Graph Search Research Articles

Constructing Knowledge Graphs for New Species in the Greater Mekong Subregion

Purpose: This study presents a methodology for developing a knowledge graph by integrating World Wildlife Fund (WWF) species discovery reports in the Greater Mekong Subregion. Design/methodology/approach: The research employs a four-phase approach: constructing a taxonomic data model, implementing a graph database, developing a web application architecture, and creating an interactive user interface. Findings: The knowledge graph integrates 2,783 species across 27 taxonomic groups, comprising 12,089 nodes and 30,303 connections. The system enables complex queries through SPARQL endpoints, revealing patterns in species distribution and conservation status from 1997 to 2022. Research limitations: Primary limitations include geographical constraints to the Greater Mekong Subregion and limited integration with external biodiversity databases. Practical implications: The system provides researchers with a graph search engine for exploring species data, supporting scientific research and conservation planning. Originality/value: This study presents a novel approach to organizing biodiversity data by combining taxonomic hierarchies with conservation metrics, establishing a framework for integrating species discovery data.

Air Corridor Planning for Urban Drone Delivery: Complexity Analysis and Comparison via Multi-Commodity Network Flow and Graph Search

Urban drone delivery, a rapidly evolving sector, holds the potential to enhance accessibility, address last-mile delivery issues, and alleviate ground traffic congestion in cities. Effective Unmanned Aircraft System Traffic Management (UTM) is essential to scale drone delivery. A critical aspect of UTM involves planning a city-wide network with spatially-separated air corridors (air routes). Most existing works have focused on routing problems or air traffic management. Compared to these problems, the air corridor planning problem requires much higher spatial and temporal resolutions and presents computational challenges due to the scale, complexity, and density of urban airspace, along with the coupling issues of multi-path planning. Therefore, we conducted this research to understand the complexity and computational resources required to optimally solve the air corridor planning problem. In this paper, we use a minimum-cost Multi-Commodity Network Flow (MCNF) model, a mathematical model, to model the problem and demonstrate the complexity of air corridor planning through the complexity of MCNF. We then apply Gurobi’s and GLPK’s integer programming (IP) solvers to find optimal solutions. Additionally, we present two existing multi-path graph search algorithms, the Sequential Route Network Planning (SRP) algorithm and the Distributed Route Network Planning (DRP) algorithm, to address this corridor planning problem. Numerical experiments conducted at various scales and settings using IP solvers and graph search algorithms indicate that finding an optimal solution requires significant computational resources and yields only a slight improvement in optimality compared to graph search algorithms. Thus, air corridor planning is complex both theoretically and numerically, and graph search algorithms can provide a feasible solution with good enough optimality for corridor planning in real-world scenarios. Moreover, the multi-path graph search algorithms can easily incorporate side constraints that are known to be impossible to solve with polynomial algorithms, making it more practical for real-world applications. Finally, we demonstrate the application of SRP and DRP in real-world 3D urban scenarios.

KMSBOT: enhancing educational institutions with an AI-powered semantic search engine and graph database

KMSBOT: enhancing educational institutions with an AI-powered semantic search engine and graph database

Localizability based path planning on occupancy grid maps

In this paper, we propose a path planning method with graph search considering localizability. Localizability is a measure of the reliability of localization. This paper aims to plan paths that avoid places with low reliability of localization by considering localizability. In the proposed method, we constructed a graph based on occupancy grid maps and planned optimal paths by graph search. Localizability costs were considered as path costs for graph search. In our experiments, path planning was performed in three environments: a corridor environment with parallel walls, a square environment larger than the sensor measurement range, and a real environment. Experimental results confirmed that the proposed method planned paths to avoid places with low reliability of localization, such as parallel wall corridors or the center of squares where the sensor scans cannot be obtained. In addition, we conducted comparative experiments between the paths planned by our method and those planned without considering localizability costs. We confirmed that the paths of the proposed method have lower localizability costs and lower localization errors.

Research on path smoothing optimisation based on improved RRT-Connect algorithm and third-order Bezier curve

