Graph Routing Research Articles

Object detection with a dynamic interactive network based on relational graph routing

Combinatorial relational reasoning in neural networks used for object detection is usually static; therefore, it cannot selectively fuse visual information and semantic relations, which limits their performance. To address this problem, we propose a relational graph routing network (RGRN) that enables the dynamic interaction of visual and semantic features. The network consists of a dynamic graph network, dual path-sharing module, and relational routing interaction module. First, we used a data-driven technique to obtain the semantic information between tags from the dataset. Rich semantic information was obtained by calculating the similarity between tags. Second, the two types of semantic information were fused using a dynamic graph network to capture high-level semantic information. The visual and semantic features are then filtered and encoded through the dual path-sharing module to obtain enhanced visual and semantic features. Finally, three units were used to dynamically fuse visual and semantic information in the relational routing interaction module, which densely links the three units and routers to construct a routing space that can autonomously decide on the optimal fusion path through model learning. A series of experiments was conducted on the MS COCO dataset. RGRN achieved 54.7% box AP on object detection, which was 2.8% box AP higher than that of the Cascade Mask R-CNN. The experimental results show that the routing space enables better interaction between visual and semantic information. Therefore, our method can achieve better performance than many state-of-the-art methods.

A Novel Topological Method for Automated and Exhaustive Wire Harness Design

The current practice of manual wire harness design is labor-intensive, time-consuming, costly, and error-prone. In this paper, we present a methodology for completely automated wire harness design. We propose a topological approach that yields all the possible electrically admissible but topologically distinct harness system layouts that can be used to connect the specified terminals. Each generated layout represents a possible harness design. For layout generation, the proposed method utilizes the so-called routing graphs associated with the closed surfaces bounding the product. The developed methods are able to handle both — (1) On-Surface routing, when the wires are required to be constrained to the surface of the product, and (2) In-Air routing, when in addition to the surface the wires are also allowed to be embedded in product’s ambiance. For the final geometric embedding of the generated harnesses, we present an optimization-based methodology that determines the optimum lengths of the segments over which the wires should be bundled together. The results presented demonstrate the efficacy of the proposed approach through multiple realistic examples.

Autonomous Ride-Sharing Service Using Graph Embedding and Dial-a-Ride Problem: Application to the Last-Mile Transit in Lyon City

Autonomous vehicles are anticipated to revolutionize ride-sharing services and subsequently enhance the public transportation systems through a first–last-mile transit service. Within this context, a fleet of autonomous vehicles can be modeled as a Dial-a-Ride Problem with certain features. In this study, we propose a holistic solving approach to this problem, which combines the mixed-integer linear programming formulation with a novel graph dimension reduction method based on the graph embedding framework. This latter method is effective since accounting for heterogeneous travel demands of the covered territory tends to increase the size of the routing graph drastically, thus rendering the exact solving of small instances computationally infeasible. An application is provided for the real transport demand of the industrial district of “Vallée de la Chimie” in Lyon city, France. Instances involving more than 50 transport requests and 10 vehicles could be easily solved. Results suggest that this method generates routes of reduced nodes with lower vehicle kilometers traveled compared to the constrained K-means-based reduction. Reductions in terms of GHG emissions are estimated to be around 75% less than the private vehicle mode in our applied service. A sensitivity analysis is also provided.

Routing in circulant graphs based on a virtual coordinate system

Routing in circulant graphs based on a virtual coordinate system

Price optimal routing in public transportation

We consider the price-optimal earliest arrival problem in public transit (POEAP) in which we aim to calculate the Pareto-set of journeys with respect to ticket price and arrival time in a public transportation network. Public transit fare structures are often a combination of various fare strategies such as, e.g., distance-based fares, zone-based fares or flat fares. The rules that determine the actual ticket price are often very complex. Accordingly, fare structures are notoriously difficult to model, as it is in general not sufficient to simply assign costs to arcs in a routing graph. Research into POEAP is scarce and usually either relies on heuristics or only considers restrictive fare models that are too limited to cover the full scope of most real-world applications. We therefore introduce conditional fare networks (CFNs), the first framework for representing a large number of real-world fare structures. We show that by relaxing label domination criteria, CFNs can be used as a building block in label-setting multi-objective shortest path algorithms. By the nature of their extensive modeling capabilities, optimizing over CFNs is NP-hard. However, we demonstrate that adapting the multi-criteria RAPTOR (McRAP) algorithm for CFNs yields an algorithm capable of solving POEAP to optimality in less than 400 ms on average on a real-world dataset. By restricting the size of the Pareto-set, running times are further reduced to below 10 ms.

