# TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs

arXiv: https://arxiv.org/abs/2203.03564 · HTML: https://mlsanity.com/paper/2203.03564/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 7 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://mlsanity.com/paper/1802.08773/) (2018)
- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [Efficient Graph Generation with Graph Recurrent Attention Networks](https://mlsanity.com/paper/1910.00760/) (2019)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Intensity-Free Learning of Temporal Point Processes](https://mlsanity.com/paper/1909.12127/) (2019)
- [NetGAN: Generating Graphs via Random Walks](https://mlsanity.com/paper/1803.00816/) (2018)
- [Fully Neural Network based Model for General Temporal Point Processes](https://mlsanity.com/paper/1905.09690/) (2019)
- [Motifs in Temporal Networks](https://mlsanity.com/paper/1612.09259/) (2016)
- [Multi-Objective De Novo Drug Design with Conditional Graph Generative Model](https://mlsanity.com/paper/1801.07299/) (2018)
- [GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation](https://mlsanity.com/paper/2001.08184/) (2020)
- [Correction of Automatic Speech Recognition with Transformer Sequence-to-sequence Model](https://mlsanity.com/paper/1910.10697/) (2019)
- [A Tutorial on Hawkes Processes for Events in Social Media](https://mlsanity.com/paper/1708.06401/) (2017)
- [An Introduction to Temporal Graphs: An Algorithmic Perspective](https://mlsanity.com/paper/1503.00278/) (2015)
- [Generating a Doppelganger Graph: Resembling but Distinct](https://mlsanity.com/paper/2101.09593/) (2021)

## Built on by (most cited first, 18 of 18)

- [Global Counterfactual Explainer for Graph Neural Networks](https://mlsanity.com/paper/2210.11695/) (2022)
- [Mirage: Model-Agnostic Graph Distillation for Graph Classification](https://mlsanity.com/paper/2310.09486/) (2023)
- [GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets](https://mlsanity.com/paper/2306.03447/) (2023)
- [Efficient Dynamic Attributed Graph Generation](https://mlsanity.com/paper/2412.08810/) (2024)
- [A Survey on Temporal Graph Representation Learning and Generative Modeling](https://mlsanity.com/paper/2208.12126/) (2022)
- [SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds](https://mlsanity.com/paper/2305.03883/) (2023)
- [A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities](https://mlsanity.com/paper/2505.04461/) (2025)
- [Self-Evolving Agents as Dynamic Graph Transformation: A Survey and New Perspective](https://mlsanity.com/paper/2608.18104/) (2026)
- [Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces](https://mlsanity.com/paper/2401.01243/) (2024)
- [A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation](https://mlsanity.com/paper/2412.15582/) (2024)
- [A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History](https://mlsanity.com/paper/2501.06485/) (2025)
- [GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning](https://mlsanity.com/paper/2507.03267/) (2025)
- [Robust Training of Temporal GNNs using Nearest Neighbours based Hard Negatives](https://mlsanity.com/paper/2402.09239/) (2024)
- [Quality Measures for Dynamic Graph Generative Models](https://mlsanity.com/paper/2503.01720/) (2025)
- [GSHOT: Few-shot Generative Modeling of Labeled Graphs](https://mlsanity.com/paper/2306.03480/) (2023)
- [A Transfer Framework for Enhancing Temporal Graph Learning in Data-Scarce Settings](https://mlsanity.com/paper/2503.00852/) (2025)
- [Efficient Learning-based Graph Simulation for Temporal Graphs](https://mlsanity.com/paper/2510.05569/) (2025)
- [When Can You Correct Distribution Drift in Temporal Graph Generation? A Sharpening--Drift Tension and an Impossibility for Observation-Based Correction](https://mlsanity.com/paper/2607.24662/) (2026)

## Abstract (arXiv)

There has been a recent surge in learning generative models for graphs. While impressive progress has been made on static graphs, work on generative modeling of temporal graphs is at a nascent stage with significant scope for improvement. First, existing generative models do not scale with either the time horizon or the number of nodes. Second, existing techniques are transductive in nature and thus do not facilitate knowledge transfer. Finally, due to relying on one-to-one node mapping from source to the generated graph, existing models leak node identity information and do not allow up-scaling/down-scaling the source graph size. In this paper, we bridge these gaps with a novel generative model called TIGGER. TIGGER derives its power through a combination of temporal point processes with auto-regressive modeling enabling both transductive and inductive variants. Through extensive experiments on real datasets, we establish TIGGER generates graphs of superior fidelity, while also being up to 3 orders of magnitude faster than the state-of-the-art.