Reachability Path Research Articles

Fuzzy Linear Temporal Logic with Quality Constraints

As an extension of quantitative temporal logic, uncertain temporal logic essentially describes the temporal behavior of uncertain and incomplete systems, thus better solving search and decision-making problems in such systems. Fuzzy linear temporal logic (FLTL) is a focal point in uncertain temporal logic research. However, there are evident shortcomings in the current research outcomes. First, in previous FLTL studies, the practice of obtaining path reachability and formula satisfaction values independently and subsequently selecting the smaller of the two as the satisfaction value metric led to information loss. Furthermore, this simplistic information fusion approach fails to reflect the varying importance of these two types of information to the requirements. Second, computing path reachability and temporal logic formula satisfaction values separately may result in a mismatch between the two pieces of information with respect to the same path segment. Thus, the primary challenge lies in accurately integrating the satisfaction values of temporal logic formulas with the path reachability of the segments that yields these satisfaction values, utilizing various reasonable information synthesis methods, to ensure synchronization between path reachability and formula satisfaction values without incurring information loss. Additionally, it is crucial to reflect the different preference requirements for these two types of information. Moreover, the temporal logic formula characterizes system properties, with its sub-formulas delineating distinct sub-properties. Consequently, considering the varying importance preferences of sub-formulas is also significant. To address these deficiencies, we introduced quality constraint operators into FLTL, resulting in quality-constrained fuzzy linear temporal logic (QFLTL). This incorporation enables the synchronization and comprehensive fusion of path-reachability information and formula satisfaction values within the final semantic metric, thereby resolving the issues related to information synchronization and loss. Furthermore, it can accommodate the differing preference requirements between the two types of information and sub-properties during the information synthesis process. We defined the syntax and semantics of QFLTL and examined its expressive power and properties. Notably, we investigated the decidability of logical decision problems in QFLTL, encompassing validity, satisfiability, and model-checking issues. We proposed corresponding solution algorithms and analyzed their complexities.

A global and local integrated dynamic path planning algorithm for village roads region

In China, the village roads are characterized by numerous intersections and significant differences in road widths, creating a complex maze-like terrain. This undoubtedly increases the difficulty of path planning for autonomous vehicles. This study proposes an improved bidirectional RRT* algorithm that utilizes the advantages of the rapid random search of the RRT* algorithm. It introduces virtual points to address the irregularity of road networks and creates enveloping circles at expanding nodes to enhance path reachability, thus obtaining the optimal global planning path. To enhance path tracking comfort, a fifth-order B-spline curve is utilized to smooth the global path, and local path planning is performed using Quadratic Programming (QP). The proposed combined global and local path planning method was evaluated through Co-simulation experiments basing on the Matlab/CarSim/PreScan platform. Simulation results demonstrate that the enhanced RRT* algorithm outperforms the traditional RRT* algorithm in the same scenario. Specifically, the improved algorithm reduces the running time by 29.56%, increases node utilization by approximately 15.33%, and decreases the planned path length by 2.8%. Additionally, the vehicle’s final lateral tracking error was controlled within 0–0.04 m, and the longitudinal tracking error was controlled within 0–0.1 m, fully demonstrating the vehicle’s excellent path-tracking performance. The study’s innovative ideas will offer methodological support for researching path planning for autonomous vehicles in specific scenarios.

Hierarchical Knowledge-Enhancement Framework for multi-hop knowledge graph reasoning

Multi-hop reasoning has gained significant attention in the area of knowledge graph completion, intending to predict missing facts through existing knowledge. However, due to sparsity knowledge and lack of reachable paths for the model to make sensible decisions, most multi-hop reasoning models suffer from degradation performance on the sparse knowledge graphs (KGs). Faced with this challenge, we propose the Hierarchical Knowledge-Enhancement Framework (Hi-KnowE), which improves reasoning performance over sparse KGs by conducting hierarchical dynamic path completion under various background knowledge guidance. Hi-KnowE is based on hierarchical reinforcement learning, breaking the task into high-level and low-level processes. Firstly, we apply high-level policy to reason relations and the relation action space is enlarged under rule guidance. Secondly, tail entities are obtained by low-level policy and semantic knowledge is used to refine the entity action space. The sparsity problem is alleviated by generating reliable information. Moreover, we introduce rule-based inner attention, adopting the overall semantics of relevant rules to assist agent reasoning. This strategy makes the path for the agent more logical and flexible. We further evaluate Hi-KnowE on four sparse datasets extracted from Wikidata and FreeBase. The results outperform the baseline models, demonstrating improvements of up to 1.5% and 1.9% in MRR and Hit@10.

