M2E embeds multi-view multi-graph brain networks for better clustering.
problem Clustering brain networks from multiple views to understand disease mechanisms.
method Stack multi-graphs into tensors, use tensor techniques to leverage multi-view and multi-graph interactions.
result M2E outperforms existing methods on clustering brain networks.
Paper proposes a new method for population-wise matching of sulcal graphs.
problem Challenges in matching cortical fold variations across individuals.
method Population-wise multi-graph matching of sulcal graphs.
result Effectiveness of multi-graph matching in obtaining consistent labeling of sulcal basins.
Bayesian framework proves thresholds for multi-graph alignment feasibility.
problem Determining when multi-graph alignment is statistically possible.
method Developed a Bayesian estimation framework over metric spaces.
result Identified thresholds for Gaussian and sparse Erdős-Rényi models.
DMGE learns cross-domain user behavior embeddings using multi-graphs and GNNs.
problem Data sparsity in learning large-scale item embedding from individual domain data.
method Construct multi-graphs from users' behaviors across domains, use multi-graph neural networks to learn cross-domain representation.
result DMGE outperforms state-of-the-art embedding methods in various tasks.
New framework learns labels at both bag and graph levels.
problem Learning multi-label classifiers from multi-graph bags.
method Designing scoring functions and rank-loss objective for graph and bag levels; developing sub-gradient descent algorithm.
result Superior performance over state-of-the-art algorithms.
New method tightens convex relaxations for permutation matrix problems without lifting.
problem Optimizing quadratic problems over permutation matrices.
method Lifting-free convex relaxation approach.
result Proves at least as tight as existing methods and performs better experimentally.
Predict bike flow at station-level with multi-graph CNNs.
problem Fine-grained bike flow prediction for station-level management.
method Multi-graph convolutional neural network model.
result Reduces 25.1% and 17.0% prediction error in New York City and Chicago.
Friend recommendation system using heterogeneous edge embeddings.
problem Inadequate performance of existing network embedding techniques on multi-graph social networks.
method Proposes a method to mine network representation exploiting heterogeneity in multi-graphs.
result Outperforms state-of-the-art baselines on Hike's social network in terms of accuracy and user satisfaction.
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 improves risk bound for MTL with graph-dependent data.
problem Sub-optimal risk bound in multi-task learning with graph-dependent data.
method Proposes a new Bennett-type inequality and develops new Talagrand-type inequality and local fractional Rademacher complexity.
result Derives a sharper risk bound of O(nlogn). New method approximates partition function of graphical models using gauge functions and polynomials.
problem Computing the partition function of graphical models is computationally challenging.
method Combines gauge function technique with real stable polynomials to approximate partition function.
result Belief Propagation estimations in the sequence do not decrease and low-bound the partition function.
RECS improves graph embeddings by preserving network structure and stability.
problem Stable and accurate graph embeddings for multi-graph problems.
method RECS uses connection subgraphs and analogy to graphs with electrical circuits to learn stable node representations.
result RECS outperforms state-of-the-art algorithms by up to 36.85% on multi-label classification problems.
Proposes a method to improve urban spatiotemporal forecasting using multi-modal graph interaction.
problem Improving spatiotemporal forecasting in urban areas using graph convolution networks.
method Develops modality interaction mechanisms for multi-graph convolution networks to reduce generalization error.
result Proposed techniques improve prediction accuracy and model robustness compared to state-of-the-art baselines.
Framework combines HMM and MTGCN for spatiotemporal causal inference in clinical data.
problem Challenges in observing direct treatment effects in clinical domains.
method Integrates Hidden Markov Model and Multi Task and Multi Graph Convolutional Network for spatiotemporal data.
result Advances predictive causal inference by structurally adapting to spatiotemporal complexities.
The paper solves a genome assembly problem by recovering hidden Hamiltonian cycles from noisy measurements.
problem Inferring an unknown Hamiltonian cycle in a genome assembly problem from noisy edge measurements.
method Introduced a linear programming relaxation (F2F LP) to recover the hidden Hamiltonian cycle with high probability.
result A simple linear programming relaxation recovers the hidden Hamiltonian cycle with high probability as no∞. We prove that the ends of a properly immersed simply or one connected minimal surface in H(2)xR contained in a slab of height less than πof H(2)xR, are multi-graphs. When such a surface is embedded then the ends are graphs. When embedded and simply connected, it is an entire graph.
