Affine equivalence of half-translation surfaces via saddle connection graphs.
problem Understanding affine equivalence of half-translation surfaces.
method Association of saddle connection graphs and investigation of their automorphism groups.
result Every isomorphism between saddle connection graphs is induced by an affine homeomorphism between the underlying half-translation surfaces.
Every infinitely edge-connected graph has a minor of Farey graph or Tℵ0∗t.
problem Characterizing edge-connected graphs with specific minor properties.
method Analyzing the minor structure of infinitely edge-connected graphs.
result Infinitely edge-connected graphs contain Farey graph or Tℵ0∗t as a minor. Paper proves edge-connectivity equals minimum degree for graphs with non-negative curvature.
problem Edge-connectivity vs. minimum degree in graphs with non-negative curvature.
method Analyzes finite connected graphs with non-negative Lin-Lu-Yau curvature.
result Edge-connectivity equals minimum degree for graphs with non-negative curvature.
Estimates the number of connected components in a graph from a sampled subgraph.
problem Inferring the number of connected components in a larger graph from a sampled subgraph.
method A highly redundant and large-dimensional representation of the subgraph using counts of network motifs, leading to a novel estimator for the number of connected components.
result Improves upon competing algorithms for graphs with spectral gaps bounded away from zero.
New curvature tensor and matrices for connection graphs derived from Bakry-Émery curvature.
problem Deriving Buser-type bounds on eigenvalues of connection Laplacians.
method Reformulation of Bakry-Émery curvature through curvature matrices and tensor representations.
result Extension of curvature matrices to connection graphs, addressing eigenfunction challenges.
The study connects spheres in specific surface curve graphs, proving connectivity and classifying components.
problem Proving connectivity and classifying components of spheres in curve graphs of low and medium complexity surfaces.
method Analyzing specific surfaces Σ2,0,Σ1,3,Σ0,6 and Σ0,5,Σ1,2, proving connectivity and classifying components. result Spheres of any radius are connected in Σ2,0,Σ1,3,Σ0,6, and the union of two consecutive spheres is connected in Σ0,5 and Σ1,2. New graphs found that can be drawn without crossing links.
problem Finding graphs that can be drawn without links crossing.
method Provided specific examples for each n≥14. result Infinite family of graphs linklessly embeddable and Tutte-4-connected.
Study shows saddle connection graph's geometry and quasi-isometry properties.
problem Characterize the geometry and quasi-isometry of saddle connection graphs.
method Proved 4-hyperbolicity and uniform quasi-isometry to a tree, used generalised unicorn paths.
result Saddle connection graph is not quasi-isometrically rigid and its boundary is straight foliations.
Spheres in curve graphs are connected, proving Gromov boundary linearity.
problem Understanding connectivity in curve graphs and their boundaries.
method Defining spheres and analyzing their connectivity for different complexities.
result Spheres in high complexity curve graphs are always connected, with weaker results for low complexity.
Minimal graphs over simply connected domains grow at most exponentially.
problem Growth of minimal graphs over simply connected domains with boundary values 0.
method Analyzing solutions to the minimal surface equation.
result Minimal graphs have at most exponential growth.
New bounds for convex clustering under graph connectivity.
problem Understanding clustering performance under different graph connectivity structures.
method Random walks and concentration inequalities for random graph models.
result Improved rates of convergence for centroid recovery.
Spatial graphs are decomposed into planar forests and braids.
problem Understanding the structure of spatial graphs in 3-space.
method Decomposition of spatial graphs into planar forests and braids.
result Every finite spatial graph is a connected sum of a planar graph and a braid.
Researchers compute connectivity of braid group in bipartite graph configuration space.
problem Understanding connectivity of braid group in complex configuration space.
method Analysis of topology, hidden symmetry, and literature results.
result Explicit computation of connectivity at infinity for braid group.
New homology theory connects graph domination to subtle algebraic structures.
problem Understanding graph domination through algebraic homology.
method Interpreting überhomology as poset homology and showing its functorial properties.
result The Euler characteristic of bold homology equals the evaluation of the connected domination polynomial.
The paper studies grid homology for spatial graphs and proves a Künneth formula for connected sums.
problem Understanding grid homology for spatial graphs with various types of edges.
method Developed grid homology for spatial graphs with cut edges and applied it to prove a Künneth formula for connected sums.
result A Künneth formula for knot Floer homology of connected sums is proven using grid homology.
