# Temporal Network Embedding via Tensor Factorization

arXiv: https://arxiv.org/abs/2108.09837 · HTML: https://mlsanity.com/paper/2108.09837/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 22 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [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)
- [Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec](https://mlsanity.com/paper/1710.02971/) (2017)
- [Novel methods for multilinear data completion and de-noising based on tensor-SVD](https://mlsanity.com/paper/1407.1785/) (2014)
- [Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization](https://mlsanity.com/paper/1411.3675/) (2014)
- [Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach](https://mlsanity.com/paper/1308.0723/) (2013)

## Built on by (most cited first, 3 of 3)

- [A Survey on Graph Representation Learning Methods](https://mlsanity.com/paper/2204.01855/) (2022)
- [Citation Trajectory Prediction via Publication Influence Representation Using Temporal Knowledge Graph](https://mlsanity.com/paper/2210.00450/) (2022)
- [Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks](https://mlsanity.com/paper/2109.01718/) (2021)

## Abstract (arXiv)

Representation learning on static graph-structured data has shown a significant impact on many real-world applications. However, less attention has been paid to the evolving nature of temporal networks, in which the edges are often changing over time. The embeddings of such temporal networks should encode both graph-structured information and the temporally evolving pattern. Existing approaches in learning temporally evolving network representations fail to capture the temporal interdependence. In this paper, we propose Toffee, a novel approach for temporal network representation learning based on tensor decomposition. Our method exploits the tensor-tensor product operator to encode the cross-time information, so that the periodic changes in the evolving networks can be captured. Experimental results demonstrate that Toffee outperforms existing methods on multiple real-world temporal networks in generating effective embeddings for the link prediction tasks.