GTEA learns node representations in temporal interaction graphs.
problem Inductive representation learning on temporal interaction graphs.
method Integrates sequence model with time encoder and self-attention scheme for edge and node embeddings.
result GTEA learns comprehensive node representations capturing temporal and structural characteristics.
Novel network models capture social interaction features like reciprocity and community.
problem Modeling temporal social interaction data with reciprocity and community structure.
method Self-exciting Hawkes point processes with conditional intensity function.
result Proposed model outperforms competing approaches for link prediction.
The paper introduces metrics for robust unsupervised learning of vehicle interactions.
problem Robust representation learning of temporal dynamic interactions in robotics.
method Geometric approach using Procrustes distance and optimal transport for comparing interaction distributions.
result Metrics for assessing stability and comparing interaction learning algorithms.
New graph kernels capture spatio-temporal interactions.
problem Lack of justified spatio-temporal graph kernels for graph problems.
method Derive graph kernels via SPDEs for spatio-temporal modelling.
result Non-separable spatio-temporal graph kernels outperform existing ones.
Model infers temporal connections in dynamic graphs from node interactions.
problem Challenges in reasoning about evolving graphs, especially with human-specified edges.
method Temporal point processes and variational autoencoders with bilinear interactions.
result Model outperforms baselines and infers semantically interpretable connections.
Dynamic model clusters interactions over time, improving prediction.
problem Sparse, evolving interaction graphs with temporal dynamics.
method Structured, nonparametric edge-exchangeable model for dynamic clustering.
result Improved predictive performance compared to static models.
Predict sepsis early from EHR data with aggregated clinical events.
problem Predict sepsis from clinical data in EHR with temporal interactions.
method Aggregates heterogeneous clinical events, captures temporal interactions with LSTM.
result Achieved high utility score (0.321) in PhysioNet/Computing in Cardiology Challenge 2019.
iCITRIS learns causal variables from interactive systems with instantaneous effects.
problem Identifying causal variables from temporal sequences with instantaneous effects.
method iCITRIS method for causal representation learning that handles instantaneous effects in intervened temporal sequences.
result iCITRIS accurately identifies causal variables and their causal graph from three interactive system datasets.
We consider the problem of estimating the topology of spatial interactions in a discrete state, discrete time spatio-temporal graphical model where the interactions affect the temporal evolution of each agent in a network. Among other models, the susceptible, infected, recovered (SIR) model for interaction events fal…
The paper models user-advertiser interactions using point processes.
problem Causal inference problems in user-advertiser interaction.
method Temporal marked point processes and neural point processes.
result Neural point processes as practical solutions.
TCFimt forecasts causal effects of multiple interventions from individual data.
problem Estimating causal effects of temporal multi-interventions from individual data.
method TCFimt uses adversarial tasks in seq2seq framework to alleviate bias and contrastive learning to decouple effects.
result TCFimt outperforms state-of-the-art methods in predicting future outcomes and choosing optimal treatments.
Estimates spatio-temporal data with satellite NO2 concentrations using Yule-Walker equations.
problem Estimating large spatio-temporal autoregressions with unknown spatial interactions.
method Sparse generalized Yule-Walker estimation, penalized regression, spatial and temporal dependence.
result Strong forecast improvements and evidence of spatial interactions in NO2 satellite data.
In evolving complex systems such as air traffic and social organizations, collective effects emerge from their many components' dynamic interactions. While the dynamic interactions can be represented by temporal networks with nodes and links that change over time, they remain highly complex. It is therefore often neces…
We model GitHub interactions as a temporal knowledge graph for software engineering questions.
problem Insufficient performance of existing temporal models on extrapolated queries and time prediction.
method Introduced an extension to current temporal models using relative temporal information.
result Improved performance on extrapolated queries and time prediction.
Paper introduces new graph concepts for better modeling of temporal interactions.
problem Graph theory struggles to capture temporal and structural aspects of interactions.
method Generalizes graph concepts to handle both temporal and structural aspects of interactions.
result Formalism allows direct modeling of interactions over time, similar to graph theory.
BrainCast predicts whole-brain fMRI time series from short scans.
problem Short scans reduce fMRI data quality and statistical power.
method Spatio-temporal forecasting framework for fMRI time series.
result BrainCast improves fMRI time series quality and prediction.
New model captures sparse, evolving multigraph structures.
problem Understanding sparse, evolving multigraph structures in dynamic interaction data.
method Dynamic nonparametric Bayesian model combining sparsity and clustering.
result Improved held-out likelihood and predictive performance.
