophisticated topological patterns. In this paper, we propose to model the user-item interactions in a hybrid geometric space, in which the merits of Euclidean and hyperbolic spaces are simultaneously enjoyed to learn expressive representations. Experimental results on public datasets validate the effectiveness of our proposal.

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“…Let h 𝑖 and h 𝑗 denote the representations of node 𝑣 𝑖 and 𝑣 𝑗 , it can be formulated as 𝑔(𝑣 𝑖 , 𝑣 𝑗 ) = 𝑀𝐿𝑃 (h 𝑖 , h 𝑗 ). Link prediction have various applications such as friend recommendation on social media [153] and knowledge graph completion [10]. Graph Classification.…”

Section: Graph Analysis Tasksmentioning

confidence: 99%

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

Dai¹,

Zhao²,

Zhu³

et al. 2022

Preprint

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Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applications, including high-stakes scenarios such as financial analysis, traffic predictions, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society. For example, existing works demonstrate that attackers can fool the GNNs to give the outcome they desire with unnoticeable perturbation on training graph. GNNs trained on social networks may embed the discrimination in their decision process, strengthening the undesirable societal bias. Consequently, trustworthy GNNs in various aspects are emerging to prevent the harm from GNN models and increase the users' trust in GNNs. In this paper, we give a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability. For each aspect, we give the taxonomy of the related methods and formulate the general frameworks for the multiple categories of trustworthy GNNs. We also discuss the future research directions of each aspect and connections between these aspects to help achieve trustworthiness.

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Graph Neural Networks for Friend Ranking in Large-scale Social Platforms

Cited by 46 publications

References 30 publications

Is Homophily a Necessity for Graph Neural Networks?

Is Homophily a Necessity for Graph Neural Networks?

Geometric Interaction Augmented Graph Collaborative Filtering

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

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