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.
Explicit computation of Kontsevich weights for symplectic Poisson structures.
problem Computing weights of Kontsevich graphs in symplectic Poisson structures.
method Detailed explicit computation using hypergeometric functions and simpler formulas.
result Explicit expressions for curvature weights and their simplification in cotangent bundles.
Framework models graph-connected entities with sparse shared HMMs.
problem Model sequential data from graph-connected entities.
method Sparse Mixture of Hidden Markov Models (HMMs) trained jointly with graph topology.
result Effectiveness and versatility demonstrated in experiments.
This paper is concerned with lower bounds for the connectivity of graphs (one-dimensional skeleta) of triangulations of compact manifolds. We introduce a structural invariant b_M for simplicial d-manifolds M taking values in the range 0 <= b_M <= d-1. The main result is that b_M influences connectivity in the following…
Connected graph for twice-punctured torus curves.
problem Structure of tri-pants graph on twice-punctured torus.
method Examined relationship with Farey complex to prove connectivity and infinite diameter.
result Tri-pants graph is connected and has infinite diameter.
The study explores symmetries of graphs in 3-manifolds and their induced homeomorphisms.
problem Understanding when graph automorphisms are induced by homeomorphisms in 3-manifolds.
method Analyzes embeddings of graphs in various 3-manifolds and their symmetries.
result Not all graph automorphisms are induced by homeomorphisms in all 3-manifolds, but many properties hold for homology spheres.
Upper bound found for graph manifold complexity.
problem Determining complexity of graph manifolds.
method Using Matveev complexity for all catalogued manifolds.
result Upper bound is sharp for 14,502 catalogued manifolds.
A new unpooling layer enhances graph generation in molecular models.
problem Efficient graph generation for complex models like molecules.
method Trainable unpooling layer that enlarges and restructures graphs.
result The unpooling layer improves graph generation in molecular models.
A new discrete formula connects vertex and edge distributions on graphs.
problem Optimal transport on graphs with mixed vertex and edge distributions.
method Discrete transport equation and Benamou-Brenier formulation.
result Classification of all Wasserstein-1 geodesics on graphs.
Edge augmentation connects disconnected graphs by elevating eigenvalues.
problem Connecting disconnected subgraphs in graphs with zero eigenvalues.
method Elevating zero eigenvalues of graph's spectrum to connect subgraphs.
result The algorithm consistently connects graph components, achieving >50% inter-community edges.
Graph neural networks suffer from oversmoothing, but adding residual connections helps.
problem Oversmoothing in deep graph neural networks where features become indistinguishable.
method Analyzed asymptotic oversmoothing rates with and without residual connections using the multiplicative ergodic theorem.
result Adding residual connections effectively mitigates or prevents oversmoothing.
The paper defines surface area for graphs and derives spectral estimates.
problem Understanding connectivity measures and spectral properties of graphs.
method Introducing surface area concepts related to inverse degree and deriving spectral bounds.
result An upper bound on the second eigenvalue for planar graphs.
New graphs show hierarchical hyperbolic properties, extending previous work.
problem Characterizing hierarchically hyperbolic properties of multiarc and curve graphs.
method Analyzing the geometric intersection number and using PMod(S) action.
result Multiarc and curve graphs are hierarchically hyperbolic.
Optimal Reeb graphs identified for polygon decomposition.
problem Investigating the topological structure of planar polygon decomposition.
method Using oriented Reeb graphs with a marked vertex for height functions.
result Described all possible optimal Reeb graphs for specific polygon configurations.
3D Schoenflies theorem for simply-connected 2-complexes.
problem Embedding simply-connected 2-complexes in 3-space uniquely.
method Proving a 3-dimensional Schoenflies theorem for 3-connected link graphs.
result Essentially unique locally flat embedding into 3-sphere.
A new kernel measures brain network similarities, improving disease classification.
problem Lack of edge weight information in existing graph kernels for brain connectivity networks.
method Ordinal pattern kernel for weighted brain connectivity networks.
result The ordinal pattern kernel achieves better classification performance than state-of-the-art graph kernels.
GraphAT improves graph neural networks by dynamically considering connected examples in adversarial training.
problem Graph neural networks are vulnerable to adversarial perturbations due to connections between examples.
method GraphAT dynamically regularizes based on graph structure to resist adversarial perturbations.
result GraphAT outperforms normal training on GCN by 4.51% in node classification accuracy.
