Study flip graphs for surfaces of infinite type, finding uncountably many connected components.
problem Understanding relationships between triangulations of infinite type surfaces via flips.
method Associate triangulations to flip graphs and study sequences of simultaneous flips.
result Flip graphs for infinite type surfaces have uncountably many connected components.
Parabolic mapping class acts on curve graphs of infinite type surfaces.
problem Understanding parabolic isometries on curve graphs of infinite type surfaces.
method Fine curve graph tools to prove existence of parabolic isometries.
result Existence of parabolic isometries on graphs of curves of infinite type surfaces.
New method improves graph neural networks by considering different types of relations in sampling.
problem Current graph neural networks ignore relation types in biomedical graphs, leading to suboptimal performance.
method Proposes relation-dependent sampling for multi-relational graphs to balance relation frequency and importance.
result State-of-the-art graph neural networks achieve better accuracy and efficiency with relation-dependent sampling.
Study of flip graphs and their automorphism groups for infinite-type surfaces.
problem Understanding automorphism groups of flip graphs for infinite-type surfaces.
method Examined the relationship between mapping class groups and flip graphs for infinite-type surfaces.
result Extended mapping class groups are isomorphic to proper subgroups of automorphism groups of flip graphs.
LambdaNet infers TypeScript types using graph neural networks.
problem Automatic inference of TypeScript type annotations.
method Graph Neural Network for type dependency graph analysis.
result LambdaNet outperforms existing methods by 14%.
New examples of mixed-type zero-curvature graphs found.
problem Finding new examples of zero-curvature graphs in Lorentz-Minkowski space.
method Using Konderak's representation formula to construct entire zero-curvature graphs over specific planes.
result Existence of new types of entire zero-curvature graphs in mixed-type in Lorentz-Minkowski space.
Graph potentials link to topological QFTs, with computational methods.
problem Defining a topological quantum field theory using graph potentials.
method Using colored trivalent graphs and birational type to define a topological QFT.
result Graph potentials' birational type depends on the graph's homotopy type.
Study of infinite-type surfaces' automorphisms and graph structures.
problem Understanding automorphisms of infinite-type surfaces.
method Isomorphic mappings between extended mapping class groups and graph automorphism groups.
result Extended mapping class groups are isomorphic to graph automorphism groups.
The paper explores non-amenability in infinite-type surfaces and graphs.
problem Determining non-amenability in mapping class groups of infinite-type surfaces and graphs.
method Analyzes mapping class groups of infinite-type surfaces and graphs, provides examples and exhibits classes of groups.
result Completely determines non-amenability of mapping class groups of infinite-type surfaces and graphs.
New method for learning on heterogeneous graphs without meta-paths.
problem Learning on heterogeneous graphs is sensitive to meta-paths choice, leading to poor performance.
method Decompose heterogeneous graph into homogeneous relation-type graphs, combine higher-order representations, use attention mechanisms.
result Our model outperforms state-of-the-art baselines in vertex classification tasks on heterogeneous graph datasets.
Generates special homeomorphisms for complex surfaces.
problem Creating specific homeomorphisms for infinite-type surfaces.
method General conditions for producing endperiodic loxodromics.
result Produces homeomorphisms acting loxodromically on arc graphs.
Survey Bernstein-type theorems for graphical surfaces in Euclidean and Lorentz-Minkowski spaces.
problem Proving theorems for minimal and constant mean curvature graphs in Euclidean and Lorentz-Minkowski spaces.
method Explains several proofs and provides mean curvature estimates for graphs in Euclidean and Lorentz-Minkowski spaces.
result Bernstein-type theorems for constant mean curvature graphs in Euclidean 3-space and space-like graphs in Lorentz-Minkowski 3-space.
It is classically known that the only zero mean curvature entire graphs in the Euclidean 3-space are planes, by Bernstein's theorem. A surface in Lorentz-Minkowski 3-space R13 is called of mixed type if it changes causal type from space-like to time-like. In R13, Osamu Kobayashi found …
Graph neural networks tackle representation learning for small and giant graphs.
problem Learning representations from small and giant graphs.
method Various graph neural network models tailored for small and giant graphs.
result Graph neural networks achieve state-of-the-art performance on node and graph classification tasks.
Graph neural network predicts JavaScript types with high accuracy.
problem Automatic code repair for JavaScript programs.
method Graph Neural Network model for token type prediction.
result Achieved above 90% accuracy in token type predictions.