Object-oriented concept acquisition based on attribute topology

The research of object-oriented concept is one of the basic contents of formal concept analysis. To overcome the complexity of computing object-oriented concept, this paper proposes an Object-oriented Concept Acquisition model (OCA) based on attribute topology. The object-oriented attribute topology is first proposed to visualize the coupling relationship between attributes. Second, inspired by rough set theory, object-oriented attribute topology is transformed into rough object-oriented attribute topology. Furthermore, based on the weights of the edges in the rough object-oriented attribute topology, object-oriented concepts are obtained by finding reachable paths. Finally, examples and experiments are used to demonstrate the effectiveness of our proposed method.

Deep Reinforcement Learning for Intelligent Penetration Testing Path Design

Penetration testing is an important method to evaluate the security degree of a network system. The importance of penetration testing attack path planning lies in its ability to simulate attacker behavior, identify vulnerabilities, reduce potential losses, and continuously improve security strategies. By systematically simulating various attack scenarios, it enables proactive risk assessment and the development of robust security measures. To address the problems of inaccurate path prediction and difficult convergence in the training process of attack path planning, an algorithm which combines attack graph tools (i.e., MulVAL, multi-stage vulnerability analysis language) and the double deep Q network is proposed. This algorithm first constructs an attack tree, searches paths in the attack graph, and then builds a transfer matrix based on depth-first search to obtain all reachable paths in the target system. Finally, the optimal path for target system attack path planning is obtained by using the deep double Q network (DDQN) algorithm. The MulVAL double deep Q network(MDDQN) algorithm is tested in different scale penetration testing environments. The experimental results show that, compared with the traditional deep Q network (DQN) algorithm, the MDDQN algorithm is able to reach convergence faster and more stably and improve the efficiency of attack path planning.

Recursive State Machine Guided Graph Folding for Context-Free Language Reachability

Context-free language reachability (CFL-reachability) is a fundamental framework for program analysis. A large variety of static analyses can be formulated as CFL-reachability problems, which determines whether specific source-sink pairs in an edge-labeled graph are connected by a reachable path, i.e., a path whose edge labels form a string accepted by the given CFL. Computing CFL-reachability is expensive. The fastest algorithm exhibits a slightly subcubic time complexity with respect to the input graph size. Improving the scalability of CFL-reachability is of practical interest, but reducing the time complexity is inherently difficult. In this paper, we focus on improving the scalability of CFL-reachability from a more practical perspective---reducing the input graph size. Our idea arises from the existence of trivial edges, i.e., edges that do not affect any reachable path in CFL-reachability. We observe that two nodes joined by trivial edges can be folded---by merging the two nodes with all the edges joining them removed---without affecting the CFL-reachability result. By studying the characteristic of the recursive state machines (RSMs), an alternative form of CFLs, we propose an approach to identify foldable node pairs without the need to verify the underlying reachable paths (which is equivalent to solving the CFL-reachability problem). In particular, given a CFL-reachability problem instance with an input graph G and an RSM, based on the correspondence between paths in G and state transitions in RSM, we propose a graph folding principle, which can determine whether two adjacent nodes are foldable by examining only their incoming and outgoing edges. On top of the graph folding principle, we propose an efficient graph folding algorithm GF. The time complexity of GF is linear with respect to the number of nodes in the input graph. Our evaluations on two clients (alias analysis and value-flow analysis) show that GF significantly accelerates RSM/CFL-reachability by reducing the input graph size. On average, for value-flow analysis, GF reduces 60.96% of nodes and 42.67% of edges of the input graphs, obtaining a speedup of 4.65× and a memory usage reduction of 57.35%. For alias analysis, GF reduces 38.93% of nodes and 35.61% of edges of the input graphs, obtaining a speedup of 3.21× and a memory usage reduction of 65.19%.

SpreadRank: A Novel Approach for Identifying Influential Spreaders in Complex Networks.

Identifying influential spreaders in complex networks is critical for information spread and malware diffusion suppression. In this paper, we propose a novel influential spreader identification method, called SpreadRank, which considers the path reachability in information spreading and uses its quantitative index as a measure of node spread centrality to obtain the spread influence of a single node. To avoid the overlapping of the influence range of the node spread, this method establishes a dynamic influential node set selection mechanism based on the spread centrality value and the principle of minimizing the maximum connected branch after network segmentation, and it selects a group of nodes with the greatest overall spread influence. Experiments based on the SIR model demonstrate that, compared to other existing methods, the selected influential spreaders of SpreadRank can quickly diffuse or suppress information more effectively.

