Search Results (584)

Spammer group detection is necessary for curbing collusive review spammers on online shopping websites. However, the current detection approaches ignore exploring deep-level suspicious user review relationships and learning group features with low discrimination, which affects detection performance. Furthermore, the interpretation of detection results is easily influenced by noise features and unimportant group structures, leading to suboptimal interpretation performance. Aimed at addressing these concerns, we propose an interpretable review spammer group detection model based on knowledge distillation and counterfactual generation. First, we analyze user review information to generate a suspicious user review relationship graph, combining a graph agglomerative hierarchical clustering approach to discover candidate groups. Second, we devise a knowledge distillation network to learn discriminative candidate group features for detecting review spammer groups. Finally, we design a counterfactual generation model to search important subgraph structures for interpreting the detection results. The experiments indicate that the improvements in our model’s Precision@k and Recall@k are among the top-1000 state-of-the-art solutions on the Amazon, YelpChi, YelpNYC, and YelpZip datasets, which are [13.37%, 72.63%, 37.46%, and 18.83%] and [17.34%, 43.81%, 41.22%, and 21.05%], respectively. The Fidelities of our interpretation results under different Sparsity are around 6%, 7%, 7%, and 6% higher than that of the state-of-the-art solutions on the Amazon, YelpChi, YelpNYC, and YelpZip datasets, respectively. Full article

In this paper, we introduce an advanced Fuzzy Record Similarity Metric (FRMS) that improves approximate record matching and models human perception of record similarity. The FRMS utilizes a newly developed similarity space with favorable properties combined with a metric space, employing a bag-of-words model with general applications in text mining and cluster analysis. To optimize the FRMS, we propose a two-stage method for approximate string matching and search that outperforms baseline methods in terms of average time complexity and F measure on various datasets. In the first stage, we construct an optimal Q-gram count filter as an optimal lower bound for fuzzy token similarities such as FRMS. The approximated Q-gram count filter achieves a high accuracy rate, filtering over 99% of dissimilar records, with a constant time complexity of $\approx \mathcal{O}\left(1\right)$. In the second stage, FRMS runs for a polynomial time of approximately $\approx \mathcal{O}\left(n^4\right)$ and models human perception of record similarity by maximum weight matching in a bipartite graph. The FRMS architecture has widespread applications in structured document storage such as databases and has already been commercialized by one of the largest IT companies. As a side result, we explain the behavior of the singularity of the Q-gram filter and the advantages of a padding extension. Overall, our method provides a more accurate and efficient approach to approximate string matching and search with real-time runtime. Full article

This paper is focused on some algebraic and combinatorial properties of a TMTO (Time–Memory Trade-Off) for a chosen plaintext attack against a cryptosystem with a perfect secrecy property. TMTO attacks aim to retrieve the preimage of a given one-way function more efficiently than an exhaustive search and with less memory than a dictionary attack. TMTOs for chosen plaintext attacks against cryptosystems with a perfect secrecy property are associated with some directed graphs, which can be defined by suitable collections of multisets called Brauer configurations. Such configurations induce so-called Brauer configuration algebras, the algebraic and combinatorial invariant analysis of which is said to be a Brauer analysis. In this line, this paper proposes formulas for dimensions of Brauer configuration algebras (and their centers) induced by directed graphs defined by TMTO attacks. These results are used to provide some set-theoretical solutions for the Yang–Baxter equation. Full article

Diffusion is well known for its success in improving retrieval performance by exploiting the local structure of data distribution. Some recent works have focused on improving its efficiency by shifting the computing burden offline. However, we find that efficient offline diffusion handles continuously updating datasets with difficulty, which directly hinders its application in the real world. Unlike previous methods that apply diffusion to the entire gallery, we introduce an anchor graph to serve as an agent of the complete gallery graph. By doing that, we empower diffusion with the ability of retrieving newly added images at acceptable computational cost. We demonstrate that our proposed method is a good approximation of diffusion featuring fast online search speed and the ability of handling growing data. Moreover, experiments on benchmark datasets show that the proposed method outperforms the state of the art by a large margin with proper parameter settings. Full article