Solving the FPGA Routing Problem Using the Model of an Extended Mixed Routing Graph

Solving the FPGA Routing Problem Using the Model of an Extended Mixed Routing Graph

Adaptive graphical routing methodology for reducing traffic overhead in wireless sensor networks

Adaptive graphical routing methodology for reducing traffic overhead in wireless sensor networks

DR-GAT: Dynamic routing graph attention network for stock recommendation

Investors are increasingly interested in using financial technology to guide investment decisions. The phenomenon of “stock linkage” or “leading-lag” is often observed between related stocks on the stock market. Based on this feature of the stock market, complex relationships between stocks influence investment decisions. Existing methods use static prior relational data (e.g., industry relations and Wiki relations) to build corporate relation graph, while learning relational features using the same graph for all stocks. This description and use of corporate relationships ignored the dynamic nature of association relationships in time and space, limiting the ability to learn latent relationships between stocks. To address these issues, we propose a Dynamic Routing Graph Attention Network (DR-GAT) for stock recommendation. We propose a novel similarity measurement method called stock price trend similarity. To track the evolution of intercorporate relationships over time, we dynamically construct relation graph attention networks using prior knowledge and stock price trend similarity. Moreover, a newly designed relation graph router (RGR) can route each stock to an optimal relationship graph based on its volatility. Extensive experiments demonstrate the superiority of our DR-GAT method. It outperforms state-of-the-art methods achieving an average return ratio of 152% and 162% on NASDAQ and NYSE, respectively.

Multiplayer Serious Games Supporting Programming Learning

Computational thinking (CT) is crucial in education for providing a multifaceted approach to problem-solving. However, challenges exist such as supporting teachers' knowledge of CT and students' desire to learn it, particularly for non-technical students. To combat these challenges, Computer Supported Collaborative Learning (CSCL) has been introduced in classrooms and implemented using a variety of technologies, including serious games, which have been adopted across several domains aiming to appeal to various demographics and skill levels. This research focuses on a Collaborative Multiplayer Serious Game (MSG) for CT skill training. The architecture is aimed at young students and is designed to aid in the learning of programming and the development of CT skills. The purpose of this research is to conduct an empirical study to assess the multiplayer game gameplay mechanics for collaborative CT learning. The proposed game leverages a card game structure and contains complex multi-team multi-player processes, allowing students to communicate and absorb sequential and conditional logics as well as graph routing in a 2D environment. A preliminary experiment was conducted with four fourth-graders and eight sixth-graders from a French school in Morocco who have varying levels of understanding of CT. Participants were split into three groups each with two teams and were required to complete a 16-question multiple-choice quiz before and after playing the same game to assess their initial structural programming logics and the effectiveness of the MSG. Questionnaires were collected along with an interview to gather feedback on their gaming experiences and the game’s role in teaching and learning. The results demonstrate that the proposed MSG had a favourable effect on the participants’ test scores as the scores of 4 of the teams increased and 1 remained the same. All students performed well on the sequential and conditional logics, which was significantly better than the achievement of the Bebras test of the graph routing. Furthermore, according to the participants, the game provides an appealing environment that allows players to immerse themselves in the game and the competitive aspect of the game adds to its appeal and helps develop teamwork, coordination, and communication skills.

Realization of maximum flow in DTN and application in CGR

The maximum flow problem based on a contact graph in Delay-Tolerant Networking (DTN) is very important for routing and data planning. Common deterministic algorithms employing topological graphs with non-time varying to solve the maximum flow between two nodes include Dinic and Improved Shortest Augmenting Path (ISAP). However, these algorithms cannot be directly applied to topological networks with time series changes. Iosifidis.G gave a solution to this problem based on the time expansion graph, but his method requires high storage space, and an increase in the number of nodes results in increasing complexity. In this paper, we propose a method of dismantling and reconstructing the graph to solve the maximum flow problem in a continuously changing network. Compared with the Storage Policy (SP) algorithm of Iosifidis.G, this method does not require equal time slot splitting of each node, but instead uses discretization to reduce the scale of the graph. Finally, we add this algorithm to Contact Graph Routing (CGR) to optimize DTN data transmission, improve data delivery rate, and reduce unnecessary link occupation. Experimental results show that the optimized CGR can increase average delivery rate by 3% and reduce link usage by 4% compared to CGR.

A delay-tolerant network approach to satellite pickup and delivery scheduling

An approach to job scheduling and task allocation in delay- and disruption-tolerant satellite networks, called Contact Graph Scheduling (CGS) is introduced. This method aims to minimize latency between the arrival of requests for satellite data products, and the delivery of those products, identifying efficient pickup and delivery paths through a resource- constrained time-varying network. Requests for goods are processed as soon as they arrive, resulting in the discovery of task distribution and product delivery routes, while being aware of both resource and operational constraints. CGS sits at the interface between Contact Graph Routing (CGR), the dynamic pickup and delivery problem (dPDP) and satellite scheduling. CGS provides a combined scheduling-routing solution, such that the route to be taken by data toward their destination informs the allocation of pickup tasks to network nodes. Results show that CGS can increase the number of fulfilled customer requests, while maintaining acceptable levels of delivery latency, compared to other, less burdensome methods. This study highlights the impact of contact reliability on CGS performance, suggesting that as uncertainty increases, so does the benefit of employing locally, rather than centrally, defined data routing strategies.