Aiming at the deficiencies of the original RRT-Connect path planning algorithm in dealing with obstacle avoidance, planning efficiency and path smoothing in static environments, an improved path optimisation method is proposed by fusing the RRT-Connect path planning algorithm with the greedy search strategy and using a third-order Bezier curve for path smoothing. Firstly, a greedy algorithm is added to the path planning process of the original RRT-Connect algorithm to guide the search direction, so as to make the path search more goal oriented, reduce the time of path planning and improve the efficiency. Secondly, the generated paths are smoothed with third-order Bezier curves to ensure the generation of smooth paths with continuous curvature and improve the quality of the planned paths. Finally, the improved RRT-Connect smoothing optimisation algorithm is simulated with the original RRT-Connect, A* and Dijkstra algorithms in different simulation environments to check the performance of the optimised algorithm. The results show that the success rate of obstacle avoidance in planning paths of the optimised algorithms are all 100%, and compared with the original RRT-Connect algorithm, the improved RRT-Connect smoothing optimisation algorithm has an overall reduction of 12.02% in planning path length, an overall reduction of 40.22% in computation time and an overall improvement of 69.15% in path smoothness. Compared to the A* algorithm, which is based on the graph search shortest path planning algorithm and takes the shortest time, there is an overall reduction of 62.46% in computation time. The improved RRT-Connect smoothing optimisation algorithm has shorter path planning lengths, smoother paths and shortest computation time in different simulation environments. Meanwhile, the stability of the optimisation algorithm is effectively improved in different environments and different running times.

On the leaves of graph search trees

On the leaves of graph search trees

Enabling efficient analysis of biobank-scale data with genotype representation graphs.

Computational analysis of a large number of genomes requires a data structure that can represent the dataset compactly while also enabling efficient operations on variants and samples. However, encoding genetic data in existing tabular data structures and file formats has become costly and unsustainable. Here we introduce the genotype representation graph (GRG), a fully connected hierarchical data structure that losslessly encodes phased whole-genome polymorphisms. Exploiting variant-sharing across samples enables GRG to compress 200,000 UK Biobank phased human genomes to 5-26 gigabytes per chromosome, also enabling graph-traversal algorithms to reuse computed values in random access memory. Constructing and processing GRG files scales to a million whole genomes. Using allele frequencies and association effects as examples, we show that computation on GRG via graph traversal runs the fastest among all tested alternatives. GRG-based algorithms have the potential to increase the scalability and reduce the cost of analyzing large genomic datasets.

A Rapid Approach Based on Empirical Formulas and Graph Traversal Algorithms to Automatic Generation of Fragrance Formulas

A Rapid Approach Based on Empirical Formulas and Graph Traversal Algorithms to Automatic Generation of Fragrance Formulas

Optimizing Graph Search Algorithms Through Adaptive Dynamic Data Structures in Social Networks

Optimizing Graph Search Algorithms Through Adaptive Dynamic Data Structures in Social Networks

Research Status of Vehicle Trajectory Planning

As autonomous driving technology has advanced, it has drawn attention from all around the world. The implementation of autonomous driving technology has the potential to enhance traffic safety, minimize traffic accidents, boost efficiency, facilitate travel, conserve energy, and lower emissions. Autonomous driving technology includes environmental perception, path planning, behavioral decision-making and other technologies. Among them, trajectory planning and control technology is the key technology to realize autonomous driving of automobiles and is the concrete embodiment of automobile intelligence. Graph search algorithms, numerical optimization algorithms, curve fitting algorithms, artificial potential field algorithms, random sampling algorithms, etc. are currently in widespread usage in the field of autonomous driving research. This article will introduce vehicle trajectory planning based on these commonly used algorithms. The necessity of autonomous driving technology research is not only reflected in technological progress, but also covers social security, economic benefits, environmental protection, travel convenience and global competition. By studying autonomous driving technology, humans can better cope with the current challenges of traffic and environment, and at the same time provide strong support for future intelligent transportation and urban planning.

Robot path planning based on IAPF-A-Star algorithm

The traditional A-Star algorithm has the problem of performing much invalid traversal work of nodes near concave obstacles when dealing with path planning tasks in the presence of concave obstacles. To solve this problem, a fusion algorithm (called IAPF-A-Star here) based on improved artificial potential field (IAPF) and A-Star is proposed. In IAPF-A-Star, to better measure the distances between the nodes and the target node, the potential energy of the artificial potential field is used to represent the node’s proximity to the target node, and an iterative correction method is used to improve the potential field in order to eliminate the local low-potential energy that may exist in the artificial potential field. The improved potential field effectively avoids the occurrence of local low-potential fields and makes it possible to measure the position of nodes in relation to their target node by means of the potential field. Finally, the obtained path is converted to a curve path by using a cubic spline curve. In addition, to solve the problem that the curve path generated by the cubic spline curve may collide with obstacles, a path optimization strategy based on an adaptive cubic spline curve is proposed to make the final path smooth and collision avoidance, shorter and closer to the actual situation. Simulation experiments show that the proposed IAPF-A-Star algorithm in this paper can effectively avoid the problem of additional local energy consumption caused by concave obstacles when processing path planning tasks, and the traversal of invalid nodes facing different complex concave obstacles is effectively avoided.