Water quality management in large basins demands tools that balance scientific rigor with computational efficiency to avoid paralysis by analysis. While traditional models offer detailed insights, their complexity and resource intensity hinder timely decision-making. To address this gap, we present AFAR-WQS, an open-source MATLAB™ toolbox that introduces a novel integration of assimilation factors with graph theory and a Depth-First Search (DFS) algorithm to rapidly simulate 13 water quality determinants across complex topological networks. AFAR-WQS resolves cumulative processes in networks of up to 30,000 segments in just 163 s on standard hardware, enabling real-time scenario evaluations. Its object-oriented architecture ensures scalability, allowing customization for urban drainage systems or macro-basin studies while maintaining computational efficiency. Case studies demonstrate its utility in prioritizing sanitation investments, assessing water quality at the national scale and fostering stakeholder collaboration through participatory workshops. By bridging the gap between simplified and complex models, AFAR-WQS supports adaptive management in contexts of hydrological uncertainty, regulatory compliance, and climate change. The toolbox is freely available at GitHub, offering a transformative approach for integrated water resource management. Full article

In this paper, a novel Doppler shift estimation method for frequency diverse array (FDA) radar based on graph signal processing (GSP) theory is proposed and investigated. First, a well-designed graph signal model for a monostatic linear FDA is formulated. Subsequently, spectral decomposition is conducted on the constructed signal model utilizing graph Fourier transform (GFT) techniques, enabling the extraction of the target’s Doppler shift parameter through spectral peak search. A comprehensive series of simulation experiments demonstrates that the proposed method can achieve the accurate estimation of target parameters even under low signal-to-noise ratio (SNR) conditions. Furthermore, the proposed method exhibits superior performance compared to the MUSIC algorithm, offering enhanced resolution and estimation accuracy. Additionally, the method is highly amenable to parallel processing, significantly reducing the computational burden associated with traditional procedures. Full article

Entity Alignment (EA) in Knowledge Graphs (KGs) is a crucial task for the integration of multiple KGs, facilitating the amalgamation of multi-source knowledge and enhancing support for downstream applications. In recent years, unsupervised EA methods have demonstrated remarkable efficacy in leveraging graph structures or utilizing auxiliary information. However, the increasing complexity of many modeling methods limits their applicability to large KGs in real-world scenarios. Given that most EA encoders primarily focus on modeling one-hop neighborhoods within the KG’s graph structure while neglecting similarities among multi-hop neighborhoods, we propose an efficient and effective unsupervised EA method, MPGT-Align, based on a multi-hop pruning graph transformer. The core innovation of MPGT-Align lies in mining multi-hop neighborhood features of entities through two components: Pruning-hop2Token and Attention-based Transformer encoder. The former aggregates only those multi-hop neighborhoods that contribute to alignment targets, inspired by search pruning algorithms. The latter empowers MPGT-Align to adaptively extract more effective alignment information from both entity itself and its multi-hop neighbors. Furthermore, Pruning-hop2Token serves as a non-parametric method that not only reduces model parameters, but also allows MPGT-Align to be trained with small batch sizes, thereby enabling efficient handling of large KGs. Extensive experiments conducted across various benchmark datasets demonstrate that our method consistently outperforms most existing supervised and unsupervised EA techniques. Full article

Graph search techniques are increasingly vital for applications involving labeled or textual content on network vertices. A key task is the top-k nearest keyword (kNK) search on undirected graphs where a query vertex and keywords identify k closest vertices containing the keywords. With cloud storage widely used for outsourcing graph services, ensuring data privacy and security is critical. Existing solutions employ encrypted indexes for privacy-preserving keyword searches but lack fine-grained access control, limiting their ability to accommodate diverse user needs. To address this, we propose privacy-preserving hierarchical top-k nearest keyword search on graphs (PH-kNK), a novel scheme enhancing privacy-preserving top-k keyword searches by integrating hierarchical access control. PH-kNK introduces hierarchical query entry indexes that regulate access at multiple security levels, significantly improving privacy, security and adaptability. The granular query entry indexes established by our approach enables users with higher security levels to query the graph structure and access corresponding vertices while maintaining transparency for lower-level users. The scheme leverages pseudo-random mapping, order-preserving encryption and re-encryption of search indexes to ensure robust data security. Experimental results on real-world datasets demonstrate the scheme’s high efficiency and validate its security. Full article

