# Gravity-Inspired Graph Autoencoders for Directed Link Prediction

arXiv: https://arxiv.org/abs/1905.09570 · HTML: https://mlsanity.com/paper/1905.09570/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 28 of 28)

- [Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities](https://mlsanity.com/paper/2506.18019/) (2025)
- [Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction](https://mlsanity.com/paper/2202.00961/) (2022)
- [Directed Graph Auto-Encoders](https://mlsanity.com/paper/2202.12449/) (2022)
- [Explainability in Music Recommender Systems](https://mlsanity.com/paper/2201.10528/) (2022)
- [Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders](https://mlsanity.com/paper/2108.01053/) (2021)
- [Sampling Enclosing Subgraphs for Link Prediction](https://mlsanity.com/paper/2206.12004/) (2022)
- [D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning](https://mlsanity.com/paper/2112.11734/) (2021)
- [Origin-Destination Network Generation via Gravity-Guided GAN](https://mlsanity.com/paper/2306.03390/) (2023)
- [DUPLEX: Dual GAT for Complex Embedding of Directed Graphs](https://mlsanity.com/paper/2406.05391/) (2024)
- [Recommending Related Products Using Graph Neural Networks in Directed Graphs](https://mlsanity.com/paper/2211.11583/) (2022)
- [Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings](https://mlsanity.com/paper/2409.10452/) (2024)
- [On Generalization of Graph Autoencoders with Adversarial Training](https://mlsanity.com/paper/2107.02658/) (2021)
- [New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction](https://mlsanity.com/paper/2211.08972/) (2022)
- [Of Spiky SVDs and Music Recommendation](https://mlsanity.com/paper/2307.01212/) (2023)
- [A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network](https://mlsanity.com/paper/2409.13671/) (2024)
- [A Deep Latent Space Model for Graph Representation Learning](https://mlsanity.com/paper/2106.11721/) (2021)
- [Learning Asymmetric Embedding for Attributed Networks via Convolutional Neural Network](https://mlsanity.com/paper/2202.06307/) (2022)
- [Rethinking the positive role of cluster structure in complex networks for link prediction tasks](https://mlsanity.com/paper/2211.02396/) (2022)
- [Concept Prerequisite Relation Prediction by Using Permutation-Equivariant Directed Graph Neural Networks](https://mlsanity.com/paper/2312.09802/) (2023)
- [Multi-Class and Multi-Task Strategies for Neural Directed Link Prediction](https://mlsanity.com/paper/2412.10895/) (2024)
- [Rethinking Link Prediction for Directed Graphs](https://mlsanity.com/paper/2502.05724/) (2025)
- [Understanding the Design Principles of Link Prediction in Directed Settings](https://mlsanity.com/paper/2502.15008/) (2025)
- [To Share or Not to Share: Investigating Weight Sharing in Variational Graph Autoencoders](https://mlsanity.com/paper/2502.16724/) (2025)
- [Directed Link Prediction using GNN with Local and Global Feature Fusion](https://mlsanity.com/paper/2506.20235/) (2025)
- [SynCircuit: Automated Generation of New Synthetic RTL Circuits Can Enable Big Data in Circuits](https://mlsanity.com/paper/2509.00071/) (2025)
- [MGKAN: Predicting Asymmetric Drug-Drug Interactions via a Multimodal Graph Kolmogorov-Arnold Network](https://mlsanity.com/paper/2602.01751/) (2026)
- [GravityGraphSAGE: Link Prediction in Directed Attributed Graphs](https://mlsanity.com/paper/2605.09408/) (2026)
- [Code evolution for link prediction in complex networks](https://mlsanity.com/paper/2606.26132/) (2026)

## Abstract (arXiv)

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges. However, these models focus on undirected graphs and therefore ignore the potential direction of the link, which is limiting for numerous real-life applications. In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs. We present a new gravity-inspired decoder scheme that can effectively reconstruct directed graphs from a node embedding. We empirically evaluate our method on three different directed link prediction tasks, for which standard graph AE and VAE perform poorly. We achieve competitive results on three real-world graphs, outperforming several popular baselines.