Selected work

Representative Papers

A Survey on Spatio-Temporal Knowledge Graph Models

Dec 18, 2025

Current spatiotemporal knowledge graph (STKG) models suffer from fundamental limitations—including conceptual fragmentation, terminological inconsistency, poor reusability, and inadequate support for long-term knowledge preservation—arising from their disparate foundations in static, temporal, and spatial graph paradigms. To address these issues, this work introduces the first multidimensional analytical framework encompassing edge semantics, spatiotemporal annotation, and semantic modeling. Through a systematic literature review and cross-dimensional comparative analysis, we clarify the theoretical evolution of STKGs. We further propose a general-purpose modeling guideline explicitly designed for long-term knowledge preservation, establish standardized design principles, and distill six key open challenges. Our contributions provide both theoretical foundations and practical pathways for transitioning STKGs from application-specific solutions toward universal, sustainable knowledge infrastructure.

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Computation of Graph Polynomials via Tree Decomposition: Theory, Algorithms, and Python Implementation

Sep 20, 2025

Efficient computation of graph polynomials—such as the chromatic, independence, and reliability polynomials—remains challenging for large graphs with bounded treewidth (e.g., pathwidth, k-trees). Method: This paper introduces a unified dynamic programming framework based on tree decompositions. Leveraging structural properties of bounded-treewidth graphs, it models polynomial evaluation as a state-transition process over the decomposition tree. A generic state-encoding scheme and transition-mapping mechanism are designed to support systematic computation of multiple graph polynomials. Contribution/Results: The algorithm achieves time complexity (O(n cdot f(k))) for graphs of treewidth (k), significantly extending the applicability of algebraic decomposition methods in graph theory. A publicly available Python implementation is provided, and empirical evaluation on sparse and k-degenerate graph families confirms both theoretical efficiency and practical utility.

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Recent publications

Latest Papers

A Survey on Spatio-Temporal Knowledge Graph Models

Dec 18, 2025

Current spatiotemporal knowledge graph (STKG) models suffer from fundamental limitations—including conceptual fragmentation, terminological inconsistency, poor reusability, and inadequate support for long-term knowledge preservation—arising from their disparate foundations in static, temporal, and spatial graph paradigms. To address these issues, this work introduces the first multidimensional analytical framework encompassing edge semantics, spatiotemporal annotation, and semantic modeling. Through a systematic literature review and cross-dimensional comparative analysis, we clarify the theoretical evolution of STKGs. We further propose a general-purpose modeling guideline explicitly designed for long-term knowledge preservation, establish standardized design principles, and distill six key open challenges. Our contributions provide both theoretical foundations and practical pathways for transitioning STKGs from application-specific solutions toward universal, sustainable knowledge infrastructure.

0 citationsRead paper

Computation of Graph Polynomials via Tree Decomposition: Theory, Algorithms, and Python Implementation

Sep 20, 2025

Efficient computation of graph polynomials—such as the chromatic, independence, and reliability polynomials—remains challenging for large graphs with bounded treewidth (e.g., pathwidth, k-trees). Method: This paper introduces a unified dynamic programming framework based on tree decompositions. Leveraging structural properties of bounded-treewidth graphs, it models polynomial evaluation as a state-transition process over the decomposition tree. A generic state-encoding scheme and transition-mapping mechanism are designed to support systematic computation of multiple graph polynomials. Contribution/Results: The algorithm achieves time complexity (O(n cdot f(k))) for graphs of treewidth (k), significantly extending the applicability of algebraic decomposition methods in graph theory. A publicly available Python implementation is provided, and empirical evaluation on sparse and k-degenerate graph families confirms both theoretical efficiency and practical utility.

0 citationsRead paper