# Scalable Link Prediction in Dynamic Networks via Non-Negative Matrix Factorization

arXiv: https://arxiv.org/abs/1411.3675 · HTML: https://mlsanity.com/paper/1411.3675/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2014.

## Built on by (most cited first, 21 of 21)

- [EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs](https://mlsanity.com/paper/2202.07648/) (2022)
- [FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding](https://mlsanity.com/paper/2103.00164/) (2021)
- [Spectral embedding for dynamic networks with stability guarantees](https://mlsanity.com/paper/2106.01282/) (2021)
- [Neural Predicting Higher-order Patterns in Temporal Networks](https://mlsanity.com/paper/2106.06039/) (2021)
- [A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations](https://mlsanity.com/paper/2204.05885/) (2022)
- [Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution](https://mlsanity.com/paper/2207.00594/) (2022)
- [EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning](https://mlsanity.com/paper/2303.12341/) (2023)
- [ConTIG: Continuous Representation Learning on Temporal Interaction Graphs](https://mlsanity.com/paper/2110.06088/) (2021)
- [Temporal Network Embedding via Tensor Factorization](https://mlsanity.com/paper/2108.09837/) (2021)
- [Robust Dynamic Network Embedding via Ensembles](https://mlsanity.com/paper/2105.14557/) (2021)
- [ConvDySAT: Deep Neural Representation Learning on Dynamic Graphs via Self-Attention and Convolutional Neural Networks](https://mlsanity.com/paper/2106.11430/) (2021)
- [ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding](https://mlsanity.com/paper/2310.04865/) (2023)
- [PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs](https://mlsanity.com/paper/2606.22914/) (2026)
- [Fast Graph Learning with Unique Optimal Solutions](https://mlsanity.com/paper/2102.08530/) (2021)
- [SPAN: Subgraph Prediction Attention Network for Dynamic Graphs](https://mlsanity.com/paper/2108.07776/) (2021)
- [TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor Aggregation](https://mlsanity.com/paper/2110.13596/) (2021)
- [WalkingTime: Dynamic Graph Embedding Using Temporal-Topological Flows](https://mlsanity.com/paper/2111.10928/) (2021)
- [Representing Social Networks as Dynamic Heterogeneous Graphs](https://mlsanity.com/paper/2209.03144/) (2022)
- [Factorized Fusion Shrinkage for Dynamic Relational Data](https://mlsanity.com/paper/2210.00091/) (2022)
- [Space-Invariant Projection in Streaming Network Embedding](https://mlsanity.com/paper/2303.06293/) (2023)
- [A source separation approach to temporal graph modelling for computer networks](https://mlsanity.com/paper/2303.15950/) (2023)

## Abstract (arXiv)

We propose a scalable temporal latent space model for link prediction in dynamic social networks, where the goal is to predict links over time based on a sequence of previous graph snapshots. The model assumes that each user lies in an unobserved latent space and interactions are more likely to form between similar users in the latent space representation. In addition, the model allows each user to gradually move its position in the latent space as the network structure evolves over time. We present a global optimization algorithm to effectively infer the temporal latent space, with a quadratic convergence rate. Two alternative optimization algorithms with local and incremental updates are also proposed, allowing the model to scale to larger networks without compromising prediction accuracy. Empirically, we demonstrate that our model, when evaluated on a number of real-world dynamic networks, significantly outperforms existing approaches for temporal link prediction in terms of both scalability and predictive power.