Learning semantics-enhanced temporal graph embedding for event prediction

GTIN: A Unified Framework for Joint Event and Time Prediction in Temporal Graphs

This work proposes a unified mathematical framework capable of capturing varying degrees of complexity across temporal graphs that is flexible and expressive enough to accommodate a wide range of network structures and temporal dynamics.

Leveraging contextual events on structure-aware next activity prediction

Predictive process monitoring aims at forecasting various aspects of running processes. Among the different tasks, next activity prediction represents the most extensively investigated. However, only a limited number of existing approaches explicitly encode contextual information, i.e., the environmental conditions in which the process is executed, typically modeled through event log attributes or aggregated measures. In this paper, an approach based on the concept of Instance Graphs is introduced. To incorporate contextual process instances, several encoding strategies are proposed and evaluated by measuring their impact on prediction performance. For each encoding strategy, a set of prefix-Instance Graphs is generated and subsequently provided as input to a Graph Neural Network for the classification task. The proposed approach is evaluated on multiple real-world event logs, and the experimental results demonstrate that incorporating contextual process instances benefits prediction performance.

DyG-Pred: Spatial-Temporal Dynamic Graph Replay for Event Prediction With Gated Explicit-Implicit Graph Dependency Fusion

Event prediction based on dynamic graph modeling captures the evolving relationships among events, entities, and environments, providing effective support for intelligent risk warning, resource allocation, and decision-making, particularly in public safety. Existing dynamic graph methods generally assume a fixed number of events (nodes) across time, whereas real-world event graphs continuously evolve with newly emerging events. This limitation hinders the modeling of event evolution and the accurate discovery of implicit event dependencies. To address this issue, we propose DyG-Pred, a dynamic event prediction framework based on Dynamic Link-to-Graph Replay Tracking with explicit–implicit gated graph fusion. First, an explicit event graph is constructed by extracting event link schemas from the observational event graph to capture static semantic relationships among events. Second, a spatial-temporal replay mechanism progressively constructs an implicit event graph by jointly modeling the evolution of nodes and edges as the graph size dynamically increases. Third, an explicit–implicit gated graph fusion module adaptively integrates the heterogeneous graph representations through dynamic weighting for final event prediction. Experimental results demonstrate that the proposed replay mechanism effectively captures long-range implicit event dependencies. By adaptively fusing these dependencies with the stable semantic information of the explicit event graph, DyG-Pred achieves excellent event prediction performance while maintaining strong generalization across large-scale datasets from different domains.

Temporal Knowledge Graph Forecasting via Constrained Large Language Models

A Network-driven Framework for Public Event Forecasting via Dynamic Interaction Network Evolution

Effective public event forecasting is essential for intelligent service systems, enabling proactive risk management, adaptive resource allocation, and timely decision-making. In many real-world scenarios, the evolution of public events is driven by dynamic interactions among participants. Motivated by this observation, this paper proposes auto-ibDLM, a network-driven deep learning framework that represents events as dynamic interaction networks and predicts public event evolution through participant growth forecasting. The proposed framework adopts a hybrid representation learning strategy that first represents network evolution using network science-informed structural metrics and subsequently transforms the resulting structural feature vectors into compact and robust latent representations through an auto-learning layer. A GRU-based temporal forecasting module is then employed to capture temporal dependencies and predict future participant growth. Extensive experiments on 13 real-world public event datasets and two publicly available dynamic network datasets demonstrate that auto-ibDLM consistently outperforms representative state-of-the-art methods in both forecasting accuracy and generalization capability, achieving over 97% accuracy in public event forecasting. Comprehensive experimental analyses further validate the effectiveness of the proposed hybrid representation learning strategy and demonstrate its representation-level interpretability. These results indicate that auto-ibDLM provides an effective and practical solution for intelligent public event forecasting.

Statistical Feature Augmentation for Anomaly Detection in Dynamic Graphs

Dynamic networks are being applied in many domains, from social media to logistics systems, each with their own set of special characteristics. A model employed on this type of data must capture the duality between temporal/structural and feature-based information. Yet state-of-the-art deep learning models often struggle to learn especially short-term behavioral interaction signals, such as sender intensity or interaction inertia, directly from raw event streams. To address this gap, we propose a statistical feature augmentation method that explicitly encodes behavioral interaction statistics into the input feature space. We evaluate our proposed method on an anomaly detection task across three real-world datasets (Reddit, Wikipedia, MOOC) and seven models spanning both continuous-time and discrete-time architectures. As a baseline, we apply the same models trained on the original embeddings. Our results show, that augmentation consistently improves detection performance. Beyond performance, the enriched input enables fine-grained post-hoc analysis of behavioral importance, since each statistic occupies a dedicated input dimension. In particular, this work showcases a promising approach for merging classical network analysis with deep learning.