New spectral conditions ensure graph rigidity and global rigidity in the Euclidean plane.
problem Ensuring graph rigidity and global rigidity in the Euclidean plane.
method Improving algebraic connectivity bounds for graph rigidity and global rigidity.
result Every 6-connected graph is rigid and globally rigid if its algebraic connectivity exceeds specific thresholds.
Study Kazdan-Warner equations on graphs using Brouwer degree theory.
problem Proving existence of solutions to Kazdan-Warner equations on finite graphs.
method Degree theory approach to uniformly bound and compute Brouwer degree.
result New proofs of existence results for Kazdan-Warner equations.
GCN and GPCA are mathematically connected, leading to improved node classification performance.
problem Improving node classification performance in semi-supervised settings.
method Established a mathematical connection between GCN and GPCA, demonstrating their equivalence and using this to design an effective initialization strategy.
result GPCA paired with a simple MLP achieves similar or better performance than GCN on semi-supervised node classification tasks.
Study evaluates neural networks based on random graph structures and finds key performance indicators.
problem Understanding and optimizing neural network architectures using graph theory.
method Evaluation of neural networks with random graph structures, focusing on structural and numerical properties.
result A new numerical graph characteristic selects a set of quasi-1-dimensional graphs that perform well.
Graph kernel uses Ricci curvature for comparison.
problem Graph comparison without node attributes.
method Edge curvature distribution for graph kernel.
result Graphs can be compared using topology alone.
Graph conditions ensure matching arc complexes are connected and hyperbolic.
problem Conditions for connectedness and hyperbolicity of matching arc complexes.
method Conditions on finite simplicial graphs guaranteeing connectedness and hyperbolicity of matching arc complexes.
result Conditions on finite simplicial graphs ensure connectedness and hyperbolicity of matching arc complexes.
Proposes a method to enhance graph models by injecting unseen connections.
problem Enhancing graph models to utilize unseen connections.
method Parametric link injection layer to find and inject weak connections.
result Improves performance on node classification and link prediction tasks.
CTGCN learns dynamic graph embeddings preserving both local and global graph structure.
problem Learning node representations for evolving graphs while preserving both local and global graph structure.
method CTGCN uses k-core based temporal graph convolutional network to learn dynamic graph embeddings.
result CTGCN outperforms existing methods in link prediction and structural role classification.
The Cayley graph of quandles reveals structural properties and is studied for various classes.
problem Investigating structural properties of Cayley graphs of quandles.
method Analyzing Cayley graphs for different quandle classes and proving properties.
result Connected components of Cayley graphs of Alexander quandles correspond to cosets of specific subgroups.
An embedding of a graph into R3 is said to be linear, if any edge of the graph is sent to be a line segment. And we say that an embedding f of a graph G into R3 is free, if π1(R3−f(G)) is a free group. It was known that for any complete graph its linear embedding is always free.…
New bound for group action length without diameter restriction.
problem Bounding minimal translation length for Artin groups.
method Graph theoretic properties of biconnected graphs.
result Upper bound of 2 for minimal translation length holds without diameter restriction.
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.
Improved graph-based connectivity estimation using heat modelling.
problem Lack of explicit model-based, dynamic, multivariate, and directed connectivity estimation methods.
method Noise-driven heat modelling on graphs with relaxed assumptions and regularisation.
result Demonstrated ability to capture meaningful spatial structure across real-world datasets.
ST-GCN improves rs-fMRI prediction accuracy by modeling spatio-temporal graph connectivity.
problem Existing rs-fMRI methods neglect functional connectivity or temporal dynamics.
method Spatio-temporal graph convolutional network (ST-GCN) trained on BOLD time series.
result ST-GCN predicts gender and age more accurately than common methods.
This paper explains spectral clustering and its equivalence to PCA, breaking it into fully connected and multi-connected cases.
problem Understanding the mathematics behind spectral clustering and its equivalence to PCA.
method Dividing spectral clustering into two categories based on graph connectivity and proving the equivalence to PCA.
result Spectral clustering and PCA are equivalent, with specific proofs for fully connected and multi-connected graphs.
DynDepNet learns dynamic brain graphs from fMRI data for better prediction performance.
problem Static brain graphs from fMRI data lead to poor GNN performance.
method Dynamic Graph Structure Learning for time-varying brain connectivity.
result DynDepNet achieves state-of-the-art sex classification accuracy on real-world fMRI data.