# GeniePath: Graph Neural Networks with Adaptive Receptive Paths

arXiv: https://arxiv.org/abs/1802.00910 · HTML: https://mlsanity.com/paper/1802.00910/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2018.

## Built on by (most cited first, 30 of 66)

- [Graph-Coupled Oscillator Networks](https://mlsanity.com/paper/2202.02296/) (2022)
- [PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations](https://mlsanity.com/paper/2108.01938/) (2021)
- [A Review on Graph Neural Network Methods in Financial Applications](https://mlsanity.com/paper/2111.15367/) (2021)
- [GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection](https://mlsanity.com/paper/2306.12251/) (2023)
- [Search to aggregate neighborhood for graph neural network](https://mlsanity.com/paper/2104.06608/) (2021)
- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Federated Graph Learning -- A Position Paper](https://mlsanity.com/paper/2105.11099/) (2021)
- [SPAGAN: Shortest Path Graph Attention Network](https://mlsanity.com/paper/2101.03464/) (2021)
- [Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2305.12398/) (2023)
- [Scalable deeper graph neural networks for high-performance materials property prediction](https://mlsanity.com/paper/2109.12283/) (2021)
- [Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting -- Full version](https://mlsanity.com/paper/2103.10760/) (2021)
- [Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs](https://mlsanity.com/paper/2106.06218/) (2021)
- [Graph Contrastive Learning for Anomaly Detection](https://mlsanity.com/paper/2108.07516/) (2021)
- [DAGAD: Data Augmentation for Graph Anomaly Detection](https://mlsanity.com/paper/2210.09766/) (2022)
- [Improving Graph Neural Networks with Learnable Propagation Operators](https://mlsanity.com/paper/2210.17224/) (2022)
- [pathGCN: Learning General Graph Spatial Operators from Paths](https://mlsanity.com/paper/2207.07408/) (2022)
- [UniGAD: Unifying Multi-level Graph Anomaly Detection](https://mlsanity.com/paper/2411.06427/) (2024)
- [Cost Sensitive GNN-based Imbalanced Learning for Mobile Social Network Fraud Detection](https://mlsanity.com/paper/2303.17486/) (2023)
- [SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning](https://mlsanity.com/paper/2406.11389/) (2024)
- [From Local to Global: Spectral-Inspired Graph Neural Networks](https://mlsanity.com/paper/2209.12054/) (2022)
- [Improving Conversational Recommendation System by Pretraining on Billions Scale of Knowledge Graph](https://mlsanity.com/paper/2104.14899/) (2021)
- [GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy](https://mlsanity.com/paper/2104.10569/) (2021)
- [PathMLP: Smooth Path Towards High-order Homophily](https://mlsanity.com/paper/2306.13532/) (2023)
- [Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling](https://mlsanity.com/paper/2406.11891/) (2024)
- [How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation](https://mlsanity.com/paper/2201.04672/) (2022)
- [Multitask Active Learning for Graph Anomaly Detection](https://mlsanity.com/paper/2401.13210/) (2024)
- [VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection](https://mlsanity.com/paper/2408.00513/) (2024)
- [Not All Neighbors Are Worth Attending to: Graph Selective Attention Networks for Semi-supervised Learning](https://mlsanity.com/paper/2210.07715/) (2022)
- [Union Subgraph Neural Networks](https://mlsanity.com/paper/2305.15747/) (2023)
- [Improving the Effective Receptive Field of Message-Passing Neural Networks](https://mlsanity.com/paper/2505.23185/) (2025)

## Abstract (arXiv)

We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data. In GeniePath, we propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively, where the former learns the importance of different sized neighborhoods, while the latter extracts and filters signals aggregated from neighbors of different hops away. Our method works in both transductive and inductive settings, and extensive experiments compared with competitive methods show that our approaches yield state-of-the-art results on large graphs.