Paper proposes a hybrid loss function for graph self-supervised learning.
problem Improving self-supervised representation learning for graphs.
method Hybrid loss function combining VICReg and HSIC.
result Hybrid loss function outperformed other methods in 4 out of 7 cases.
Traffic actors' future motion predicted using a hybrid graph model.
problem Predicting long-term behaviors of traffic actors in complex scenes.
method A hybrid graph model with nodes for actors and traffic elements, and edges for interaction types.
result TrafficGraphNet achieves state-of-the-art trajectory prediction accuracy.
A new hybrid GNN framework tackles oversmoothing in graph data.
problem Oversmoothing in graph convolutional networks limits their expressive power and generalization.
method Combines traditional GCN filters with band-pass filters defined via geometric scattering and introduces an attention framework.
result Improves expressive power and generalization of graph convolutional networks.
Register allocation, which is a crucial phase of a good optimizing compiler, relies on graph coloring. Hence, an efficient graph coloring algorithm is of paramount importance. In this work we try to learn a good heuristic for coloring interference graphs that are used in the register allocation phase. We aim to handle …
Hybrid LSTM-GNN model improves stock price prediction accuracy.
problem Enhancing stock price prediction accuracy using complex data.
method Combining LSTM for time series and GNN for graph-based analysis.
result Hybrid model reduces MSE by 10.6% compared to standalone LSTM.
Hybrid approach for large-scale network synchronization using KF and PTP.
problem Synchronization of large-scale networks in 5G.
method Combines Kalman Filtering and PTP for pairwise synchronization, and Factor Graphs and Belief Propagation for end-to-end synchronization.
result Error in offset estimation remains below 5 ns in simulations.
Hybrid model improves COVID-19 case forecasting accuracy.
problem Limited data and simplistic models for accurate prediction.
method Combining SEIR and RNN on a graph structure with local and edge features.
result Improves prediction accuracy on state-level COVID-19 data.
AI learns to design chemical processes efficiently.
problem Designing efficient chemical processes.
method Hierarchical reinforcement learning and graph neural networks.
result Quick learning in various decision spaces.
Hybrid model integrates GATv2 and geostatistics for better spatial prediction and uncertainty.
problem Accurate spatial prediction and uncertainty quantification in epidemiology and risk analysis.
method Integrates Graph Attention Network (GATv2) with model-based geostatistics (MBG) to capture relational and spatial dependencies.
result Hybrid model improves predictive accuracy and uncertainty quantification compared to standalone models.
Develops numerical methods for PDEs on hypergraphs and networks.
problem Solving PDEs on complex geometric structures like hypergraphs and networks.
method Hybrid finite element methods, focusing on hybrid discontinuous Galerkin methods.
result Derives numerical approximations for PDEs on hypergraphs and networks.
Mathematical analysis of Riemann surfaces and their moduli spaces using hybrid Laplacians.
problem Analyzing the asymptotics of Arakelov Green functions on Riemann surfaces near boundary of moduli spaces.
method Introducing hybrid Laplacian, solving hybrid Poisson equation, and defining hybrid Green functions.
result Layered description of asymptotics of Arakelov Green functions on Riemann surfaces near boundary of their moduli spaces.
New method sparsifies hybrid neural ODEs for better performance and stability.
problem Excessive latent states and interactions from mechanistic models lead to training inefficiency and over-fitting.
method Automatic state selection and structure optimization combining domain-informed graph modifications with data-driven regularization.
result Improved predictive performance and robustness with desired sparsity.
Hybrid model uses GNNs and pathfinding to optimize portfolio rebalancing costs.
problem Optimizing transaction costs in dynamic portfolio rebalancing.
method Combines GNNs for cost prediction and Dijkstra's algorithm for pathfinding.
result Significantly reduces transaction costs in financial asset graphs.
With higher-order neighborhood information of graph network, the accuracy of graph representation learning classification can be significantly improved. However, the current higher order graph convolutional network has a large number of parameters and high computational complexity. Therefore, we propose a Hybrid Lower …
Hybrid model combines neural networks and fluid dynamics for efficient, generalized simulations.
problem Inefficient and poor generalization of deep learning approximations of fluid dynamics.
method Combines graph neural networks with a differentiable PDE solver inside a neural network.
result Hybrid model generalizes well to new scenarios and outperforms both neural network and traditional methods.