Study p-parabolicity on graphs using various energy functionals.
problem Characterize p-parabolicity on infinite locally summable graphs. method Analyze p-energy functionals and use approximation by finite graphs. result Prove various characterizations of p-parabolicity. Paper finds a graph Steklov eigenvalue estimate with rigidity results.
problem Estimating Steklov eigenvalues on graphs.
method Lichnerowicz-type estimate for the first Steklov eigenvalues.
result Rigidity results for the Steklov eigenvalues on graphs.
We describe two locally finite graphs naturally associated to each knot type K, called Reidemeister graphs. We determine several local and global properties of these graphs and prove that in one case the graph-isomorphism type is a complete knot invariant up to mirroring. Lastly, we introduce another object, relating t…
We introduce a topological invariant, it a type of a graph-manifold, which takes natural values. For a 4-dimensional graph-manifold, whose type does not exceed two, it is proved that its universal cover is bi-Lipschitz equivalent to a universal cover of an orthogonal graph-manifold (for any Riemannian metrics on graph-…
Shifts are not type-preserving on surface graphs.
problem Understanding the type-preserving property of shift maps on surface graphs.
method Analyzing Dehn twists and shift maps on arc, curve, and relative arc graphs of surfaces.
result Shift maps are not type-preserving on surfaces with isolated punctures.
Graph Hawkes Neural Network forecasts evolving graph sequences.
problem Modeling dynamic graph sequences with complex event types.
method Generalized Hawkes process to neural network, capturing complex event impacts.
result Effective at predicting future events in evolving graph sequences.
Maximal diameter theorem for graphs with positive Ricci curvature.
problem Diameter comparison in directed graphs with positive Ricci curvature.
method Introduced a Lin-Lu-Yau type Ricci curvature for directed graphs and investigated rigidity properties for the equality case.
result Concluded a maximal diameter theorem of Cheng type.
The aim of this work is studying translating graphs by mean curvature flow in $\Real^3$. We prove non-existence of complete translating graphs over bounded domains in $\Real^2$. Furthermore, we show that there are only three types of complete translating graphs in $\Real^3$; entire graphs, graphs between two vertical p…
New combinatorial type helps distinguish plane curve topologies.
problem Distinguishing the topology of plane curves.
method Introducing G-combinatorial type using modified plumbing graphs.
result Invariant of G-combinatorial type under certain homeomorphisms.
Characterizes minor-minimal separating projective planar graphs and their generalizations.
problem Understanding projective planar graphs and their properties.
method Analyzing minors, embeddings, and specific link types.
result Partial characterization of minor-minimal separating projective planar graphs and their generalizations.
The abstract reviews graph clustering models and their extensions.
problem Graph clustering and model-based approaches.
method Different clustering, inference, and topic models for various graph types.
result Comparison of different approaches to graph clustering.
We define and study analogs of curve graphs for infinite type surfaces. Our definitions use the geometry of a fixed surface and vertices of our graphs are infinite multicurves which are bounded in both a geometric and a topological sense. We show that the graphs we construct are generally connected, infinite diameter a…
Uniform hyperbolicity proved for nonorientable surface curve graphs.
problem Proving uniform hyperbolicity for nonorientable surface curve graphs.
method Using bicorn curves and arguments from orientable surfaces.
result Graph of nonseparating curves is uniformly hyperbolic.
THGFM models dynamic relational systems with cross-type and temporal fusion.
problem Learning on temporal heterogeneous graphs with diverse node and relation types.
method Dual-Path Architecture with Shared-Space and Relational Type-Partitioned Temporal Attention.
result THGFM outperforms baseline models on academic graph benchmarks.
New inequalities for learning from graph-dependent data with stability bounds.
problem Learning from dependent data with graph dependency.
method Proved McDiarmid-type concentration inequalities for graph-dependent variables, showed concentration relies on forest complexity.
result Proved stability bounds for learning from graph-dependent data.
Graph Neural Networks (GNN) learn from graph data by sharing weights over vertices of different types.
problem Learning from graph data with relational structures.
method Revisit and generalize the Graph Neural Network (GNN) model by partitioning vertices into types.
result GNN generalizes many recent models and can assign embeddings to vertices, edges, and global graph attributes.
Paper discusses hyperbolic structures for Artin-Tits groups.
problem No specific problem stated, focuses on hyperbolic structures.
method Algebraic analogues of previously known structures on Artin braid groups.
result Presented several candidates for hyperbolic structures.