MST: Topology-Aware Message Aggregation for Exascale Graph Processing of Traversal-Centric Algorithms

This article presents MST, a communication-efficient message library for fast graph traversal on exascale clusters. The key idea is to follow the multi-level network topology to perform topology-aware message aggregation, where small messages are gathered and scattered at each level of domain. To facilitate message aggregation, we equip MST with flexible buffer management including active buffer switching and dynamic buffer expansion. We implement MST on the newest-generation Tianhe supercomputer and evaluated its performance using various traversal-centric algorithms on both synthetic trillion-scale graphs and real-world big graphs. The results show that MST-based graph traversal is orders of magnitude faster than that based on Active Messages Library (AML). For the Graph500-BFS benchmark, MST-based Tianhe (with 77.2 K nodes) outperforms the Fugaku supercomputer (with 148.5 K nodes) by 18.53%, while Fugaku is ranked No. 1 in the latest Graph500-BFS ranking (June 2023). MST also greatly improves graph processing performance on other commercial large-scale computing systems at the National Supercomputing Center in Changsha (NSCC) and WuzhenLight.

Learning Based Toolpath Planner on Diverse Graphs for 3D Printing

This paper presents a learning based planner for computing optimized 3D printing toolpaths on prescribed graphs, the challenges of which include the varying graph structures on different models and the large scale of nodes & edges on a graph. We adopt an on-the-fly strategy to tackle these challenges, formulating the planner as a Deep Q-Network (DQN) based optimizer to decide the next 'best' node to visit. We construct the state spaces by the Local Search Graph (LSG) centered at different nodes on a graph, which is encoded by a carefully designed algorithm so that LSGs in similar configurations can be identified to re-use the earlier learned DQN priors for accelerating the computation of toolpath planning. Our method can cover different 3D printing applications by defining their corresponding reward functions. Toolpath planning problems in wire-frame printing, continuous fiber printing, and metallic printing are selected to demonstrate its generality. The performance of our planner has been verified by testing the resultant toolpaths in physical experiments. By using our planner, wire-frame models with up to 4.2k struts can be successfully printed, up to 93.3% of sharp turns on continuous fiber toolpaths can be avoided, and the thermal distortion in metallic printing can be reduced by 24.9%.

Solving Paths Search Problems in Complex Graphs

The construction of models of various systems is associated with the enumeration of the values of the parameters of the elements of the structure and taking into account all the characteristics of operation and interaction of components to find a certain set of solutions that determine the configuration of the system. Such tasks belong to enumeration type tasks and imply that some of the next solutions from this set are obtained from the previous solution in a certain order. It is known that any problem of the enumeration type is solved only by methods of exhaustive search, and other methods for their enumeration do not exist yet. The paper presents a new method of searching paths in a graph – the method of node-graph transformation. The proposed method, unlike the existing ones, allows one to search all directed simple paths in an oriented graph of arbitrary structure much faster. In the known graph search methods (Breadth First Search and Depth First Search), the object of the search is a path. The total number of such paths in the graph determines the size of the search space. The main idea of the node-graph transformation method is to significantly reduce the size of the search space by enlarging the search objects. The enlargement of enumeration objects is performed by clustering paths into combinatorial objects that concentrate some set of paths of the same length according to certain rules. These combinatorial objects are called node-graphs. A node-graph refers to center-peripheral combinatorial objects, and specific node-graph transformation operations have been developed to enumerate all paths in the graph, which allow finding new paths based on previous paths. The method can be used as a basic toolkit to reduce the dimensionality of the search space for solutions to NP-complete problems while maintaining the universality and accuracy of the full search.

Efficient generation of grids and traversal graphs in compositional spaces towards exploration and path planning

AbstractDiverse disciplines across science and engineering deal with problems related to compositions, which exist in non-Euclidean simplex spaces, rendering many standard tools inaccurate or inefficient. This work explores such spaces conceptually in the context of materials discovery, quantifies their computational feasibility, and implements several essential methods specific to simplex spaces through a new high-performance open-source library . Most significantly, we derive and implement an algorithm for constructing a novel n-dimensional simplex graph data structure, containing all discretized compositions and possible neighbor-to-neighbor transitions. Critically, no distance or neighborhood calculations are performed, instead leveraging pure combinatorics and order in procedurally generated simplex grids, keeping the algorithm $${\mathcal{O}}(N)$$ O ( N ) , with minimal memory, enabling rapid construction of graphs with billions of transitions in seconds. Additionally, we demonstrate how such graph representations can be combined to homogeneously express complex path-planning problems, while facilitating efficient deployment of existing high-performance gradient descent, graph traversal, and other optimization algorithms.

