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

Multiway Autoregressive Network: A Dynamic Graph Representation Framework for Temporal Link Prediction.

A novel theoretical model, namely the multiway autoregressive (MARS) model, which characterizes multiple evolutionary paths by capturing dependencies within and across two core factors underlying diverse evolutionary mechanisms is proposed, which develops a general DGNN framework, a multiway autoregressive network (MAN).

Spectradyn: an adaptive spectral-temporal framework for dynamic graph link prediction

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.

RegRole: Regularized Role Detection and Prediction in Temporal Dynamic Networks

This paper introduces a dynamic role discovery technique in temporal dynamic networks, utilizing temporally regularized Non-negative Matrix Factorization (NMF). Our technique differs from existing dynamic role analysis techniques by creating a consistent set of roles across all time periods, as well as a universal transition matrix that describes the probability of transitioning between roles. We also apply a regularization penalty to ensure that role membership does not change dramatically between time periods making our model more robust against real-world noise. We test our data on five real-world and one synthetically simulated dataset using both engineered and automatically generated features. We demonstrate that the proposed regularized role detection method, for appropriate regularization weight parameter reduces prediction errors compared to other techniques. Furthermore, trace analysis of the transition matrices indicates that our method yields a more stable system, that is, individuals are more likely to stay in their roles with fewer arbitrary transitions. Our model learns time-aligned roles, captures behavioral transitions over time, and scales efficiently to large and sparse graphs.

Learning semantics-enhanced temporal graph embedding for event prediction

Event prediction is an important task for applications such as risk assessment and resource allocation. However, it is non-trivial to model the past due to the complexity and heterogeneity of available data. In recent years, Graph Neural Networks (GNNs) have shown flexibility in processing different forms of data and have been applied for event forecasting because events can be formalized as temporal graph. Although the GNN-based methods have achieved impressive performance, they still have limitations in event prediction task. First, different types of data such as graph and text have not been fully utilized and the contextual information has not been effectively exploited. Secondly, GNNs typically take individual snapshots of temporal graphs as input, which restricts their ability to learn long-term dependencies. To address these problems, we propose a temporal graph embedding learning model, named TGELN. It consists of two major modules, data processor and event predictor. The former processes records of event databases and creates an event-based temporal graph. The latter adopts a novel semantics-enhanced graph neural network that incorporates semantic information as well as other text features to learn temporal graph embedding for event prediction. Extensive experiments are conducted on eight datasets of events between countries, and the results demonstrate the superiority of the proposed model compared to the baseline methods.

Decorated graphons for temporal network estimation

The framework gives a nonparametric baseline for dynamic network analysis with explicit convergence guarantees and establishes nonparametric convergence rates in both block-model and Holder-smooth regimes.