This paper introduces GraphTrace, a novel retrieval framework that integrates a domain-specific knowledge graph (KG) with a large language model (LLM) to improve information retrieval for complex, multi-hop queries. Built on structured economic data...
Modern communication networks may provide several heterogeneous links between the same pair of devices, including Wi-Fi, 5G, Bluetooth, and SparkLink. Existing multicast schemes often use simple-graph abstractions and therefore cannot distinguish the...
Traditional intrusion detection systems (IDSs) rely on static rules and one-dimensional features, and they have difficulty dealing with zero-day attacks and highly concealed threats; furthermore, mainstream deep learning models cannot capture the cor...
Accurately forecasting demand for intermittent items is essential for effective inventory control, improved service levels, and cost reduction. This study focuses on highly sparse, irregular, and volatile demand patterns and proposes a generalizable...
Urban anomalies such as rare facilities, unusual spatial configurations, and atypical functional patterns can provide valuable insights into urban dynamics and planning processes. However, graph-based anomaly discovery methods often suffer from insta...
Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning technique...
Vehicle detection is an important part of modern intelligent transportation systems. At present, complex deep learning algorithms are often used for vehicle detection and tracking, but high-precision detection results are often obtained at the cost o...
Decision-making in sustainable supply chains requires mathematical frameworks capable of handling uncertainty, parameterization, and relationships among alternatives. This paper proposes a Picture Fuzzy Soft Rough Graph with VIKOR (PFSRG-VIKOR) frame...
Dam deformation monitoring is essential for ensuring the safe operation of hydraulic structures, yet practical data are often compromised by missing values and noise, while spatial coupling among monitoring points further complicates prediction. To a...
In this contribution, we provide a detailed analysis of the search operation for the Interval Merging Binary Tree (IMBT), an efficient data structure proposed earlier to handle typical anomalies in the transmission of data packets. A framework is pro...
Network pharmacology examines drug action across interacting targets, pathways, and disease modules. Increasingly, however, the graph neural networks and knowledge-graph models used for this purpose are difficult to inspect. This critical narrative r...
As an emerging nano energy technology, nanogenerators have been developed rapidly, which makes it crucial to analyze the evolutionary pathways of advanced technology in this field to help estimate the development trend and direction. However, some li...
Conventional process planning methods—typically driven by rules or expert heuristics—struggle to handle complex structural components, often yielding low efficiency and limited optimization. This paper proposes an intelligent process plan...
Background: Mechanistic reporting from large-scale biomedical knowledge graphs remains challenging, particularly when integrating structured graph evidence with large language model (LLM)–based explanation in a reproducible and auditable manner...
Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and iden...
Graph Neural Networks (GNNs) face fundamental algorithmic challenges in real-world applications due to a combination of data heterogeneity, adversarial heterophily, and severe class imbalance. A critical research gap exists for a unified framework th...
Ethereum hosts more than 50 million deployed contracts, and cumulative losses from contract exploits exceed three billion US dollars, making automated detection and attacker attribution a practical priority. Existing tool-augmented language models an...
Multi-view spectral clustering provides powerful modeling tools for clustering tasks in complex scenarios by fusing the complementary information of multi-source data. However, the existing methods have the following three defects: (1) failing to ful...
Rapid urbanization and the proliferation of heterogeneous urban data have intensified the challenges of semantic interoperability and integrated urban governance. To address this, we propose the Smart City Ontology Framework (SMOF), a standards-drive...
Extracting and organizing knowledge from the agricultural crop disease research literature are challenging tasks because of the heterogeneous terminologies, complicated symptom descriptions, and unstructured nature of scientific documents. In this st...
Bundle recommendation has gained significant attention, but it faces two key challenges: sparse interaction data and complex UB, UI, and BI relations. Recent work uses multi-view contrastive learning, yet current frameworks rely on fixed-weight fusio...
Multimodal object detection based on synthetic aperture radar (SAR) and optical imagery is of great significance in remote sensing, particularly under adverse weather conditions, nighttime environments, and complex background scenarios. Although SAR...
Wind-turbine fault diagnosis is limited by incomplete fault information in single-sensor signals and the poor adaptability of fixed graph structures to non-stationary operating responses. This study proposes an adaptive correlation spatio-temporal-fr...
Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level pre...
Accurate traffic flow prediction in road networks is essential for intelligent transportation systems (ITS). Since traffic data are collected from the road network with spatial topological and time series sequences, the traffic flow prediction is reg...
