# Topic-aware latent models for representation learning on networks

arXiv: https://arxiv.org/abs/2111.05576 · HTML: https://mlsanity.com/paper/2111.05576/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [struc2vec: Learning Node Representations from Structural Identity](https://mlsanity.com/paper/1704.03165/) (2017)
- [Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec](https://mlsanity.com/paper/1710.02971/) (2017)
- [GEMSEC: Graph Embedding with Self Clustering](https://mlsanity.com/paper/1802.03997/) (2018)
- [HARP: Hierarchical Representation Learning for Networks](https://mlsanity.com/paper/1706.07845/) (2017)
- [VERSE: Versatile Graph Embeddings from Similarity Measures](https://mlsanity.com/paper/1803.04742/) (2018)
- [NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization](https://mlsanity.com/paper/1906.11156/) (2019)
- [Is a Single Embedding Enough? Learning Node Representations that Capture Multiple Social Contexts](https://mlsanity.com/paper/1905.02138/) (2019)
- [BiasedWalk: Biased Sampling for Representation Learning on Graphs](https://mlsanity.com/paper/1809.02482/) (2018)
- [Node Embedding with Adaptive Similarities for Scalable Learning over Graphs](https://mlsanity.com/paper/1811.10797/) (2018)
- [Exponential Family Graph Embeddings](https://mlsanity.com/paper/1911.09007/) (2019)
- [Kernel Node Embeddings](https://mlsanity.com/paper/1909.03416/) (2019)

## Abstract (arXiv)

Network representation learning (NRL) methods have received significant attention over the last years thanks to their success in several graph analysis problems, including node classification, link prediction, and clustering. Such methods aim to map each vertex of the network into a low-dimensional space in a way that the structural information of the network is preserved. Of particular interest are methods based on random walks; such methods transform the network into a collection of node sequences, aiming to learn node representations by predicting the context of each node within the sequence. In this paper, we introduce TNE, a generic framework to enhance the embeddings of nodes acquired by means of random walk-based approaches with topic-based information. Similar to the concept of topical word embeddings in Natural Language Processing, the proposed model first assigns each node to a latent community with the favor of various statistical graph models and community detection methods and then learns the enhanced topic-aware representations. We evaluate our methodology in two downstream tasks: node classification and link prediction. The experimental results demonstrate that by incorporating node and community embeddings, we are able to outperform widely-known baseline NRL models.