Path reachability including distance-constrained detours

When nodes and/or links are down in a network, the network may not function normally. Most of the existing work focuses on the reachability between two nodes along a path, that is, path reliability, and that through arbitrary paths, that is, network reliability. However, in case of wireless multi-hop networks and road networks, it may be inefficient or difficult to recalculate a path from the source to the destination when a failure occurs at an intermediate link in the path. In such cases, we can expect that the reachability between two nodes will improve by taking a detour from the entry of the failure link (i.e. failure point) to the destination without traversing the failure link. Since the detour may also increase the communication/travel delay, in this paper, we propose a new path metric (i.e. path reachability including distance-constrained detours), which consists of the conventional path reachability and the reachability along distance-constrained detours under arbitrary link failures in the original path. We first prove the two important characteristics: (1) the proposed metric is exactly the same as the network reliability in case of no distance constraint and (2) it is upper bounded by the diameter constrained network reliability. Through numerical results using a grid network and more realistic networks (i.e. wireless networks and a road network), we show the fundamental characteristics of the proposed metric and analyze the goodness of several representative paths in terms of the proposed metric as well as the conventional metrics (i.e. path length and path reachability).

A sequential-path tree-based centrality for identifying influential spreaders in temporal networks

The problem of identifying influential spreaders in temporal networks has attracted extensive attention in recent years. Existing studies have proposed various centrality measures for quantifying the influence of nodes based on the structures of temporal networks, such as temporal degree centrality and temporal closeness centrality. However, most existing methods only take into account a single feature of nodes, while ignoring other temporal features (e.g., the propagation time, or the path length from an infected node to a destination node). In this paper, we propose a new centrality measure, namely as sequential-path tree-based centrality (SPT-C) which takes into account three different temporal features based on a new representation structure of temporal networks (i.e., sequential-path tree). The three temporal features include propagation time which measures the time that an infection propagates from an infected node to another, hop count which denotes the number of intermediate nodes that an infection propagates from an infected node to another, and reachable paths which represent the number of different time-respecting paths from an infected node to another. The evaluation experiments on 12 real-world temporal networks show that the effectiveness of our SPT-C in identifying influential spreaders is superior to other baseline measures.

An Efficient Index-Based Approach to Distributed Set Reachability on Small-World Graphs

Set reachability query in directed graphs has a plethora of graph-based applications such as dependency analysis and graph centrality calculation. Given two sets <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> of source and target vertices, set reachability query needs to acquire all pairs <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$s{\in }S$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$t{\in }T$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> can reach <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> . The state-of-the-art approach distributed set reachability (DSR) investigates the set reachability query in a distributed environment and adopts a static graph-based index to enhance the query efficiency. Nevertheless, DSR needs to store the graph-based index in all partitions, which causes a huge space overhead. Furthermore, it cannot efficiently solve the negative query <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> cannot reach <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> , since DSR needs to traverse the whole reachable paths and becomes unable to efficiently reduce the computations. To alleviate these issues, we propose a novel multi-level 2-hop (ML2hop) index for the set reachability query in a distributed environment. Based on ML2hop, we further present a bi-directional query algorithm, called MLQA, to achieve efficient support for both positive and negative queries in Pregel-like systems. Generally, MLQA is equipped with the following three significant properties: (1) Low computation costs. It reduces redundant local computations in each partition by controlling the rounds of path traversals. (2) Low communication costs. It restricts the message exchange among different partitions within one single round with guaranteed accuracy of query results. (3) High parallelism. It adopts a bi-directional query technique for message propagation, achieving the better query efficiency than the forward-traversal query strategy utilized in DSR. Experimental results over several real-world graphs demonstrate that MLQA significantly outperforms the state-of-the-art algorithm by up to two orders of magnitude speedup.

Finding Multidimensional Constraint Reachable Paths for Attributed Graphs

A graph acts as a powerful modelling tool to represent complex relationships between objects in the big data era. Given two vertices, vertex and edge constraints, the multidimensional constraint reachable ( MCR) paths problem finds the path between the given vertices that match the user-specified constraints. A significant challenge is to store the graph topology and attribute information while constructing a reachability index. We propose an optimized hashing-based heuristic search technique to address this challenge while solving the multidimensional constraint reachability queries. In the proposed technique, we optimize hashing and recommend an efficient clustering technique based on matrix factorization. We further extend the heuristic search technique to improve the accuracy. We experimentally prove that our proposed techniques are scalable and accurate on real and synthetic datasets. Our proposed extended heuristic search technique is able to achieve an average execution time of 0.17 seconds and 2.55 seconds on MCR true queries with vertex and edge constraints for Robots and Twitter datasets respectively.

GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network

Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations. Recent attempts either rely on explicit path reachability to leverage path-based semantic relatedness or graph neighborhood to learn heterogeneous network representations before predictions. These weakly coupled manners overlook the rich interactions among neighbor nodes, which introduces an early summarization issue. In this article, we propose GraphHINGE ( H eterogeneous IN teract and aggre G at E ), which captures and aggregates the interactive patterns between each pair of nodes through their structured neighborhoods. Specifically, we first introduce Neighborhood-based Interaction (NI) module to model the interactive patterns under the same metapaths, and then extend it to Cross Neighborhood-based Interaction (CNI) module to deal with different metapaths. Next, in order to address the complexity issue on large-scale networks, we formulate the interaction modules via a convolutional framework and learn the parameters efficiently with fast Fourier transform. Furthermore, we design a novel neighborhood-based selection (NS) mechanism, a sampling strategy, to filter high-order neighborhood information based on their low-order performance. The extensive experiments on six different types of heterogeneous graphs demonstrate the performance gains by comparing with state-of-the-arts in both click-through rate prediction and top-N recommendation tasks.

Evidential link prediction method based on the importance of high-order path index

Link prediction, aiming to find missing links in an observed network or predict those links that may occur in the future, has become a basic challenge of network science. Most existing link prediction methods are based on local or global topological attributes of the network such as degree, clustering coefficient, path index, etc. In the process of resource allocation, as the number of connections between the common neighbors of the paired nodes increases, it is easy to leak information through them. To overcome this problem, we proposed a new similarity index named ESHOPI (link prediction based on Dempster–Shafer theory and the importance of higher-order path index), which can prevent information leakage by penalizing ordinary neighbors and considering the information of the entire network and each node at the same time. In addition, high-order paths are used to improve the performance of link prediction by penalizing the longer reachable paths between the seed nodes. The effectiveness of ESHOPI is shown by the experiments on both synthetic and real-world networks.

Differential Resting-State Networks in Major Depressive Disorders After One-Week Antidepressant Medication

The dynamic architecture of the human brain has been consistently observed. However, there is still limited modeling work to elucidate how neuronal circuits are hierarchically and flexibly organized in functional systems. Here we proposed a reachable probability approach based on non-homogeneous Markov chains, to characterize all possible connectivity flows and the hierarchical structure of brain functional systems at the dynamic level. We proved at the theoretical level the convergence of the functional brain network system, and demonstrated that this approach is able to detect network steady states across connectivity structure, particularly in areas of the default mode network. We further explored the dynamically hierarchical functional organization centered at the primary sensory cortices. We observed smaller optimal reachable steps to their local functional regions, and differentiated patterns in larger optimal reachable steps for primary perceptual modalities. The reachable paths with the largest and second largest transition probabilities between primary sensory seeds via multisensory integration regions were also tracked to explore the flexibility and plasticity of the multisensory integration. The present work provides a novel approach to depict both the stable and flexible hierarchical connectivity organization of the human brain.

A reachable probability approach for the analysis of spatio-temporal dynamics in the human functional network

The dynamic architecture of the human brain has been consistently observed. However, there is still limited modeling work to elucidate how neuronal circuits are hierarchically and flexibly organized in functional systems. Here we proposed a reachable probability approach based on non-homogeneous Markov chains, to characterize all possible connectivity flows and the hierarchical structure of brain functional systems at the dynamic level. We proved at the theoretical level the convergence of the functional brain network system, and demonstrated that this approach is able to detect network steady states across connectivity structure, particularly in areas of the default mode network. We further explored the dynamically hierarchical functional organization centered at the primary sensory cortices. We observed smaller optimal reachable steps to their local functional regions, and differentiated patterns in larger optimal reachable steps for primary perceptual modalities. The reachable paths with the largest and second largest transition probabilities between primary sensory seeds via multisensory integration regions were also tracked to explore the flexibility and plasticity of the multisensory integration. The present work provides a novel approach to depict both the stable and flexible hierarchical connectivity organization of the human brain.

A Robot-Centered Path-Planning Algorithm for Multidirectional Additive Manufacturing for WAAM Processes and Pure Object Manipulation

Previous algorithms for slicing, path planning or trajectory planning of additive manufacturing cannot be used consistently for multidirectional additive manufacturing with pure object manipulation in wire-arc additive manufacturing. This work presents a novel path planning approach that directly takes robot kinematics into account and thus ensures the reachability of all critical path poses. In an additional step, the planned path segments are smoothed so that joint velocity limits are respected. It is shown that the implemented path planner generates executable robot paths and at the same time maintains the process quality (in this case, sufficient coverage of the slice area). While the introduced method enables the generation of reachable printing paths, the smoothing algorithm allows for the execution of the path with respect to the robot’s velocity limits and at the same time improves the slice coverage. Future experiments will show the realization of the real robot setup presented.