Proposes a hybrid model for stock market report classification using graph neural networks.
problem Lack of unified node embeddings for heterogeneous graphs in text datasets.
method Transductive hybrid approach combining unsupervised node representation learning and supervised node classification/edge prediction.
result Demonstrates the model's ability to classify stock market technical analysis reports.
DanSmp predicts stock movement using a hybrid-relational MKG and dual attention networks.
problem Predicting stock price trends in volatile financial markets.
method Constructs a bi-typed MKG with hybrid-relations and uses DanSmp, a dual attention network, to learn momentum spillover signals.
result DanSmp improves stock prediction accuracy using the MKG.
New model enables AI to learn autonomously.
problem Enabling AI to acquire domain knowledge.
method Hybrid model combining ontology, knowledge graph, and Logic Neural Network.
result System can enrich and extend its knowledge.
Novel hybrid method for Bayesian network structure learning reduces computational time without sacrificing accuracy.
problem Bayesian network structure learning efficiency and accuracy trade-off.
method Partitioned PC (pPC), p-value adjacency thresholding (PATH), hybrid greedy initialization (HGI). result pHGS achieves significant computational reductions compared to the PC algorithm without sacrificing structure learning accuracy.
Paper introduces hybrid curves moduli space and canonical measures.
problem Asymptotic geometry of Riemann surfaces and their moduli spaces.
method Definition of hybrid curves and canonical measures.
result Continuous variation of canonical measures over hybrid curves moduli space.
Hybrid approach combines topic and graph embeddings for legal document clustering.
problem Challenges in classifying legal texts due to domain-specific language and limited labeled data.
method Combines unsupervised topic and graph embeddings with a supervised model.
result Improves clustering quality over text-only or graph-only embeddings.
Many classification problems involve data instances that are interlinked with each other, such as webpages connected by hyperlinks. Techniques for "collective classification" (CC) often increase accuracy for such data graphs, but usually require a fully-labeled training graph. In contrast, we examine how to improve the…
This paper generalizes graph representation for diverse data types.
problem Representing and querying hybrid data types in a unified way.
method Introducing a directed Tensor-Typed Multi-Graph with embeddings.
result Unified representation for visual, linguistic, and auditory data.
Paper forecasts stock correlations using a hybrid model combining graph neural networks and transformers.
problem Improving stock correlation forecasts for better portfolio management.
method Hybrid model combining Transformer and graph attention networks for forecasting residual deviations from historical data.
result The hybrid model reduces correlation forecasting error compared to rolling-window estimates.
NePTuNe combines neural and tensor methods for efficient link prediction in knowledge graphs.
problem Incomplete knowledge graphs, especially in link prediction.
method Hybrid model combining neural and tensor factorization methods.
result NePTuNe achieves state-of-the-art performance on FB15K-237 and near state-of-the-art on WN18RR datasets.
New method clusters multi-view data by squeezing hybrid knowledge.
problem Removal of redundant information and fusion of multi-view features.
method Low-rank subspace multi-view clustering with adaptive graph regularization.
result Our method outperforms state-of-the-art algorithms on multi-view benchmarks.
Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph compone…
The study examines discrete curvature notions on Cayley graphs of certain groups.
problem Understanding curvature in discrete settings for various groups.
method Introduced Right Angled Artin-Coxeter Hybrids (RAACHs) and derived curvatures of Cayley graphs.
result Addition of relators does not decrease weighted curvatures of Cayley graphs.
We define a hybrid between Ollvier and Bakry Emery curvature on graphs with dependence on a variable neighborhood. The hexagonal lattice is non-negatively curved under this new curvature notion. Bonnet-Myers diameter bounds and Lichnerowicz eigenvalue estimates follow from the standard arguments. We prove gradient esti…
A deep learning model for traffic forecasting in telecommunication networks.
problem Complex spatial-temporal dependency in traffic forecasting.
method Spatio-Temporal Hybrid Graph Convolutional Network (STHGCN) combining GRUs and hybrid-GCN.
result The proposed model outperforms classical and state-of-the-art methods.