This systematic review examines the integration of directed acyclic graph (DAG)-based blockchain technology in smart mobility ecosystems, focusing on electric vehicles (EVs), robotic systems, and drone swarms. Adhering to PRISMA guidelines, we conducted a comprehensive literature search across Web of Science, Scopus, IEEE Xplore, and ACM Digital Library, screening 1248 records to identify 47 eligible studies. Our analysis demonstrates that DAG-based blockchain addresses critical limitations of traditional blockchains by enabling parallel transaction processing, achieving high throughput (>1000 TPS), and reducing latency (<1 s), which are essential for real-time applications like autonomous vehicle coordination and microtransactions in EV charging. Key technical challenges include consensus mechanism complexity, probabilistic finality, and vulnerabilities to attacks such as double-spending and Sybil attacks. This study identifies five research priorities: (1) standardized performance benchmarks, (2) formal security proofs for DAG protocols, (3) hybrid consensus models combining DAG with Byzantine fault tolerance, (4) privacy-preserving cryptographic techniques, and (5) optimization of feeless microtransactions. These advancements are critical for deploying robust, scalable DAG-based solutions in smart mobility, and fostering secure and efficient urban transportation networks. Full article

In this work, an efficient graph-induced neighborhood search heuristic is proposed to address the capacitated multicommodity network design problem. This problem, which commonly arises in transportation and telecommunication, is well known for its inherent complexity and is often classified as $\mathcal{NP}$-hard. Our approach commences with an arbitrary feasible solution and iteratively improves it by solving a series of small-scale auxiliary mixed-integer programming problems. These small-scale problems are closely tied to the cycles inherent in the network topology, enabling us to reroute the flow more effectively. Furthermore, we have developed a novel resource-efficient facility assignment technique that departs from standard variable neighborhood search algorithms. By solving a series of small knapsack problems, this technique not only enhances the quality of solutions further but also can serve as a primary heuristic to generate initial feasible solutions. Furthermore, we theoretically guarantee that our algorithm will always produce an integer-feasible solution within polynomial time. The experimental results highlight the superior performance of our method compared to other existing approaches. Our heuristic algorithm efficiently discovers high-quality feasible solutions, substantially reducing the computation time and number of nodes in the branch-and-bound tree. Full article

Background: In unresectable pleural mesothelioma, pemetrexed+cisplatin as first line is considered the standard of care, but novel treatments have been recently proposed. Methods: Our objective was to compare, albeit indirectly, the results of randomized controlled trials on overall survival (OS). The IPDfromKM method was employed for reconstruct individual patient data (IPD) from the graphs of Kaplan–Meier curves. Cox statistics was run to estimate hazard ratios (HRs). Results: After a literature search on Medline (via PubMed) and Scopus databases, six randomized controlled trials were identified in which five new treatments (nivolumab plus ipilimumab, bevacizumab plus pemetrexed plus cisplatin, chemotherapy plus pembrolizumab, ONCOS-102 plus pemetrexed plus cisplatin/carboplatin and cediranib plus pemetrexed+cisplatin with maintenance with cediranib) were evaluated. In five trials, pemetrexed plus cisplatin was the standard of care given to the control arms. Nivolumab plus ipilimumab, bevacizumab plus pemetrexed plus cisplatin and chemotherapy plus pembrolizumab showed a significantly better OS compared with controls. ONCOS-102 plus pemetrexed plus cisplatin/carboplatin did not significantly improve OS. In contrast, OS worsened with cisplatin alone and with cediranib plus pemetrexed+cisplatin with maintenance with cediranib. Discussion: Our analysis indicates that, in patients with unresectable pleural mesothelioma, three of the five novel treatments provided a significant survival benefit compared with the standard of care. Further research is needed to confirm the OS benefit found in our analysis with some treatments, whereas cisplatin alone and cediranib plus pemetrexed+cisplatin with maintenance with cediranib do not seem to deserve further research. Full article