Graph based routing algorithm for torus topology and its evaluation for the Angara interconnect

Several approaches and techniques exist to resolve load balancing problem in general and torus topology networks. Graph methods are natural ways to perform balancing of routing paths. A routing balancing algorithm must operate within the constraints of the underlying network architecture that limits several parameters, such as the number of logical paths in the network. In this paper, we consider a torus topology that is one of the common topologies that are used in high performance computing systems. We introduce a routing graph that corresponds to a interconnect topology, abstracts interconnect routing rules, providing one-to-one correspondence of network routes and graph paths. We propose a deadlock-free routing algorithm based on a fast single-source shortest path algorithm in a routing graph for the deterministic routing of a torus topology interconnect with a single virtual channel. The proposed algorithm is a tradeoff between two existing routing algorithms, also the algorithm is optimized for multidimensional torus topology and can be applied to any torus topology HPC system. We present a complete description of a routing graph that abstracts the Angara interconnect with 4D torus topology. We evaluated the proposed routing algorithm and benchmarked the maximum performance improvement of 71% for the Alltoall pattern for torus topology systems up to 432 nodes on the Angara interconnect simulator. The performance improvement of more than 5% was obtained for the NPB FT and IS application kernels on a 32-node supercomputer.

Generation of Homotopy Classes for Unconstrained 3D Wire Routing from Characteristic Loops

Routing of wire harnesses in complex machinery is a complicated problem. In this paper, we present an approach for computing all non-homotopic paths characteristic of all the homotopy classes associated with a pair of source–destination points embedded on the surface of the product. We introduce the notion of routing graphs, which are generated from specific non-trivial loops in the first homology group associated with the surface of the product. The routing graph, which is constructed only once, encodes the homotopy of the routing environment and is, therefore, used to find all the non-homotopic paths between multiple source–destination pairs. Based on the path embedding requirements, we classify the routing process into (1) On-Surface Routing, when the paths are constrained to the surface of the product, and (2) In-Air Routing, when the paths are allowed to venture into the product ambiance space. In the former case, the routing graph is defined by both the handle and tunnel loops in the first homology group, whereas in the latter case, it is defined by only the handle loops. We propose a linking number-based strategy for computing the handle and tunnel loops for a given closed surface. The methods are extended for routing in assemblies using the so-called shell structures associated with them. The results obtained demonstrate the efficacy of the proposed approach. The results show that the computation of the routing graph is very efficient, and the homotopy classes can be generated in very little time using the proposed approach.

TrackPuzzle: Efficient registration of unlabeled PDR trajectories for learning indoor route graph

This paper proposes to learn the graph of accessible indoor routes by crowd-sourcing inertial sensor data from mobile phones. The inertial sensor data collected by each phone is processed by Pedestrian Dead Reckoning (PDR) algorithm to generate motion trajectories. These motion trajectories are noisy, unlabeled, and are in different coordinate frames. Then the crucial problem to learn the route graph is reduced to firstly find associations among the motion trajectories according to intrinsic features of the trajectories, and then to register the trajectories to generate the route graph, which is called a TrackPuzzle problem. Since finding the longest common subsequence on multiple trajectories is NP-hard, a key contribution of this paper is to convert the multiple trajectory association problem to a trajectory-to-graph association problem. A trajectory is selected as a leader graph and all other trajectories are associated and registered with the leader graph in turn and expand the leader graph gradually. A dynamic programming based Longest Common Sub-sequence on Graph (LCSG) searching method is proposed to solve the trajectory-to-graph association in each step. We further employ a DBSCAN-based outlier detection method to filter out the edges in the association set which are highly inconsistent with others. Then filtered associated edges are registered non-rigidly to expand the leader graph. At last, a hierarchical graph optimization process is applied to the final graphs of different floors to generate multi-floor route graph. Extensive experiments in a synthetic environment and two real-world environments show the more feasibility, efficiency and accuracy of the proposed methods than the state-of-the-art methods.

Quantum computing for a profusion of postman problem variants

In this paper we study the viability of solving the Chinese Postman Problem, a graph routing optimization problem, and many of its variants on a quantum annealing device. Routing problem variants considered include graph type, directionally varying weights, number of parties involved in routing, among others. We put emphasis on the explanation of how to convert such problems into quadratic unconstrained binary optimization (QUBO) problems. QUBO is one of two equivalent natural paradigms for quantum annealing devices, the other being the Ising Model. We also expand upon a previously discovered algorithm for solving the Chinese Postman Problem on a closed undirected graph to decrease the number of constraints and variables used in the problem. Optimal annealing parameter settings and constraint weight values are discussed based on results from implementation on the D-Wave 2000Q and Advantage. Results from classical, purely quantum, and hybrid algorithms are compared.

CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence

3D non-rigid shape correspondence is significant but challenging in computer graphics, computer vision, and related fields. Although some deep neural networks have achieved encouraging results in shape correspondence, due to the complexity of the local deformation of non-rigid shapes, the ability of these networks to identify the spatial changes of objects is still insufficient. In this paper, we design a Component-aware Capsule Graph Network (CA-CGNet) to further address the features of embedding space based on the component constraints. Specifically, the dynamic clustering strategy is used to classify the features of patches produced by over-segmentation in order to further reduce noise interference. Moreover, aiming at the problem that existing routing ignores the embedding relationship between capsules, we propose a component-aware capsule graph routing to fully describe the relationship between capsules, which regards capsules as nodes in the graph network and constrains nodes through component information. Then, a knowledge distillation strategy is introduced to improve the convergence speed of the network by decreasing the parameters while maintaining accuracy. Finally, a component pair constraint is added to the functional map, and the component-based semantic loss function is proposed, which can compute isomeric in both direct and symmetric directions. The experimental results show that CA-CGNet improves by 10.21% compared with other methods, indicating the accuracy, generalization, and efficiency of our method on the FAUST, SCAPE, TOSCA, and KIDS datasets.

Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP

Because the operation of space networks is carefully planned, it is possible to predict future contact opportunities from link budget analysis using the anticipated positions of the nodes over time. In the standard approach to space delay-tolerant networking (DTN), such knowledge is used by contact graph routing (CGR) to decide the paths for data bundles. However, the computation assumes nearly ideal channel conditions, disregarding the impact of the convergence layer retransmissions (e.g., as implemented by the Licklider transmission protocol (LTP)). In this paper, the effect of the bundle forwarding time estimation (i.e., the link service time) to routing optimality is analyzed, and an accurate expression for lossy channels is discussed. The analysis is performed first from a general and protocol-agnostic perspective, assuming knowledge of the statistical properties and general features of the contact opportunities. Then, a practical case is studied using the standard space DTN protocol, evaluating the performance improvement of CGR under the proposed forwarding time estimation. The results of this study provide insight into the optimal routing problem for a space DTN and a suggested improvement to the current routing standard.

Mesoscopic Modeling of Airspace Traffic Flow

The aim of this paper is to present a method for composing mesoscopic simulation models from a large amount of aircraft trajectory data in the airspace. The method consists of two technologies. This first one is extraction of abstracted route graphs that indicate representative routes. The second one is extraction of statistical information on flight time along each edge of the abstracted route graphs. Since the first method was already presented in a different paper, we focus on the second technology in this paper. Finally, we show numerical results to evaluate accuracy of the simulation models.

Routing Heterogeneous Traffic in Delay-Tolerant Satellite Networks

Delay-tolerant networking (DTN) offers a novel architecture that can be used to enhance store-carry-forward routing in satellite networks. Since these networks can take advantage of scheduled contact plans, distributed algorithms like the Contact Graph Routing (CGR) can be utilized to optimize data delivery performance. However, despite the numerous improvements made to CGR, there is a lack of proposals to prioritize traffic with distinct quality of service (QoS) requirements. This study presents adaptations to CGR to improve QoS-compliant delivery ratio when transmitting traffic with different latency constraints, along with an integer linear programming optimization model that serves as a performance upper bound. The extensive results obtained by simulating different scenarios show that the proposed algorithms can effectively improve the delivery ratio and energy efficiency while meeting latency constraints.

Continuous control algorithms for conveyer belt routing based on multi-agent deep reinforcement learning

Introduction: We consider the problem of routing of piece cargo by a conveyor system. When moving cargo pieces, it is necessary not only to minimize the time of transportation, but also to minimize the energy spent on it. Purpose: Development of a routing algorithm that is adaptive to changes in the topology of the routing graph and is able to optimize the delivery time and the consumed energy. Results: We propose an algorithm based on multi-agent deep reinforcement learning that places agents at the vertices of a conveyor network graph and uses a new state value function. The algorithm has two tunable parameters: the length of the path along which the state value function is calculated, and the learning coefficient. Through the selection of parameters, we have revealed that the optimal values are 2 and 1, respectively. An experimental study of the algorithm using a simulation model has shown that it allows to reduce the number of collisions of moving objects to zero, demonstrates stable results for both optimized scores, and also leads to a lower energy consumption compared with the method used as a baseline. Practical relevance: The proposed algorithm can be used to reduce delivery time and energy when managing conveyor systems.