New simplicial complex for infinite-type surfaces shows graph properties.
problem Characterizing infinite-type surfaces using graph theory.
method Constructing grand arc graph and analyzing its properties.
result Grand arc graph is infinite-diameter and δ-hyperbolic under certain conditions.
In this paper, we prove some Bernstein type results for n-dimensional minimal Lagrangian graphs in quaternion Euclidean space Hn≅R4n. In particular, we also get a new Bernstein Theorem for special Lagrangian graphs in Cn
A new graph classification method using persistent homology.
problem Graph classification with graph connectivity structure.
method Learnable filter function for persistent homology computation.
result Empirically, the method compares favorably to previous techniques.
Unified estimates for mean curvature in Lorentz-Minkowski space.
problem Estimating mean curvature for space-like and time-like graphs.
method Using gradient bounds to derive Heinz-type estimates.
result Unified vanishing theorem for mean curvature of constant mean curvature graphs.
Improved RL for knowledge graph reasoning with entity types.
problem Challenges in path-based relational reasoning over knowledge graphs.
method Type-enhanced RL agent using GNN for neighborhood information.
result Outperforms state-of-the-art RL methods and discovers novel paths.
New rays on infinite type surfaces help understand their boundaries.
problem Describe the boundary of the loop graph of infinite type surfaces.
method Combining combinatorial and geometric approaches, constructing 2-filling rays.
result Constructed the first examples of 2-filling rays on infinite type surfaces.
GEM detects malicious accounts using adaptive embeddings from heterogeneous graphs.
problem Detecting malicious accounts on a leading mobile payment platform.
method Adaptive learning of discriminative embeddings from heterogeneous account-device graphs with attention mechanism for node importance.
result GEM consistently outperforms competitive methods in detecting malicious accounts.
The study classifies isotopy types of 3-periodic nets and their embeddings.
problem Classifying isotopy types of 3-periodic nets and their embeddings.
method Definition of entangled embedded periodic nets, classification methodology using linear graph knots.
result Enumeration and classification of isotopy classes for various 3-periodic nets.
We study arc graphs and curve graphs for surfaces of infinite topological type. First, we define an arc graph relative to a finite number of (isolated) punctures and prove that it is a connected, uniformly hyperbolic graph of infinite diameter; this extends a recent result of J. Bavard to a large class of punctured sur…
A novel graph spectral method for mixed categorical and numerical data.
problem Feature learning for mixed data types (numerical and categorical).
method Graph spectral decomposition of the graph Laplacian to model probabilistic dependence structure.
result Increased separability and clusterability of observations in the transformed feature space.
New curvature measure for graphs improves diameter and eigenvalue estimates.
problem Estimating properties of graphs using Ricci curvature.
method Introduced integral Ricci curvature Iκ0 for graphs. result Uniform estimates for diameter, number of vertices, and eigenvalue.
Two types of nonidentifiability in latent position graphs identified and characterized.
problem Identifying and characterizing nonidentifiability in latent position random graph models.
method Defined and examined subspace nonidentifiability and model-based nonidentifiability, providing examples and characterizing limits.
result Characterized the limits of model-based nonidentifiability and obtained additional limiting results for specific graph models.
HGT models heterogeneous graphs with dedicated node and edge representations.
problem Infeasibility of existing GNNs for heterogeneous graphs.
method Node- and edge-type dependent parameters, relative temporal encoding, heterogeneous mini-batch graph sampling.
result HGT outperforms state-of-the-art GNNs by 9%--21% on various downstream tasks.
Graph neural networks improve with edge similarity constraints in RNA structure analysis.
problem Lack of edge similarity constraints in graph neural networks.
method Introduced a graph neural network layer that leverages prior information about edge similarities.
result Edge similarity constraints do not enhance performance in graph neural networks.
The paper proves stability of certain graph types in Euclidean space with specific densities.
problem Stability of vertical and radial graphs in Euclidean space with certain densities.
method Techniques of calibrations used to prove stability and minimization.
result Vertical and radial graphs are strongly stable for specific densities.
Paper introduces HGSL for heterogeneous graphs, improving edge type and weight recovery.
problem Learning structure in heterogeneous graphs with multiple node and edge types.
method Proposes H2MN model for DGPs and derives alternating optimization method.
result Demonstrates superior performance on synthetic and real-world datasets.