Stochastic Event Prediction via Temporal Motif Transitions
arXiv:2603.05874v1 Announce Type: new Abstract: Networks of timestamped interactions arise across social, financial, and biological domains, where forecasting future events requires modeling both evolving topology and temporal ordering. Temporal link prediction methods typically frame the task as binary classification with negative sampling, discarding the sequential and correlated nature of real-world interactions. We introduce STEP (STochastic Event Predictor), a framework that reformulates temporal link prediction as a sequential forecasting problem in continuous time. STEP models event dynamics through discrete temporal motif transitions governed by Poisson processes, maintaining a set of open motif instances that evolve as new interactions arrive. At each step, the framework decides whether to initiate a new temporal motif or extend an existing one, selecting the most probable event via Bayesian scoring of temporal likelihoods and structural priors. STEP also produces compact, te
arXiv:2603.05874v1 Announce Type: new Abstract: Networks of timestamped interactions arise across social, financial, and biological domains, where forecasting future events requires modeling both evolving topology and temporal ordering. Temporal link prediction methods typically frame the task as binary classification with negative sampling, discarding the sequential and correlated nature of real-world interactions. We introduce STEP (STochastic Event Predictor), a framework that reformulates temporal link prediction as a sequential forecasting problem in continuous time. STEP models event dynamics through discrete temporal motif transitions governed by Poisson processes, maintaining a set of open motif instances that evolve as new interactions arrive. At each step, the framework decides whether to initiate a new temporal motif or extend an existing one, selecting the most probable event via Bayesian scoring of temporal likelihoods and structural priors. STEP also produces compact, temporal motif-based feature vectors that can be concatenated with existing temporal graph neural network outputs, enriching their representations without architectural modifications. Experiments on five real-world datasets demonstrate up to 21% average precision gains over state-of-the-art baselines in classification and 0.99 precision in next $k$ sequential forecasting, with consistently lower runtime than competing motif-aware methods.
Executive Summary
This article proposes a novel framework, STEP, for stochastic event prediction in temporal networks. By reformulating temporal link prediction as a sequential forecasting problem in continuous time, STEP models event dynamics through discrete temporal motif transitions governed by Poisson processes. The framework demonstrates significant precision gains over state-of-the-art baselines in classification and next k sequential forecasting, with improved runtime. STEP's ability to produce compact, temporal motif-based feature vectors also enhances the representation of existing temporal graph neural networks. The study's findings have substantial implications for various domains, including social, financial, and biological networks, where forecasting future events is crucial.
Key Points
- ▸ STEP reformulates temporal link prediction as a sequential forecasting problem in continuous time.
- ▸ The framework models event dynamics through discrete temporal motif transitions governed by Poisson processes.
- ▸ STEP achieves significant precision gains over state-of-the-art baselines in classification and next k sequential forecasting.
Merits
Strength in Sequential Modeling
STEP's sequential modeling approach effectively captures the evolving topology and temporal ordering of real-world interactions, leading to improved forecasting accuracy.
Compact Feature Vectors
STEP's ability to produce compact, temporal motif-based feature vectors enhances the representation of existing temporal graph neural networks without requiring architectural modifications.
Improved Runtime
STEP consistently demonstrates lower runtime compared to competing motif-aware methods, making it a more efficient solution for large-scale temporal network analysis.
Demerits
Complexity of Poisson Process Modeling
The framework's reliance on Poisson processes may introduce complexity in terms of parameter estimation and computation, particularly for large-scale networks.
Limited Generalizability
STEP's performance on specific datasets may not generalize well to other domains or networks with varying characteristics, requiring further evaluation and adaptation.
Expert Commentary
The proposed framework, STEP, represents a significant advancement in stochastic event prediction for temporal networks. By leveraging discrete temporal motif transitions and Bayesian scoring, STEP effectively captures the sequential and correlated nature of real-world interactions. The study's findings demonstrate the potential of STEP to outperform state-of-the-art baselines in classification and next k sequential forecasting, with improved runtime. However, the framework's complexity and limited generalizability require further investigation and adaptation to ensure its applicability across various domains and networks. Nevertheless, STEP's compact feature vectors and sequential modeling approach make it an attractive solution for enhancing the representation of existing temporal graph neural networks.
Recommendations
- ✓ Future research should focus on exploring the limitations of Poisson process modeling and adapting the framework to accommodate diverse network characteristics.
- ✓ The study's findings should be applied to real-world scenarios, such as social and financial network analysis, to demonstrate the practical implications of the proposed framework.