A novel multi-view spectral clustering model fuses and clusters data views.
problem Fusing and clustering multi-view data effectively.
method Simultaneously fuses and clusters views into a single graph.
result The proposed method outperforms existing techniques.
SF-GCN improves semi-supervised classification by fusing multi-view data structures.
problem Semi-supervised classification challenges due to multi-view data diversity and complexity.
method Structure fusion based on graph convolutional networks (SF-GCN) that balances specificity and commonality.
result SF-GCN outperforms state-of-the-art methods on citation networks datasets.
Unlike R3, the homogeneous spaces E(−1,τ) have a great variety of entire vertical minimal graphs. In this paper we explore conditions which guarantees that a minimal surface in E(−1,τ) is such a graph. More specifically: we introduce the definition of a generalized slab in $\mathbb{E…
Paper develops an online EM algorithm for graph signal inference from streaming data.
problem Joint inference and clustering of graph signals with non-white excitation.
method Mixture model with low-rank plus sparse prior, online EM algorithm.
result Proposed online EM algorithm converges to MAP solution.
Enhances graph classification with multiple graphs.
problem Improving graph classification accuracy with multiple graphs.
method Graph fusion embedding using encoder embedding.
result The method consistently improves classification accuracy for large vertex sets.
Paper predicts travel costs across regions using neural networks.
problem Predicting travel costs in sparse, stochastic OD matrices.
method Recurrent Multi-Graph Neural Networks (R-MGNN) for sparse, stochastic OD matrix forecasting.
result Framework effectively predicts future OD matrices without empty elements.
End-to-end trainable graph matching using improved combinatorial solvers.
problem Graph matching in deep learning.
method Combining deep learning with optimized combinatorial solvers.
result Advances state-of-the-art on deep graph matching benchmarks.
ALMGIG uses adversarial learning to generate and infer novel molecules efficiently.
problem Efficiently generating and inferring novel molecules using graph representations.
method Adversarial learning framework that avoids explicit graph isomorphism, using cycle-consistency loss and multi-graph Graph Isomorphism Network.
result ALMGIG more accurately learns the distribution over the space of molecules and efficiently searches the molecular space.
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches …
A new framework SIMBA improves graph classification performance on size-imbalanced datasets.
problem Size imbalance in graph classification leads to poor model performance.
method Energy-guided structural smoothing between head and tail graphs, re-weighting based on energy propagation.
result SIMBA outperforms existing methods in size-imbalanced graph classification tasks.
Matrix completion models are among the most common formulations of recommender systems. Recent works have showed a boost of performance of these techniques when introducing the pairwise relationships between users/items in the form of graphs, and imposing smoothness priors on these graphs. However, such techniques do n…
A new framework predicts stock movements using news sentiment and relational data.
problem Predicting stock prices from textual information is challenging due to market uncertainty and natural language complexity.
method Multi-Graph Recurrent Network (MGRN) combining textual sentiment from financial news and relational data.
result The model outperforms benchmarks in predicting stock movements.
LinkNBed learns entity and relationship representations across multiple graphs.
problem Jointly learn over multiple graphs and construct a unified graph.
method LinkNBed is a deep relational learning framework that learns entity and relationship representations across multiple graphs. It identifies entity linkage as a vital component and designs a novel objective to leverage it.
result Substantial improvements in link prediction and entity linkage over state-of-the-art relational learning approaches.
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem. Among the existing methods, Low-Rank Representation (LRR) based method is quite superior in terms of its effectiveness, intu…
The study explores planar Cayley graphs and their connection to Kleinian groups.
problem Understanding the structure of planar Cayley graphs and their relationship to Kleinian groups.
method Analyzing the properties of Cayley graphs embedded in a planar surface and their relation to Kleinian groups.
result A finitely generated planar Cayley graph can be constructed that does not belong to the class of Kleinian groups.
New 3D protein analysis methods improve accuracy.
problem Lack of suitable learning algorithms for protein data.
method Intrinsic-Extrinsic Convolution and Pooling for 3D protein structures.
result Outperforms state-of-the-art methods on protein analysis tasks.
