Graph neural networks (GNNs) have been highly successful in graph representation learning. The goal of GNNs is to enrich node representations by aggregating information from neighboring nodes. Much work has attempted to improve the quality of aggrega...
Graph representation learning has become a mainstream method for processing network structured data, and most graph representation learning methods rely heavily on labeling information for downstream tasks. Since labeled information is rare in the re...
Traffic forecasting in large-scale urban networks must operate reliably under imperfect sensing conditions, where measurements may contain noise or missing values. Most existing spatio-temporal graph neural networks focus primarily on modeling spatia...
Hyper-graphs offer the opportunity to formulate logical statements about their components, for example, using Horn clauses. Several models of Information Systems can be represented using hyper-graphs as the workflows, i.e., the business processes. Du...
Canonical ensembles of walks in a finite connected graph assign the properly normalized probability distributions to all nodes, subgraphs, and nodal subsets of the graph at all time and connectivity scales of the diffusion process. The probabilistic...
Due to their wide application in many disciplines, how to make an efficient ranking for nodes, especially for nodes in graph data, has aroused lots of attention. To overcome the shortcoming that most traditional ranking methods only consider the mutu...
Clustering ensemble is a research hotspot of data mining that aggregates several base clustering results to generate a single output clustering with improved robustness and stability. However, the validity of the ensemble result is usually affected b...
Accurate network traffic prediction is important for the autonomy, resilience and resource orchestration of 6G and AI-native communication infrastructures, while also supporting green networking and digital twin network applications. However, existin...
Higher-order information brings significant accuracy gains in semantic dependency parsing. However, modeling higher-order information is non-trivial. Graph neural networks (GNNs) have been demonstrated to be an effective tool for encoding higher-orde...
Dynamic vehicular integrated sensing and communication (ISAC) requires representations that remain compact, decision-relevant, and structurally stable as mobility rewires interference and sensing relations. This paper presents IC-GMRO, a conditional...
There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different typ...
Recently, there has been a dramatic rise in the demand for accurate temperature forecasts. However, challenges arise from modeling and fusing complex spatial and temporal features in temperature data. In this study, we propose a physics-informed dire...
Knowledge graph-based question answering is an intelligent approach to deducing the answer to a natural language question from structured knowledge graph information. As one of the mainstream knowledge graph-based question answering approaches, infor...
In this paper, we address the problem of autonomous exploration in unknown environments for ground mobile robots with deep reinforcement learning (DRL). To effectively explore unknown environments, we construct an exploration graph considering histor...
Predicting wind speed over the ocean is difficult due to the unequal distribution of buoy stations and the occasional fluctuations in the wind field. This study proposes a dynamic graph embedding-based graph neural network—long short-term memor...
Personalized recommendation has become an essential component of intelligent information systems and electronic multimedia platforms. However, cold-start items with limited user–item interactions remain difficult to model, especially when colla...
Multi-source heterogeneous data in substation secondary systems are typically characterized by high entropy and disorder, which pose significant challenges for cross-modal information integration and efficient retrieval. Therefore, a method for multi...
Retrieval-Augmented Generation (RAG) mitigates hallucination and knowledge staleness in large language models (LLMs) by injecting external knowledge into the context window, yet its effectiveness is rigidly constrained by attention dilution and infer...
Along with the popularity of mobile Internet and smart applications, more and more high-dimensional sensor data have appeared, and these high-dimensional sensor data have hidden information about system performance degradation, system failure, etc.,...
Effective data interoperability and schedule analysis play a significant role in improving the management of prefabricated buildings. However, there is a lack of efficient strategies and comprehensive approaches for data interoperability and data-bas...
Knowledge graph completion (KGC) utilizes known knowledge graph triples to infer and predict missing knowledge, making it one of the research hotspots in the field of knowledge graphs. There are still limitations in generating high-quality entity emb...
Summarizing the causation of aviation accidents is conducive to enhancing aviation safety. The knowledge graph of aviation accident causation, constructed based on aviation accident reports, can assist in analyzing the causes of aviation accidents. W...
Paraphrase generation is an important yet challenging task in natural language processing. Neural network-based approaches have achieved remarkable success in sequence-to-sequence learning. Previous paraphrase generation work generally ignores syntac...
Trust prediction is essential to enhancing reliability and reducing risk from the unreliable node, especially for online applications in open network environments. An essential fact in trust prediction is to measure the relation of both the interacti...