Exhaust gas temperature is a critical indicator of combustion efficiency and waste heat recovery in regenerative aluminum smelting furnaces, directly governing the energy intensity and carbon footprint of secondary aluminum production—a corners...
Vehicle dispatching is essential for maintaining supply–demand balance in ride-hailing systems, but existing frameworks separate demand prediction from vehicle allocation, causing a mismatch between prediction accuracy and operational performan...
Manually annotating remote sensing images is laborious work, especially on large-scale datasets. To improve the efficiency of this work, we propose an automatic annotation method for remote sensing images. The proposed method formulates the multi-lab...
Traffic flow prediction is a key component of Intelligent Transportation Systems (ITS), crucial for alleviating urban congestion, optimizing traffic management, and improving the overall efficiency of road networks. With the rapid growth in vehicle n...
Wind power forecasting is challenging because of complex, nonlinear relationships between inherent patterns and external disturbances. Though much progress has been achieved in deep learning approaches, existing methods cannot effectively decompose a...
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Traffic speed prediction is an essential part of urban transportation systems that contributes to minimizing the environmental pollution caused by vehicle emissions. The existing traffic speed prediction studies have achieved good results, but some c...
In order to explore complex structures and relationships hidden in data, plenty of graph-based dimensionality reduction methods have been widely investigated and extended to the multi-view learning field. For multi-view dimensionality reduction, the...
The results of aerial scene classification can provide valuable information for urban planning and land monitoring. In this specific field, there are always a number of object-level semantic classes in big remote-sensing pictures. Complex label-space...
How can complex relationships among molecular or clinico-pathological entities of neurological disorders be represented and analyzed? Graphs seem to be the current answer to the question no matter the type of information: molecular data, brain images...
Multi-source spatio-temporal data analysis is an important task in the development of smart cities. However, traditional data analysis methods cannot adapt to the growth rate of massive multi-source spatio-temporal data and explain the practical sign...
In the last decade, graph embedding-based dimensionality reduction for multi-view data has been extensively studied. However, constructing a high-quality graph for dimensionality reduction is still a significant challenge. Herein, we propose a new al...
To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source het...
Graph-enhanced Retrieval-Augmented Generation (RAG) frameworks, such as GraphRAG, improve large language model (LLM)-based question answering (QA) by constructing and leveraging structured, knowledge-condensed graph information. However, they still f...
To address the limitations of existing graph contrastive learning methods, which fail to adaptively integrate feature and topological information and struggle to efficiently capture multi-hop information, we propose an adaptive multi-view parallel gr...
High-dimensional unlabeled data often contain complex latent structures that are easily obscured by redundant features, noise, and unreliable neighborhood relationships. Although graph-based learning provides an effective means of preserving sample r...
In the past decade, multi-view clustering has received a lot of attention due to the popularity of multi-view data. However, not all samples can be observed from every view due to some unavoidable factors, resulting in the incomplete multi-view clust...
Large-scale and stochastic electric vehicle (EV) charging behaviors pose challenges to the grid operation due to their spatial heterogeneity and temporal variability. This paper proposes a Multiple Charging Station (MCS) Collaborative Load Forecastin...
Twitter, as a popular social network, has been targeted by different bot attacks. Detecting social bots is a challenging task, due to their evolving capacity to avoid detection. Extensive research efforts have proposed different techniques and approa...
Traditional SLAM methods face significant challenges in complex environments, including high computational complexity, ambiguous data association, and limited real-time performance. Existing approaches often rely on explicit data association or compu...
Accurate short-term photovoltaic (PV) power forecasting is essential for power system scheduling and market operations. Existing studies have shown the value of numerical weather prediction (NWP), graph-based spatial modeling, and temporal sequence l...
Accurate measurement of ocean wave parameters is paramount for offshore engineering design and marine environmental monitoring. Distributed pressure sensing technology provides a robust data foundation for analyzing the spatio-temporal characteristic...
Multi-view representation learning, aiming to extract discriminative patterns from heterogeneous data sources, has attracted increasing attention in recent years. A central challenge in multi-view data fusion lies in determining the appropriate weigh...
Background/Objectives: Integrating multi-omics data for cancer prognosis remains a challenging problem in bioinformatics because molecular profiles are high-dimensional, heterogeneous, and structured by incomplete biological relationships. Pathway da...
Blockchain networks have become a major target for fraudulent activities, including phishing and scamming attacks, due to the irreversible and pseudonymous nature of cryptocurrency transactions. Existing Ethereum fraud detection studies commonly form...
We introduce the concept of a k-dimensional matrix product D of k matrices
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