In this paper we study constant mean curvature surfaces Σ in a product space, M2×R, where M2 is a complete Riemannian manifold. We assume the angle function $ν= \meta{N}{\partial_t}$ does not change sign on Σ. We classify these surfaces according to the infimum c(Σ) of the G…
A new graph neural network framework captures long-range interactions efficiently.
problem Efficiently modeling long-range interactions in graph neural networks for PDEs.
method Proposes a multi-level graph neural network framework using multipole methods.
result Captures interaction at all ranges with only linear complexity, learning discretization-invariant solution operators.
Predict stock movement by considering cross effects among stocks.
problem Challenges in predicting stock price movement due to cross effects among stocks.
method Multi-GCGRU framework combining GCN and GRU, encoding cross effects from financial domain knowledge and data-driven relationships.
result Our model outperforms other baselines in predicting stock movement.
Characterizes invariant and equivariant linear layers for graphs.
problem Maximal collection of invariant and equivariant linear layers for graphs.
method Characterization of all permutation invariant and equivariant linear layers for graphs.
result Dimension of linear layers for edge-value graph data is 2 and for k-tuples of nodes, it is the k-th and 2k-th Bell numbers.
A scalable method for graph partitioning and matching using Gromov-Wasserstein discrepancy.
problem Graph partitioning and matching for large-scale graphs.
method Recursive K-partition mechanism with proximal gradient algorithm. result Achieves a trade-off between accuracy and efficiency.
In recent years there has been an increased interest in statistical analysis of data with multiple types of relations among a set of entities. Such multi-relational data can be represented as multi-layer graphs where the set of vertices represents the entities and multiple types of edges represent the different relatio…
tf_geometric simplifies graph deep learning in TensorFlow.
problem Efficient graph deep learning in TensorFlow.
method Kernel libraries and infrastructures for GNNs.
result tf_geometric supports various graph tasks and provides efficient GNN models.
Enhances community detection in correlated networks with node attributes.
problem Community detection in multiple networks with correlated node attributes and edges.
method Introduced the correlated Contextual Stochastic Block Model (CSBM), developed a two-step matching procedure.
result Algorithm recovers exact node correspondence, enabling enhanced community detection.
We survey the status of some decision problems for 3-manifolds and their fundamental groups. This includes the classical decision problems for finitely presented groups (Word Problem, Conjugacy Problem, Isomorphism Problem), and also the Homeomorphism Problem for 3-manifolds and the Membership Problem for 3-manifold gr…
Optimal transport reformulates multiple quantile hedging problem.
problem Multiple quantile hedging problem in incomplete markets.
method Reformulated as Monge optimal transport problem, introduced Kantorovitch version, proved no duality gap.
result Multiple quantile hedging problem can be seen as semi-discrete optimal transport problem.
Solves four problems related to circle families in the plane.
problem Four basic problems of circle families in the plane.
method Solves all four basic problems of circle families in the plane.
result All four basic problems are solved.
Solves four problems related to sphere families in 3D space.
problem Four basic problems of sphere families in Euclidean 3-space.
method Solves all four basic problems of sphere families in Euclidean 3-space.
result All four basic problems are solved.
The paper solves optimal control problems for various convex sets using convex trigonometry.
problem Optimal control problems with 2D convex compact sets.
method Using convex trigonometry to derive extremals for various problems.
result Geodesics in multiple sub-Finsler problems are derived.
Explains eigenvalue and generalized eigenvalue problems with examples.
problem Eigenvalue and generalized eigenvalue problems.
method Introduction and examples from machine learning.
result Solutions to eigenvalue and generalized eigenvalue problems.
This paper solves the Christoffel problem in hyperbolic space and its equivalent on spheres.
problem Prescribing curvatures for convex hypersurfaces in hyperbolic space.
method Proving a full rank theorem to establish the existence of solutions.
result Existence of solutions to the Christoffel problem and its equivalent Nirenberg-Kazdan-Warner problem on spheres.
In the present paper, the primal-dual problem consisting of the investment risk minimization problem and the expected return maximization problem in the mean-variance model is discussed using replica analysis. As a natural extension of the investment risk minimization problem under only a budget constraint that we anal…