Safety evaluation for the dismantling of long-span spatial lattice structures based on deep learning and graph traversal

The demolition issue of long-span spatial lattice structures has become increasingly prominent with the rapid urban development and building renewal. Compared with traditional demolition techniques, building deconstruction offers superior economic and social benefits. Dismantling in blocks is one of the primary ways to realize the deconstruction of long-span spatial lattice structures, yet the safety is rarely studied. To fill this gap, a safety evaluation framework was established based on deep learning and graph traversal algorithm in this study. Hazardous configuration of the remaining structure is the basis of dismantling safety evaluation. To collect the hazardous configurations, an identification method in conjunction with deep neural network (DNN) and depth-first search (DFS) algorithm was proposed. The dismantling safety is measured by the improved structural well-formedness whose allowable range is determined by statistical analysis of hazardous configurations. The applicability of the framework was illustrated by the safety evaluation for the dismantling of single-layer reticulated domes. Results indicated that the dismantling safety is significantly affected by the configuration quality of the remaining structure, and the DNN-based prediction model is effective to classify the configuration state. Additionally, a large number of hazardous configurations were obtained through the identification method, and the allowable value of the safety evaluation index was determined to be 0.7. The configuration quality mainly depends on the geometry and boundary conditions. This novel framework provides scientific and effective methodological guidance for engineers, promoting the further application of building deconstruction.

Training Climbing Roses by Constrained Graph Search

ABSTRACTCultivated climbing roses are skillfully shaped by arranging their stems manually against support walls to enhance their aesthetic appeal. This study introduces a procedural technique designed to replicate the branching pattern of climbing roses, simulating the manual training process. The central idea of the proposed approach is the conceptualization of tree modeling as a constrained path‐finding problem. The primary goal is to optimize the stem structure to achieve the most impressive floral display. The proposed method operates iteratively, generating multiple stems while applying the objective function in each iteration for maximizing coverage on the support wall. Our approach offers a diverse range of tree forms employing only a few parameters, which eliminates the requirement for specialized knowledge in cultivation or plant ecology.

Real-time reactive task allocation and planning of large heterogeneous multi-robot systems with temporal logic specifications

Existing methods for the task allocation and planning (TAP) of multi-robot systems with temporal logic specifications mainly rely on optimization-based approaches or graph search techniques applied to the product automaton. However, these methods suffer from high computational cost and scale poorly with the number of robots and the complexity of temporal logic tasks, thus limiting the applicability in real-time implementation, especially for large multi-robot systems. To address these challenges, this work develops a novel TAP framework that can solve reactive temporal logic planning problems for large-scale heterogeneous multi-robot systems (HMRS) in real time. Specifically, we develop a planning decision tree (PDT) to represent the task progression and task allocation specialized for HMRS with temporal logic specifications. Based on the PDT, we develop two key search algorithms—the planning decision tree search (PDTS) and the interactive planning decision tree search (IPDTS)—where PDTS generates an offline plan which will be modified online by PDTS and IPDTS jointly to enable fast reactive planning if environmental changes or temporary tasks occur. Such a design can generate satisfying plan for HMRS with multiple orders of magnitude more robots than those that existing methods can manipulate. Rigorous analysis shows that the PDT-based planning is feasible (i.e., the generated plan is applicable) and complete (i.e., a feasible plan, if exits, is guaranteed to be found). The algorithm complexity further indicates that the solution time is only linearly proportional to the robot numbers and types. Simulation and experiment results demonstrate that reactive plan can be generated for large HMRS in real-time, which outperforms the state-of-the-art methods.

Optimization Algorithm of Network Node Layout of Fresh Agricultural Products’ Online Consumption Behavior in E-Commerce Environment

In order to improve the efficiency of online consumption and supply chain efficiency of fresh agricultural products in the e-commerce environment, a network node layout optimization algorithm was designed. In the e-commerce environment, a network node deployment model has been constructed for the online consumption behavior of fresh agricultural products. A network transmission supply chain detection model was established through the rotation scheduling method. By combining distributed transformation protocols, the spatial layout characteristics of network nodes have been reorganized. Further introduction of a multi-coverage scheduling model, combined with sensor information fusion tracking technology, accurately detects the layout characteristics of network nodes. Based on these detection data, forwarding and adaptive control protocols were developed. Real-time control using the node traversal method was developed. Under the optimal node supply chain transmission mechanism, the scheduling of the network node layout has been achieved. Meanwhile, convolutional neural networks were utilized to dynamically adjust layout control to ensure convergence and stability. The simulation results show that the node forwarding ability of the network node layout design for online consumption behavior of fresh agricultural products in the e-commerce environment is good, which improves the output balance of the network supply chain transmission of online consumption behavior of fresh agricultural products in the e-commerce environment. In addition, the optimized delivery cost decreased by 20% compared to before optimization, delivery efficiency increased by 30%, and inventory turnover rate increased by 25%, confirming that the proposed method has good optimization performance.

Locality-Preserving Graph Traversal With Split Live Migration

Locality-Preserving Graph Traversal With Split Live Migration