# DyLink2Vec: Effective Feature Representation for Link Prediction in Dynamic Networks

arXiv: https://arxiv.org/abs/1804.05755 · HTML: https://mlsanity.com/paper/1804.05755/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2018.

## Built on by (most cited first, 9 of 9)

- [A Survey on Embedding Dynamic Graphs](https://mlsanity.com/paper/2101.01229/) (2021)
- [On the Equivalence Between Temporal and Static Graph Representations for Observational Predictions](https://mlsanity.com/paper/2103.07016/) (2021)
- [Efficient Dynamic Graph Representation Learning at Scale](https://mlsanity.com/paper/2112.07768/) (2021)
- [A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature](https://mlsanity.com/paper/2401.02542/) (2024)
- [Direct Embedding of Temporal Network Edges via Time-Decayed Line Graphs](https://mlsanity.com/paper/2210.00032/) (2022)
- [SnapE -- Training Snapshot Ensembles of Link Prediction Models](https://mlsanity.com/paper/2408.02707/) (2024)
- [Learning distributed representations with efficient SoftMax normalization](https://mlsanity.com/paper/2303.17475/) (2023)
- [NODDLE: Node2vec based deep learning model for link prediction](https://mlsanity.com/paper/2305.16421/) (2023)
- [ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding](https://mlsanity.com/paper/2310.04865/) (2023)

## Abstract (arXiv)

The temporal dynamics of a complex system such as a social network or a communication network can be studied by understanding the patterns of link appearance and disappearance over time. A critical task along this understanding is to predict the link state of the network at a future time given a collection of link states at earlier time points. In existing literature, this task is known as link prediction in dynamic networks. Solving this task is more difficult than its counterpart in static networks because an effective feature representation of node-pair instances for the case of dynamic network is hard to obtain. To overcome this problem, we propose a novel method for metric embedding of node-pair instances of a dynamic network. The proposed method models the metric embedding task as an optimal coding problem where the objective is to minimize the reconstruction error, and it solves this optimization task using a gradient descent method. We validate the effectiveness of the learned feature representation by utilizing it for link prediction in various real-life dynamic networks. Specifically, we show that our proposed link prediction model, which uses the extracted feature representation for the training instances, outperforms several existing methods that use well-known link prediction features.