Position Spaces and Graphs
- URL: http://arxiv.org/abs/2606.25719v1
- Date: Wed, 24 Jun 2026 11:36:00 GMT
- Title: Position Spaces and Graphs
- Abstract summary: We introduce position graphs, a graph-based reasoning framework based on the formalization of position spaces.<n>We provide a comprehensive theoretical analysis of this representation, beginning with a characterization of graph consistency.<n>We demonstrate that this problem remains NP-complete even within the restricted class of position graphs.
- Score: 0.9134661726886929
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this paper, we introduce position graphs, a graph-based reasoning framework based on the formalization of position spaces. This framework utilizes two strict partial orders, representing horizontal and vertical alignment and precedence, to model the relative positions of discrete tokens. Unlike general qualitative spatial calculi, position graphs are constrained by a chain condition and compatibility requirements that focus on rows and columns. We provide a comprehensive theoretical analysis of this representation, beginning with a characterization of graph consistency. Conditions to ensure the consistency of position graphs are established. Furthermore, we investigate the computational complexity of structural pattern discovery, modeled as the induced subgraph isomorphism problem. We demonstrate that this problem remains NP-complete even within the restricted class of position graphs. While initially motivated by document processing, this work focuses on the underlying mathematical properties and algebraic consistency of position-based constraints, providing a formal logical layer that is independent of specific data extraction techniques.
Related papers
- When Do Graph Foundation Models Transfer? A Data-Centric Theory [68.54337055801571]
Graph foundation models (GFMs) aim to reuse a single backbone across diverse graph domains, yet their transfer is often uneven and can exhibit negative transfer.<n>We show that for both set-based and message-passing tokenizations, any Lipschitz backbone admits an explicit decomposition of cross-domain output shift.<n>A key ingredient is positional-encoding (PE) stability: we establish stability guarantees for spectral PEs and highlight contrasting behaviors of eigenvector- versus subspace-based PEs.
arXiv Detail & Related papers (2026-05-28T12:07:27Z) - A Theory of Random Graph Shift in Truncated-Spectrum vRKHS [26.195791008324495]
This paper develops a theory of graph classification under domain shift through a random-graph generative lens.<n>We consider intra-class graphs sharing the same random graph model (RGM) and the domain shift induced by changes in RGM components.
arXiv Detail & Related papers (2026-02-27T10:19:57Z) - Topological Spatial Graph Coarsening [1.4323566945483497]
We consider the problem of spatial graph reduction, that aims to find a smaller spatial graph with the same overall structure as the initial one.<n>We propose a topological spatial graph coarsening approach based on a new framework that finds a trade-off between the graph reduction and the preservation of the topological characteristics.<n>Our coarsening approach is parameter-free and we prove that it is equivariant under rotations, translations and scaling of the initial spatial graph.
arXiv Detail & Related papers (2025-12-30T16:27:48Z) - What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding [67.59552859593985]
Graph Transformers, which incorporate self-attention and positional encoding, have emerged as a powerful architecture for various graph learning tasks.
This paper introduces first theoretical investigation of a shallow Graph Transformer for semi-supervised classification.
arXiv Detail & Related papers (2024-06-04T05:30:16Z) - Improving embedding of graphs with missing data by soft manifolds [51.425411400683565]
The reliability of graph embeddings depends on how much the geometry of the continuous space matches the graph structure.
We introduce a new class of manifold, named soft manifold, that can solve this situation.
Using soft manifold for graph embedding, we can provide continuous spaces to pursue any task in data analysis over complex datasets.
arXiv Detail & Related papers (2023-11-29T12:48:33Z) - GrannGAN: Graph annotation generative adversarial networks [72.66289932625742]
We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton.
The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases.
In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features.
arXiv Detail & Related papers (2022-12-01T11:49:07Z) - Harnessing spectral representations for subgraph alignment [15.86857474914914]
We propose a spectral representation for maps that is compact, easy to compute, robust to topological changes, easy to plug into existing pipelines, and is especially effective for subgraph alignment problems.
We report for the first time a surprising phenomenon where the partiality arising in the subgraph alignment task is manifested as a special structure of the map coefficients.
arXiv Detail & Related papers (2022-05-30T09:03:28Z) - Graph Spectral Embedding using the Geodesic Betweeness Centrality [76.27138343125985]
We introduce the Graph Sylvester Embedding (GSE), an unsupervised graph representation of local similarity, connectivity, and global structure.
GSE uses the solution of the Sylvester equation to capture both network structure and neighborhood proximity in a single representation.
arXiv Detail & Related papers (2022-05-07T04:11:23Z) - Articulated Shape Matching Using Laplacian Eigenfunctions and
Unsupervised Point Registration [38.16866987817019]
Spectral graph theory can be used to map these graphs onto lower dimensional spaces and match shapes by aligning their embeddings.
We derive a new formulation that finds the best alignment between two congruent $K$-dimensional sets of points by selecting the best subset of eigenfunctions of the Laplacian matrix.
arXiv Detail & Related papers (2020-12-14T08:49:25Z) - GraphOpt: Learning Optimization Models of Graph Formation [72.75384705298303]
We propose an end-to-end framework that learns an implicit model of graph structure formation and discovers an underlying optimization mechanism.
The learned objective can serve as an explanation for the observed graph properties, thereby lending itself to transfer across different graphs within a domain.
GraphOpt poses link formation in graphs as a sequential decision-making process and solves it using maximum entropy inverse reinforcement learning algorithm.
arXiv Detail & Related papers (2020-07-07T16:51:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.