Graph Search Research Articles

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.

SGIR-Tree: Integrating R-Tree Spatial Indexing as Subgraphs in Graph Database Management Systems

Efficient spatial query processing in Graph Database Management Systems (GDBMSs) has become increasingly important owing to the prevalence of spatial graph data. However, current GDBMSs lack effective spatial indexing, causing performance issues with complex spatial graph queries. This study proposes a spatial index called Subgraph Integrated R-Tree (SGIR-Tree) for efficient spatial query processing in GDBMSs. The SGIR-Tree integrates the hierarchical R-Tree structure with the graph structure of GDBMSs by converting R-Tree elements into graph components like nodes and edges. The Minimum Bounding Rectangle (MBR) information of spatial objects and R-Tree nodes is stored as properties of these graph elements, and the leaf nodes are directly connected to the spatial nodes. This approach combines the efficiency of spatial indexing with the flexibility of graph databases, thereby allowing spatial query results to be directly utilized in graph traversal. Experiments using OpenStreetMap datasets demonstrate that the SGIR-Tree outperforms the previous approaches in terms of query overhead and index overhead. The results are expected to improve spatial graph data processing in various fields, including location-based service and urban planning, significantly advancing spatial data management in GDBMSs.

Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.

Computational drug repositioning, through predicting drug-disease associations (DDA), offers significant potential for discovering new drug indications. Current methods incorporate graph neural networks (GNN) on drug-disease heterogeneous networks to predict DDAs, achieving notable performances compared to traditional machine learning and matrix factorization approaches. However, these methods depend heavily on network topology, hampered by incomplete and noisy network data, and overlook the wealth of biomedical knowledge available. Correspondingly, large language models (LLMs) excel in graph search and relational reasoning, which can possibly enhance the integration of comprehensive biomedical knowledge into drug and disease profiles. In this study, we first investigate the contribution of LLM-inferred knowledge representation in drug repositioning and DDA prediction. A zero-shot prompting template was designed for LLM to extract high-quality knowledge descriptions for drug and disease entities, followed by embedding generation from language models to transform the discrete text to continual numerical representation. Then, we proposed LLM-DDA with three different model architectures (LLM-DDANode Feat, LLM-DDADual GNN, LLM-DDAGNN-AE) to investigate the best fusion mode for LLM-based embeddings. Extensive experiments on four DDA benchmarks show that, LLM-DDAGNN-AE achieved the optimal performance compared to 11 baselines with the overall relative improvement in AUPR of 23.22%, F1-Score of 17.20%, and precision of 25.35%. Meanwhile, selected case studies of involving Prednisone and Allergic Rhinitis highlighted the model's capability to identify reliable DDAs and knowledge descriptions, supported by existing literature. This study showcases the utility of LLMs in drug repositioning with its generality and applicability in other biomedical relation prediction tasks.

MAKG: A maritime accident knowledge graph for intelligent accident analysis and management

With the increasing frequency of human activities at sea, maritime accidents are occurring more often. Analyzing and mining maritime accident cases can help uncover the causal mechanisms behind these incidents, thereby enhancing maritime safety. As an emerging technology for knowledge management and mining, knowledge graphs offer significant support for the storage, reasoning, and decision-making processes related to maritime accidents.In this study, we established a knowledge graph construction and application framework for maritime accidents to facilitates the extraction and management of maritime knowledge from unstructured texts. First, 581 accident reports released by the China Maritime Safety Administration over the past decade (2014–2023) were used as the data basis for analysis and construction of the maritime accident ontology structure using the seven-step method, which comprises 8 entity types, 8 relationship types, and 18 attribute entity types. Second, We proposed MBERT-BiLSTM-CRF-SF, a named entity recognition model based on domain pretraining and self-training, to reduce graph construction costs. This model achieved state-of-the-art performance in the maritime domain, with an F1 score of 0.910 ± 0.006, which is about 5% higher than the mainstream model. In addition, we proposed an entity alignment method based on font and semantics to refine knowledge further. On the basis of the proposed method, we constructed a large, high-quality maritime accident knowledge graph (MAKG) system that contains 16,099 entities and 20,809 relationship instances. Finally, we reduced the complexity of applying knowledge graphs by integrating the CRISPE prompt learning framework of the large language model, and experiments on graph traversal, pattern recognition, and aggregation analysis were conducted to assess the quality of MAKG. Results demonstrate that MAKG can effectively enhance the efficiency of querying and reasoning about maritime accident information, thus providing significant support for the prevention and management of maritime accidents.