To semantically understand remote sensing images, it is not only necessary to detect the objects in them but also to recognize the semantic relationships between the instances. Scene graph generation aims to represent the image as a semantic structur...
Biomedical texts are relatively obscure in describing relations between specialized entities, and the automatic extraction of drug–drug or drug–disease relations from massive biomedical texts presents a challenge faced by many researchers...
Information security compliance has become critical for organizations worldwide, with the ISO/IEC 27000 family serving as the most widely adopted framework for establishing information security management systems. Despite their global acceptance, the...
Information popularity prediction is a critical problem in social network analysis. With the increasing prevalence of social platforms, accurate prediction of the diffusion process has become increasingly important. Existing methods mainly rely on gr...
Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the piv...
The widespread use of social media has led to a rise in online bullying and abusive language. Huge amounts of content are created on social media daily, creating a challenge for platforms to manually detect and remove offensive posts, making automate...
Identification of protein-protein interactions (PPIs) plays an essential role in the understanding of protein functions and cellular biological activities. However, the traditional experiment-based methods are time-consuming and laborious. Therefore,...
This paper proposes a KeyBERT-based method for constructing a knowledge graph of the electronic information curriculum system, aiming to enhance the structured representation and relational analysis of educational content. Electronic Information Engi...
The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving...
Urban building energy prediction is a critical challenge for sustainable city planning and large-scale retrofit prioritization. However, traditional data-driven models struggle to capture real urban environments’ spatial and morphological compl...
Graph data and multi-sensor information fusion have been integrated into the abnormal vibration type classification and the identification of the rolling mill for extracting spatial–temporal and robust features. However, most of the existing de...
Accurate bus passenger flow prediction is a critical prerequisite for optimizing resource allocation in intelligent bus systems and improving public transport service quality. However, due to dynamic fluctuations in passenger travel demand and the co...
Flight dynamics modeling is a fundamental cornerstone of aircraft design, simulation, and control. Traditional approaches rely on aerodynamic look-up tables for numerical integration, which suffer from high data-acquisition costs, poor extrapolation...
The present paper offers, in its first part, a unified approach for the derivation of families of inequalities for set functions which satisfy sub/supermodularity properties. It applies this approach for the derivation of information inequalities wit...
Name ambiguity, due to the fact that many people share an identical name, often deteriorates the performance of information integration, document retrieval and web search. In academic data analysis, author name ambiguity usually decreases the analysi...
The big data revolution transformed how we think of data analytics in many ways. Critical amongst them are the somewhat interconnected ideas of volunteered geographic information, crowdsourcing, and the big data property of variety. The robust litera...
The automated identification and evaluation of potential attack paths within infrastructures is a critical aspect of cybersecurity risk assessment. However, existing methods become impractical when applied to complex infrastructures. While machine le...
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...
This study introduces the Graph-Structured Physics-Informed DeepONet (GS-PI-DeepONet), a novel neural network framework designed to address the challenges of solving parametric Partial Differential Equations (PDEs) in structural analysis, particularl...
As an emerging distributed energy system, microgrid power flow prediction plays a crucial role in optimizing energy dispatch and power grid operation. Traditional methods of power flow prediction mainly rely on statistics and time series models, negl...
Infrared thermal field (ITF) prediction for meter boxes is crucial for the early warning of power system faults, yet this method faces three major challenges: data sparsity, complex geometry, and resource constraints in edge computing. Existing physi...
We investigate the impact of textual content and its structural characteristics on the detection of false information. We propose a Bidirectional Graph Convolutional Neural Network (ICP-BGCN) that integrates message content with its propagation paths...
Information cascades in online social networks arise from stochastic interactions among user behavior, temporal activation, and graph-structured exposure. Early prediction of cascade outcomes remains difficult because only a short diffusion prefix is...
The knowledge graph was first used in the information search of the Internet as a way to improve the quality of the search because it contains a huge amount of structured knowledge data. In this paper, the knowledge map algorithm is studied through n...
Occluded person re-identification (Re-ID) aims to accurately match occluded pedestrian images against complete gallery images captured across multiple cameras, a task that is critical to public security and intelligent surveillance systems. Existing...
Targeting the joint challenges posed by sparse graph topology, limited semantic expressiveness, and scarce annotation resources that commonly afflict knowledge graphs in the oil and gas pipeline safety domain, this paper presents a Multi-Information...
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