# Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs

arXiv: https://arxiv.org/abs/1508.00715 · HTML: https://mlsanity.com/paper/1508.00715/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2015.

## Built on by (most cited first, 5 of 5)

- [Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations](https://mlsanity.com/paper/2104.02667/) (2021)
- [Delving Into Deep Walkers: A Convergence Analysis of Random-Walk-Based Vertex Embeddings](https://mlsanity.com/paper/2107.10014/) (2021)
- [Network representation learning systematic review: ancestors and current development state](https://mlsanity.com/paper/2109.07583/) (2021)
- [DGEM: A New Dual-modal Graph Embedding Method in Recommendation System](https://mlsanity.com/paper/2108.04031/) (2021)
- [A Complex Network based Graph Embedding Method for Link Prediction](https://mlsanity.com/paper/2209.04884/) (2022)

## Abstract (arXiv)

We study the extent to which online social networks can be connected to open knowledge bases. The problem is referred to as learning social knowledge graphs. We propose a multi-modal Bayesian embedding model, GenVector, to learn latent topics that generate word and network embeddings. GenVector leverages large-scale unlabeled data with embeddings and represents data of two modalities---i.e., social network users and knowledge concepts---in a shared latent topic space. Experiments on three datasets show that the proposed method clearly outperforms state-of-the-art methods. We then deploy the method on AMiner, a large-scale online academic search system with a network of 38,049,189 researchers with a knowledge base with 35,415,011 concepts. Our method significantly decreases the error rate in an online A/B test with live users.