Nowadays Knowledge Graphs constitute a mainstream approach for the representation of relational information on big heterogeneous data, however, they may contain a big amount of imputed noise when constructed automatically. To address this problem, different error detection methodologies have been proposed, mainly focus…
Algorithm reconstructs vertex positions in random geometric graphs with improved accuracy.
problem Reconstructing vertex positions in random geometric graphs with high accuracy.
method Hybrid of graph distances and short-range estimates based on common neighbors.
result Algorithm reconstructs vertex positions with error of O(nβ), improving over previous results. We present a hybrid method for latent information discovery on the data sets containing both text content and connection structure based on constrained low rank approximation. The new method jointly optimizes the Nonnegative Matrix Factorization (NMF) objective function for text clustering and the Symmetric NMF (SymNMF…
H-STGCN predicts traffic using navigation data and improves accuracy.
problem Limited accuracy in traffic forecasting due to lack of contextual information.
method Proposes H-STGCN, a hybrid spatio-temporal graph convolutional network.
result H-STGCN outperforms state-of-the-art methods in various metrics, especially for non-recurring congestion.
A new method uses ABC-SMC to infer hybrid models in bioprocesses with limited data.
problem Inference of hybrid models in bioprocesses with limited real data and high uncertainties.
method Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) and linear Gaussian dynamic Bayesian network (LG-DBN) for posterior distribution approximation.
result The method accelerates hybrid model inference and supports process monitoring and robust control.
Directed acyclic graphs are the basic representation of the structure underlying Bayesian networks, which represent multivariate probability distributions. In many practical applications, such as the reverse engineering of gene regulatory networks, not only the estimation of model parameters but the reconstruction of t…
We consider the two problems of predicting links in a dynamic graph sequence and predicting functions defined at each node of the graph. In many applications, the solution of one problem is useful for solving the other. Indeed, if these functions reflect node features, then they are related through the graph structure.…
This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering via total variation (TV) on graphs. The graphs encode both playlist proximity information and song similarity, using …
Neural GDEs improve graph prediction by blending discrete structures and differential equations.
problem Dynamic graph prediction challenges in irregularly sampled data.
method Continuous-depth graph neural networks (GNNs) with Neural GDEs.
result Neural GDEs enhance performance across various applications.
SJDs unify masked, continuous, and hybrid diffusion models.
problem Unified modeling of diffusion processes.
method Continuous-time Markov processes with token embeddings and hazard rates.
result Unified model recovers masked, continuous, and hybrid diffusion as limits.
A hybrid method for causal discovery in latent confounders.
problem Estimating linear non-Gaussian models with latent confounders.
method Hybrid approach combining FCI and ICA.
result Hybrid method uniquely identifies causal relations under mild assumptions.
D-Wave hybrid quantum-classical portfolio optimization shows classical decomposition is key, not quantum sampling.
problem Optimizing portfolios with constraints using hybrid quantum-classical methods.
method Operational decomposition audit of D-Wave's hybrid quantum-classical service on mean-variance-turnover instances.
result Classical decomposition and feasibility-aware reassembly are key to hybrid quantum-classical performance.
New hybrid model reduces MILP solver time by up to 26%.
problem Improving CPU-based MILP solver efficiency.
method Combines GNN and MLP for efficient CPU-based branching.
result Up to 26% reduction in solver running time compared to state-of-the-art methods.
New benchmarks show LLMs struggle with causal discovery.
problem Leveraging LLMs for causal discovery is unreliable due to dataset leakage.
method Developing science-grounded benchmarks and hybrid methods combining LLM predictions with statistical analysis.
result LLMs perform poorly on novel, real-world scientific studies compared to classical methods.
Proposes a novel graph representation learning framework using contrastive methods.
problem Graph representation learning for graph-structured data.
method Leverages a contrastive objective at the node level, generating two graph views by corruption and learning node representations by maximizing agreement.
result Consistently outperforms existing state-of-the-art methods on transductive and inductive learning tasks.
We report a scalable hybrid quantum-classical machine learning framework to build Bayesian networks (BN) that captures the conditional dependence and causal relationships of random variables. The generation of a BN consists of finding a directed acyclic graph (DAG) and the associated joint probability distribution of t…
Cold-start is a very common and still open problem in the Recommender Systems literature. Since cold start items do not have any interaction, collaborative algorithms are not applicable. One of the main strategies is to use pure or hybrid content-based approaches, which usually yield to lower recommendation quality tha…
Defines non-parabolic curves in spatial hybrid space with applications.
problem Defining and analyzing non-parabolic spatial hybrid framed curves.
method Definition and proof of existence and uniqueness theorem for non-parabolic spatial hybrid framed curves.
result Existence and uniqueness theorem for non-parabolic spatial hybrid framed curves.