With the increasing utilization of drones, the cyber security threats they face have become more prominent. Code reuse in the software development of drone systems has led to vulnerabilities in drones. The binary code similarity analysis method offers a way to analyze drone firmware lacking source code. This paper proposes DEGNN, a novel graph neural network for binary code similarity analysis. It uses call-enhanced control graphs and attention mechanisms to generate dual embeddings of functions and predict similarity based on graph structures and node features. DEGNN is effective in cross-architecture tasks. Experimental results show that in the cross-architecture binary function search, DEGNN’s mean reciprocal rank and recall@1 surpass the state of the art by 12% and 28.6%, respectively. In the cross-architecture real-world vulnerability search, specifically targeting drone systems, it has a 33.3% performance improvement over the SOTA model, indicating its great potential in enhancing drone cyber security. Full article

This paper presents a path-planning approach for tethered robots. The proposed planner finds paths that minimize the tether tension due to tether–obstacle and tether–floor interaction. The method assumes that the tether is managed externally by a tether management system and pulled by the robot. The planner is initially formulated for ground robots in a 2D environment and then extended for 3D scenarios, where it can be applied to tethered aerial and underwater vehicles. The proposed approach assumes a taut tether between two consecutive contact points and knowledge of the coefficient of friction of the obstacles present in the environment. The method first computes the visibility graph of the environment, in which each node represents a vertex of an obstacle. Then, a second graph, named the tension-aware graph, is built so that the tether–environment interaction, formulated in terms of tension, is computed and used as the cost of the edges. A graph search algorithm (e.g., Dijkstra) is then used to compute a path with minimum tension, which can help the tethered robot reach longer distances by minimizing the tension required to drag the tether along the way. This paper presents simulations and a real-world experiment that illustrate the characteristics of the method. Full article

Scheduling is essential in managing projects. ‘Schedula Anima’ is a new software designed to provide a comprehensive view of schedules between early and late dates for construction project managers. Capturing the dynamic nature of projects, it offers improved visualization through an animation process that creates incremental frames of bar charts and the corresponding resource graphs. As activity delays are simulated, it is observed that delays earlier in the schedule have more significant effects on project completion. A new prioritization method is introduced to evaluate the ease of rescheduling activities. A metric for monitoring resource usage float is presented, and the search space for resource utilization is delineated. As resource smoothing is studied in the resource usage graph and the time domain, a correlation is discovered between resource smoothness and the float consumption rate. It is shown that the schedule and resource usage graph comprises five sub-areas representing different risk exposures. Animation also improves communication in project teams and is beneficial in education. Finally, it is discovered that the permutations of activities in the simulation form a group. Enhancing our perception of resource utilization and the management of delays, ‘Schedula Anima’ brings a renewed perspective to project scheduling. Full article

Navigatingdensely connected networks can be complex due to the different connection structures present within a network. No explicit algorithms are designed specifically for this navigation, so heuristic approaches and existing network systems are often employed. However, this task can become computationally asymmetrical, as the complexity of creating a representation of the city is lower than the complexity involved in identifying a set of feasible paths in a combinatorial order. This paper extends the applicability of morphological approaches to compute the shortest path in smart cities, driven by the complexity and size of the vital communication infrastructure. As is well known, this communication infrastructure changes dynamically, particularly with the evolving connection paths due to continuous population growth. Consequently, efficient communication trajectories can quickly become obsolete. The challenge of computing the best trajectories to respond more quickly to the growing population comes with high computational complexity. This paper presents an application that uses a discrete algorithm designed to compute the shortest path through a morphological approach. Specifically, it seeks to identify the best trajectory within a densely populated city based on a complex density graph. By incorporating morphological approaches into path-search algorithms, we can define a new family of methods that operate in discrete spaces with a morphological representation, resulting in approaches that have lower computational requirements. Other well-known applications in this context include the delivery of resources, such as managing electrical power consumption or minimizing time delays in resource delivery. This task is essential but classified as an NP problem, making it an appropriate scenario for applying the proposed algorithm to navigate a dense graph. The paper highlights the well-known problem of finding the shortest path as one of the potential applications of the introduced algorithm. The algorithm aims to identify the optimal path trajectory within a graph representing a dense city’s real scenario. This discussion compares and contrasts the proposal with other established approaches, highlighting the advantages and characteristics of the proposed method. Full article