A behavioral propagation and competition model based on pressure

The influence of node behavior by the relevant group behavior in complex networks is a topic of recent interest. In order to measure the direct and indirect influence of the neighborhoods, the behavioral propagation and competition model was established based on the pressure. The pressure is described by the impact of group behavior on nodes, which is related to the length and number of reachable paths between two nodes for measuring the nodal behavioral influence. In addition, the pressure range has an effect on the pressure. By modeling and analyzing the change of nodes motivation and the rules of behavioral propagation, and numerical simulations are performed on the small-world networks and the scale-free networks. The results show that pressure is the major factor in the node behavioral motivation, where the pressure generated from behavior in related group network is dependent on the relative location and number of participators. At the same time, network structure also plays an important role at behavior propagation process. Further, competition arises when multiple behaviors are spread among people, while winning behaviors are widely spread among people.

ProSy: API-Based Synthesis with Probabilistic Model

Program synthesis is an exciting topic that desires to generate programs satisfying user intent automatically. But in most cases, only small programs for simple or domain-specific tasks can be synthesized. The major obstacle of synthesis lies in the huge search space. A common practice in addressing this problem is using a domain-specific language, while many approaches still wish to synthesize programs in general programming languages. With the rapid growth of reusable libraries, component-based synthesis provides a promising way, such as synthesizing Java programs which are only composed of APIs (application programming interfaces). However, the efficiency of searching for proper solutions for complex tasks is still a challenge. Given an unfamiliar programming task, programmers would search for API usage knowledge from various coding resources to reduce the search space. Considering this, we propose a novel approach named ProSy to synthesize API-based programs in Java. The key novelty is to retrieve related knowledge from Javadoc and Stack Overflow and then construct a probabilistic reachability graph. It assigns higher probabilities to APIs that are more likely to be used in implementing the given task. In the synthesis process, the program sketch with a higher probability will be considered first; thus, the number of explored reachable paths would be decreased. Some extension and optimization strategies are further studied in the paper. We implement our approach and conduct several experiments on it. We compare ProSy with SyPet and other state-of-the-art API-based synthesis approaches. The experimental results show that ProSy reduces the synthesis time of SyPet by up to 80%.

Context-Aware Link Embedding with Reachability and Flow Centrality Analysis for Accurate Speed Prediction for Large-Scale Traffic Networks

This paper presents a novel method for predicting the traffic speed of the links on large-scale traffic networks. We first analyze how traffic flows in and out of every link through the lowest cost reachable paths. We aggregate the traffic flow conditions of the links on every hop of the inbound and outbound reachable paths to represent the traffic flow dynamics. We compute a new measure called traffic flow centrality (i.e., the Z value) for every link to capture the inherently complex mechanism of the traffic links influencing each other in terms of traffic speed. We combine the features regarding the traffic flow centrality with the external conditions around the links, such as climate and time of day information. We model how these features change over time with recurrent neural networks and infer traffic speed at the subsequent time windows. Our feature representation of the traffic flow for every link remains invariant even when the traffic network changes. Furthermore, we can handle traffic networks with thousands of links. The experiments with the traffic networks in the Seoul metropolitan area in South Korea reveal that our unique ways of embedding the comprehensive spatio-temporal features of links outperform existing solutions.

Network Analysis of Swine Shipments in China: The First Step to Inform Disease Surveillance and Risk Mitigation Strategies.

China's pork industry has been dramatically changing in the last few years. Pork imports are increasing, and small-scale farms are being consolidated into large-scale multi-site facilities. These industry changes increase the need for traceability and science-based decisions around disease monitoring, surveillance, risk mitigation, and outbreak response. This study evaluated the network structure and dynamics of a typical large-scale multi-site swine facility in China, as well as the implications for disease spread using network-based metrics. Forward reachability paths were used to demonstrate the extent of epidemic spread under variable site and temporal disease introductions. Swine movements were found to be seasonal, with more movements at the beginning of the year, and fewer movements of larger pigs later in the year. The network was highly egocentric, with those farms within the evaluated production system demonstrating high connectivity. Those farms which would contribute the highest epidemic potential were identified. Among these, different farms contributed to higher expected epidemic spread at different times of the year. Using these approaches, increased availability of swine movement networks in China could help to identify priority locations for surveillance and risk mitigation for both endemic problems and transboundary diseases such as the recently introduced, and rapidly spreading, African swine fever virus.