Proposes HMHP for joint modeling of user-topic interactions.
problem Complex interactions between users, topics and time on social media.
method Hidden Markov Hawkes Process (HMHP) incorporating topical Markov Chains.
result HMHP outperforms state-of-the-art models in generalization and accuracy.
Model improves mortgage credit risk prediction with spatio-temporal machine learning.
problem Improving accuracy of default probabilities and loan portfolio loss distributions in mortgage credit risk.
method Combines tree-boosting with a latent spatio-temporal Gaussian process model.
result Predictive models outperform conventional methods due to non-linear and spatio-temporal effects.
RE-NET predicts future interactions in temporal knowledge graphs.
problem Predicting future facts in temporal knowledge graphs.
method Autoregressive architecture with recurrent event encoder and neighborhood aggregator.
result State-of-the-art performance on five public datasets.
Dynamic Vine Copulas detect and quantify time-varying higher-order interactions in multivariate systems.
problem Time-varying dependence in multivariate systems, including tail behavior, asymmetry, and conditional structure.
method Dynamic Vine Copulas (DVC) framework for estimating and diagnosing non-Gaussian dependence, using fixed-root-order C-vines and smooth parameter trajectories.
result DVC detects and quantifies time-varying higher-order interactions, distinguishing between pairwise and conditional dependence.
TIMeSynC combines financial service interactions for intent prediction.
problem Aligning and learning from multi-domain, multi-resolution sequences for accurate intent prediction.
method An encoder-decoder transformer model addressing sequence alignment, temporal dynamics, and dynamic/static sequence combination.
result Significant improvement in intent prediction over existing methods.
SeqFM models dynamic and sequential features for better predictive analytics.
problem Inadequate handling of sequential dependencies in existing FM-based models.
method Introduces SeqFM, a novel model that incorporates multi-view self-attention to model static, dynamic, and their interactions.
result SeqFM outperforms existing models in ranking, classification, and regression tasks on six large-scale datasets.
New models predict interactions in multilayer networks more accurately.
problem Predicting interactions in multilayer networks.
method Introduce two stochastic block models for multilayer and temporal networks; develop scalable algorithms for inferring parameters.
result Modeling all layers simultaneously results in more accurate link prediction.
A new method detects financial fraud using graph transformers.
problem Detecting fraudulent transactions in financial data.
method Spatial-Temporal-Aware Graph Transformer (STA-GT) integrating GNNs and transformers.
result STA-GT outperforms general GNN models on financial fraud detection.
Credit risk analysis improved with a joint model for spatial and temporal effects.
problem Predicting borrower's time-to-event with spatial and temporal covariates.
method Spatio-Temporal Joint Model (STJM) using Bayesian hierarchical approach and INLA.
result Spatial effects improve joint model performance, but spatio-temporal interactions have less impact.
We describe and extract time-ordered multibody interactions from complex systems.
problem Complex systems with temporal and multibody dependencies.
method Decompose multivariate Markov chains into time-ordered multibody interactions. Algorithm to extract interactions from data. Measure complexity of interaction ensembles.
result Robust and efficient algorithm to infer time-ordered multibody interactions from data.
Game-theoretic model captures investor interactions for stock price forecasting.
problem Complex market dynamics driving stock price movements.
method Game-theoretic modeling of heterogeneous investor interactions in a dynamic graph structure.
result Our method outperforms state-of-the-art stock price forecasting methods.
New algorithm predicts spatio-temporal events with improved accuracy.
problem Non-stationary spatio-temporal prediction on dense and sparse sequences.
method Probabilistic approach using point processes and self-organizing decision trees.
result Significant performance improvements over baseline and state-of-the-art methods.
SG-NTF completes HDI tensors with spectral mapping and spatio-temporal gating.
problem High-dimensional and incomplete tensor completion.
method Spectra-Guided Neural Tucker Factorization (SG-NTF) with Spatio-Temporal Co-Gating (STCG).
result Maintains competitive completion accuracy with parameter efficiency.
Proposes a new model for complex multivariate event data.
problem Modeling complex multivariate event data with spatio-temporal dynamics.
method Integrates spatial information into latent state evolution through learned temporal and spatial decay dynamics.
result Successfully recovers sensible temporal and spatial intensity structure in multivariate spatio-temporal point patterns.
ASARS integrates temporal dynamics from CF into session-based RNN for better personalized recommendations.
problem Discarding long-term data across sessions in session-based RNNs.
method ASARS framework that combines attentional network and inter-session temporal dynamic model.
result ASARS improves personalized recommendation performance on four real datasets.
