# Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders

arXiv: https://arxiv.org/abs/1911.05465 · HTML: https://mlsanity.com/paper/1911.05465/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2019.

## Built on by (most cited first, 13 of 13)

- [On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs](https://mlsanity.com/paper/2107.01495/) (2021)
- [Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining](https://mlsanity.com/paper/2403.04780/) (2024)
- [HGWaveNet: A Hyperbolic Graph Neural Network for Temporal Link Prediction](https://mlsanity.com/paper/2304.07302/) (2023)
- [How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation](https://mlsanity.com/paper/2201.04672/) (2022)
- [MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields](https://mlsanity.com/paper/2302.02978/) (2023)
- [FedGraphNN: A Federated Learning System and Benchmark for Graph Neural Networks](https://mlsanity.com/paper/2104.07145/) (2021)
- [Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem](https://mlsanity.com/paper/2401.14876/) (2024)
- [Fast Graph Neural Tangent Kernel via Kronecker Sketching](https://mlsanity.com/paper/2112.02446/) (2021)
- [AdaGNN: A multi-modal latent representation meta-learner for GNNs based on AdaBoosting](https://mlsanity.com/paper/2108.06452/) (2021)
- [Filling in the Gaps: Efficient Event Coreference Resolution using Graph Autoencoder Networks](https://mlsanity.com/paper/2310.11965/) (2023)
- [Informative Subgraphs Aware Masked Auto-Encoder in Dynamic Graphs](https://mlsanity.com/paper/2409.09262/) (2024)
- [Sharpness-aware Federated Graph Learning](https://mlsanity.com/paper/2512.16247/) (2025)
- [Aligning the Unseen in Attributed Graphs: Interplay between Graph Geometry and Node Attributes Manifold](https://mlsanity.com/paper/2601.22806/) (2026)

## Abstract (arXiv)

While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i.e., social relations. Ideal edge semantics should not only show that two users are connected, but also why they know each other and what they share in common. However, relations in social networks are often hard to profile, due to noisy multi-modal signals and limited user-generated ground-truth labels. In this work, we aim to develop a unified and principled framework that can profile user relations as edge semantics in social networks by integrating multi-modal signals in the presence of noisy and incomplete data. Our framework is also flexible towards limited or missing supervision. Specifically, we assume a latent distribution of multiple relations underlying each user link, and learn them with multi-modal graph edge variational autoencoders. We encode the network data with a graph convolutional network, and decode arbitrary signals with multiple reconstruction networks. Extensive experiments and case studies on two public DBLP author networks and two internal LinkedIn member networks demonstrate the superior effectiveness and efficiency of our proposed model.