# ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregation

arXiv: https://arxiv.org/abs/2105.02254 · HTML: https://mlsanity.com/paper/2105.02254/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2021.

## Built on by (most cited first, 30 of 39)

- [GraphRouter: A Graph-based Router for LLM Selections](https://mlsanity.com/paper/2410.03834/) (2024)
- [Federated Social Recommendation with Graph Neural Network](https://mlsanity.com/paper/2111.10778/) (2021)
- [Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey](https://mlsanity.com/paper/2302.07200/) (2023)
- [DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN](https://mlsanity.com/paper/2108.11883/) (2021)
- [Imitation Learning-based Implicit Semantic-aware Communication Networks: Multi-layer Representation and Collaborative Reasoning](https://mlsanity.com/paper/2210.16118/) (2022)
- [Graph World Model](https://mlsanity.com/paper/2507.10539/) (2025)
- [Multi-task Item-attribute Graph Pre-training for Strict Cold-start Item Recommendation](https://mlsanity.com/paper/2306.14462/) (2023)
- [TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support](https://mlsanity.com/paper/2306.13339/) (2023)
- [Multitask Active Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2401.13210/) (2024)
- [A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability](https://mlsanity.com/paper/2406.09031/) (2024)
- [Enhance Graph Alignment for Large Language Models](https://mlsanity.com/paper/2410.11370/) (2024)
- [Cross-Network Social User Embedding with Hybrid Differential Privacy Guarantees](https://mlsanity.com/paper/2209.01539/) (2022)
- [Causal Disentanglement with Network Information for Debiased Recommendations](https://mlsanity.com/paper/2204.07221/) (2022)
- [Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph](https://mlsanity.com/paper/2211.01830/) (2022)
- [Graph-based Alignment and Uniformity for Recommendation](https://mlsanity.com/paper/2308.09292/) (2023)
- [DiffGraph: An Automated Agent-driven Model Merging Framework for In-the-Wild Text-to-Image Generation](https://mlsanity.com/paper/2603.20470/) (2026)
- [Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation](https://mlsanity.com/paper/2407.18910/) (2024)
- [Intellectual Property in Graph-Based Machine Learning as a Service: Attacks and Defenses](https://mlsanity.com/paper/2508.19641/) (2025)
- [LEDA: Latent Semantic Distribution Alignment for Multi-domain Graph Pre-training](https://mlsanity.com/paper/2602.22660/) (2026)
- [Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering](https://mlsanity.com/paper/2306.15905/) (2023)
- [Causal Disentanglement for Regulating Social Influence Bias in Social Recommendation](https://mlsanity.com/paper/2403.03578/) (2024)
- [D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks](https://mlsanity.com/paper/2409.09079/) (2024)
- [LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNN](https://mlsanity.com/paper/2410.02826/) (2024)
- [CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?](https://mlsanity.com/paper/2512.11352/) (2025)
- [Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation](https://mlsanity.com/paper/2311.09577/) (2023)
- [End-to-End Graph Flattening Method for Large Language Models](https://mlsanity.com/paper/2409.14880/) (2024)
- [Structure-Guided Input Graph for GNNs facing Heterophily](https://mlsanity.com/paper/2412.01757/) (2024)
- [GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning](https://mlsanity.com/paper/2509.23616/) (2025)
- [Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming](https://mlsanity.com/paper/2510.18363/) (2025)
- [Differentially Private Graph Neural Network with Importance-Grained Noise Adaption](https://mlsanity.com/paper/2308.04943/) (2023)

## Abstract (arXiv)

Social recommendation aims to fuse social links with user-item interactions to alleviate the cold-start problem for rating prediction. Recent developments of Graph Neural Networks (GNNs) motivate endeavors to design GNN-based social recommendation frameworks to aggregate both social and user-item interaction information simultaneously. However, most existing methods neglect the social inconsistency problem, which intuitively suggests that social links are not necessarily consistent with the rating prediction process. Social inconsistency can be observed from both context-level and relation-level. Therefore, we intend to empower the GNN model with the ability to tackle the social inconsistency problem. We propose to sample consistent neighbors by relating sampling probability with consistency scores between neighbors. Besides, we employ the relation attention mechanism to assign consistent relations with high importance factors for aggregation. Experiments on two real-world datasets verify the model effectiveness.