Study higher-order interactions in networks, proposing link prediction as a new benchmark.
problem Understanding higher-order interactions in complex systems.
method Temporal analysis of 19 datasets, focusing on interactions involving more than two nodes.
result Higher-order interactions are consistent across different systems, with tie strength and edge density influencing their presence.
Two-stream model recognizes affect from audio and video.
problem Human affect recognition in real-world settings.
method Two-stream aural-visual analysis model with separate audio and visual processing.
result Model achieves promising results on Aff-Wild2 database.
The stochastic block model (SBM) is a flexible probabilistic tool that can be used to model interactions between clusters of nodes in a network. However, it does not account for interactions of time varying intensity between clusters. The extension of the SBM developed in this paper addresses this shortcoming through a…
Study builds complex network from multimodal physiological data.
problem Understanding dynamic interactions in biological systems.
method Network-based multimodal data fusion using recurrence plots and temporal metrics.
result Model accurately characterizes emotional states through physiological responses.
Modeling solar ramping events with spatio-temporal point processes.
problem Predicting solar ramping events influenced by weather conditions.
method Novel spatio-temporal categorical point process model.
result Effective modeling of spatio-temporal correlations in solar ramping events.
FreST Loss decorrelates spatio-temporal dependencies in graph signals.
problem Complex spatio-temporal dependencies in graph-structured signals are not well captured by standard forecasting models.
method FreST Loss extends supervision to the joint spatio-temporal spectrum using Joint Fourier Transform (JFT).
result FreST Loss reduces estimation bias and improves forecasting accuracy on real-world datasets.
The study combines social interaction data into a single network, identifying stable groups of chimpanzees.
problem Identifying stable groups of chimpanzees based on social interactions over time.
method Network representation, weighted proximity weights, principled loss function, statistical tests.
result The approach detects stable groups of chimpanzees that stay related for a significant length of time.
New method learns dynamic node embeddings from temporal networks.
problem Temporal networks' temporal information is often ignored or approximated.
method Continuous-time dynamic network embeddings (CTDNEs) using temporal walks.
result CTDNEs achieve an average gain of 11.9% in AUC across all methods and graphs.
Survey on modeling event sequences through temporal processes.
problem Modeling phenomena with sequences of events over continuous time.
method Probabilistic models based on point processes, categorized into simple, marked, and spatio-temporal.
result Analysis of existing approaches and their applicability to prediction and modeling.
CoI framework models clinical feature interactions, revealing temporal dependencies and enhancing transparency.
problem Capturing latent, time-varying dependencies among clinical features in time-series data.
method Chain-of-Influence (CoI) framework constructs an explicit, time-unfolded graph of feature interactions.
result Achieves state-of-the-art predictive performance (AUROC of 0.960 on CKD progression and 0.950 on ICU mortality).
Paper introduces TSSDMN for modeling dynamic multilayer networks.
problem Capturing temporal and cross-layer dynamics in multilayer networks.
method Tensor State Space Model (TSSDMN) using symmetric Tucker decomposition.
result TSSDMN uniquely captures temporal dynamics within and across layers.
JODIE learns dynamic user-item embeddings from interactions, outperforming existing methods.
problem Modeling dynamic user-item interactions for accurate future predictions.
method JODIE uses coupled recurrent models with update, projection, and prediction components, and a novel t-Batch algorithm.
result JODIE outperforms state-of-the-art methods by up to 22.4% on future interaction and state change prediction tasks.
ESN enhances forecasting for nonlinear spatio-temporal data.
problem Forecasting nonlinear spatio-temporal processes efficiently.
method Enhanced ESN machine learning approach.
result Reasonable uncertainty quantification for long-lead forecasts.
This study prioritizes temporal resolution over spatial in energy systems models due to higher influence.
problem The impact of spatial and temporal resolution on energy system models.
method Global sensitivity analysis to compare structural aspects, spatial, and temporal resolution.
result Temporal resolution has a higher influence on all results parameters compared to spatial resolution.
MRA-BGCN improves traffic forecasting accuracy through complex graph interactions.
problem Challenging traffic forecasting due to spatial-temporal dependency and uncertainty.
method Proposes MRA-BGCN, a deep learning model that uses bicomponent graph convolution and multi-range attention.
result MRA-BGCN achieves state-of-the-art results on real-world traffic datasets.
Method learns to predict agent interactions from partial observations.
problem Predicting interactions between multiple agents from incomplete data.
method Graph-Structured Variational Recurrent Neural Network (Graph-VRNN) trained end-to-end.
result Graph-VRNN outperforms baselines on sports datasets.