RGB-D visual odometry by constructing and matching features at superpixel level

Abstract Visual odometry (VO) is a key technology for estimating camera motion from captured images. In this paper, we propose a novel RGB-D visual odometry by constructing and matching features at the superpixel level that represents better adaptability in different environments than state-of-the-art solutions. Superpixels are content-sensitive and perform well in information aggregation. They could thus characterize the complexity of the environment. Firstly, we designed the superpixel-based feature SegPatch and its corresponding 3D representation MapPatch. By using the neighboring information, SegPatch robustly represents its distinctiveness in various environments with different texture densities. Due to the inclusion of depth measurement, the MapPatch constructs the scene structurally. Then, the distance between SegPatches is defined to characterize the regional similarity. We use the graph search method in scale space for searching and matching. As a result, the accuracy and efficiency of matching process are improved. Additionally, we minimize the reprojection error between the matched SegPatches and estimate camera poses through all these correspondences. Our proposed VO is evaluated on the TUM dataset both quantitatively and qualitatively, showing good balance to adapt to the environment under different realistic conditions.

Dynamic-ACTS - A Dynamic Graph Analytics Accelerator For HBM-Enabled FPGAs

Graph processing frameworks suffer performance degradation from under-utilization of available memory bandwidth, because graph traversal often exhibits poor locality. A prior work, ACTS [ 24 ], accelerates graph processing with FPGAs and High Bandwidth Memory (HBM). ACTS achieves locality by partitioning vertex-update messages (based on destination vertex IDs) generated online after active edges have been processed. This work introduces Dynamic-ACTS which builds on ideas in ACTS to support dynamic graphs. The key innovation is to use a hash table to find the edges to be updated. Compared to Gunrock, a GPU graph engine, Dynamic-ACTS achieves a geometric mean speedup of 1.5X, with a maximum speedup of 4.6X. Compared to GraphLily, an FPGA-HBM graph engine, Dynamic-ACTS achieves a geometric speedup of 3.6X, with a maximum speedup of 16.5X. Our results also showed a geometric mean power reduction of 50% and a mean reduction of energy-delay product of 88% over Gunrock. Compared to GraSU, an FPGA graph updating engine, Dynamic-ACTS achieves an average speedup of 15X.

Space-time graph path planner for unsignalized intersection management with a V2V agent coordination architecture

Reducing traffic congestion and increasing passenger safety are important objectives for emerging automated transportation systems. Autonomous intersection management systems (AIMS) enable large scale optimization of vehicle trajectories with connected and autonomous vehicles (CAVs). We propose a novel approach for computing the fastest waypoint trajectory in intersections using graph search in a discretized space-time graph that produces collision-free paths with variable vehicle speeds that comply with traffic rules and vehicle dynamical constraints. To assist our planner algorithm in decentralized scenarios, we also propose a multi-agent protocol architecture for vehicle coordination for trajectory planning using a vehicle-to-vehicle (V2V) network. The trajectories generated allow a much higher evacuation rate and congestion threshold, with lower O(N) algorithm runtime compared to the state of the art conflict detection graph platoon path planning method, even for large scenarios with vehicle arrival rate of 1/s and thousands of vehicles.

Space‐efficient data structures for the inference of subsumption and disjointness relations

AbstractConventional database systems function as static data repositories, storing vast amounts of facts and offering efficient query processing capabilities. The sheer volume of data these systems store has a direct impact on their scalability, both in terms of storage space and query processing time. Deductive database systems, on the other hand, require far less storage space since they derive new knowledge by applying inference rules. The challenge is how to efficiently obtain the required derivations, compared to having them in explicit form. In this study, we concentrate on a set of predefined inference rules for subsumption and disjointness relations, including their negations. We use compact data structures to store the facts and provide algorithms to support each type of relation, minimizing even further the storage space requirements. Our experimental findings demonstrate the feasibility of this approach, which not only saves space but is often faster than a baseline that uses well‐known graph traversal algorithms implemented on top of a traditional adjacency list representation to derive the relations.

Truss community search in uncertain graphs

Truss community search in uncertain graphs

Trajectory Planning Design for Parallel Parking of Autonomous Ground Vehicles with Improved Safe Travel Corridor

In this paper, the concept of symmetry is utilized to design the trajectory planning for parallel parking of autonomous ground vehicles—that is, the construction and the solution of the optimization-based trajectory planning approach are symmetrical. Parking is the main factor that troubles most drivers for their daily driving travel, and it can even lead to traffic congestion in severe cases. With the rise of new intelligent and autonomous vehicles, automatic parking seems to have become a trend. Traditional geometric planning methods are less adaptable to parking scenarios, while the parking paths planned by graph search methods may only achieve local optimality. Additionally, significant computational time is often required by numerical optimization methods to find a parking path when a good initial solution is not available. This paper presents a hierarchical trajectory planning approach for high-quality parallel parking of autonomous ground vehicles. The approach begins with a graph search layer to roughly generate an initial solution, which is refined by a numerical optimization layer to produce a high-quality parallel parking trajectory. Considering the high dimensionality and difficulty of finding an optimal solution for the path planning optimization problem, this paper proposes an improved safe travel corridor (I-STC) with the construction of collision constraints isolated from surrounding environmental obstacles. By constructing collision constraints of the I-STC based on the initial solution, the proposed method avoids the complexities and non-differentiability of traditional obstacle avoidance constraints, and simplifies the problem modeling the subsequent numerical optimization process. The simulation results demonstrate that the I-STC is capable of generating parallel parking trajectories with both comfort and safety.

Enhanced forest fire evacuation planning using real-time sensor and GPS algorithm

Forest fires are the source of countless fatalities and extreme economic repercussions. The safe evacuation of residents of an area affected by forest fires is the highest priority of local authorities, and finding the most optimal course of action has been a primary research focus for years. Previous studies over several decades have attempted to find an optimal solution using the applications of bug navigation systems, road network reconfiguration, graph traversals, swarm optimization, etc. The author, with the motivation to prevent human casualties at the time of such calamity, presents a novel study which solves the problem in nearly linear time computation, surpassing the performance standards of previous research, and accommodates the unpredictability of the spread of forest fires. This includes a proposal of an algorithm which builds upon the application of Spielman and Teng’s Electrical Circuit Approach to solve for maximum flow in a network and implements this with real-time sensor and Global Positioning System input.

Genotype Representation Graphs: Enabling Efficient Analysis of Biobank-Scale Data.

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. Current practice is to store large-scale genetic polymorphism data using tabular data structures and file formats, where rows and columns represent samples and genetic variants. However, encoding genetic data in such formats has become unsustainable. For example, the UK Biobank polymorphism data of 200,000 phased whole genomes has exceeded 350 terabytes (TB) in Variant Call Format (VCF), cumbersome and inefficient to work with. To mitigate the computational burden, we introduce the Genotype Representation Graph (GRG), an extremely compact data structure to losslessly present phased whole-genome polymorphisms. A GRG is a fully connected hierarchical graph that exploits variant-sharing across samples, leveraging ideas inspired by Ancestral Recombination Graphs. Capturing variant-sharing in a multitree structure compresses biobank-scale human data to the point where it can fit in a typical server's RAM (5-26 gigabytes (GB) per chromosome), and enables graph-traversal algorithms to trivially reuse computed values, both of which can significantly reduce computation time. We have developed a command-line tool and a library usable via both C++ and Python for constructing and processing GRG files which scales to a million whole genomes. It takes 160GB disk space to encode the information in 200,000 UK Biobank phased whole genomes as a GRG, more than 13 times smaller than the size of compressed VCF. We show that summaries of genetic variants such as allele frequency and association effect can be computed on GRG via graph traversal that runs significantly faster than all tested alternatives, including vcf.gz, PLINK BED, tree sequence, XSI, and Savvy. Furthermore, GRG is particularly suitable for doing repeated calculations and interactive data analysis. We anticipate that GRG-based algorithms will improve the scalability of various types of computation and generally lower the cost of analyzing large genomic datasets.

A Route Planning Method for Multiple Mobile Robots by Combining Deep Q-Network and Graph Search

A Route Planning Method for Multiple Mobile Robots by Combining Deep Q-Network and Graph Search

Automated parking trajectory planning: a hybrid curve search and nonlinear optimisation approach

ABSTRACT This paper focuses on the problem of automatic parking trajectory planning in unstructured scenarios. The proposed algorithm adopts a combination of graph search and optimization strategy. First, A* search is utilized to obtain paths connecting the global start and end points. Second, the path is divided into three segments and subpaths are searched using the hybrid A* algorithm and the proposed A*-iBspline algorithm. Finally, the three subpaths are merged and a valid initial guess is generated for the OCP. Additionally, an iterative framework is used to improve the quality of the OCP solution. Simulation results show that the proposed algorithm has a significant advantage over existing methods in terms of time and applicability.

Braess's Paradox Analog in Physical Networks of Optimal Exploration.

In stochastic exploration of geometrically embedded graphs, intuition suggests that providing a shortcut between a pair of nodes reduces the mean first passage time of the entire graph. Counterintuitively, we find a Braess's paradox analog. For regular diffusion, shortcuts can worsen the overall search efficiency of the network, although they bridge topologically distant nodes. We propose an optimization scheme under which each edge adapts its conductivity to minimize the graph's search time. The optimization reveals a relationship between the structure and diffusion exponent and a crossover from dense to sparse graphs as the exponent increases.