A multicomplex structure is defined from an ordered lattice of multigraphs. This structure will help us to observe the features of persistent homology in this context, its interaction with the ordering, and the repercussions of merging multigraphs in...
We presented a method based on multigraphs to mathematically define a distribution function in time for the generation of data exchange in a special-purpose communication network. This is needed for the modeling and design of communication networks (...
We study the line graphs of bipartite multigraphs, which naturally arise in combinatorics, game theory, and applications such as scheduling and motion planning. We introduce a new characterization of these graphs via valid partial assignments of the...
Recently, Multi-Graph Learning was proposed as the extension of Multi-Instance Learning and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on Multi-Graph Multi-Label Learning, where each objec...
The enumeration of chemical graphs plays a crucial role in cheminformatics and bioinformatics, especially in the search for novel drug discovery. These graphs are usually tree-like multigraphs, or they consist of tree-like multigraphs attached to a c...
This paper presents an efficient algorithm for matching subgraph queries in a multi-graph based on features-based indexing techniques. The KD-tree data structure represents these nodes’ features, while the set-trie index data structure represen...
The increase in motorized vehicles in cities and the inefficient use of parking spaces have exacerbated parking difficulties in cities. To effectively improve the utilization rate of parking spaces, it is necessary to accurately predict future parkin...
In air traffic systems, aircraft trajectories between airports are monitored by the radar networking system forming dynamic air traffic flow. Accurate airport arrival flow prediction is significant in implementing large-scale intelligent air traffic...
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...
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...
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...
Temporal information plays a central role in shaping the structure of a network. In this paper, we consider the impact of an object on network structure over time. More specifically, we use a novel object-based dynamic measure to reflect the extent t...
Unsupervised band selection has gained increasing attention recently since massive unlabeled high-dimensional data often need to be processed in the domains of machine learning and data mining. This paper presents a novel unsupervised HSI band select...
Deep forgery detection plays a crucial role in addressing the challenges posed by the rapid spread of deeply generated content that significantly erodes public trust in online information and media. Deeply forged images typically present subtle but s...
Geolocating Twitter users from social media data holds significant value in applications such as targeted advertising, disaster response, and social network analysis. However, existing social network-based geolocation methods tend to focus primarily...
Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic sp...
With the advent of the era of rapid information expansion, the massive data backlog that exists on the Internet has led to a serious information overload problem, which makes recommendation systems a crucial part of human life. In particular, the Poi...
Human Pose Estimation (HPE) aims to accurately locate the positions of human key points in images or videos. However, the performance of HPE is often significantly reduced in practical application scenarios due to environmental interference. To addre...
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...
Analogies are an important part of human cognition for learning and discovering new concepts. There are many different approaches to defining analogies and how new ones can be found or constructed. We propose a novel approach in the tradition of stru...
Eigenvector-based centrality captures recursive notions of importance in networks. While the direct problem computes centrality from given edge weights, the inverse eigenvector centrality problem seeks edge weights that reproduce a prescribed central...
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...
Brain imaging genetics examines associations between imaging quantitative traits (QTs) and genetic factors such as single nucleotide polymorphisms (SNPs) to provide important insights into the pathogenesis of Alzheimer’s disease (AD). The indiv...
From the perspective of process, time may be viewed as that which marks the occurrence of change, as previously proposed by this author. In contrast, spatial distinctions may be viewed as enabling the individuation and counting of events generated by...
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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...
Hyperspectral image (HSI) data classification often faces the problem of the scarcity of labeled samples, which is considered to be one of the major challenges in the field of remote sensing. Although active deep networks have been successfully appli...
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks u...
In Mongolian Text Sentiment Analysis (MTSA), the scarcity of annotated sentiment datasets and the insufficient consideration of syntactic dependency and topological structural information pose significant challenges to accurately capturing semantics...
Matching constraints in an analog integrated circuit (IC) are critical to optimizing layout performance. To extract these matching constraints accurately and efficiently from the netlist, we propose the heterogeneous matching constraint extraction gr...
The effectiveness of depth information in saliency detection has been fully proved. However, it is still worth exploring how to utilize the depth information more efficiently. Erroneous depth information may cause detection failure, while non-salient...
The presented herb recommendation system aims to analyze the patients’ symptoms and recommends a set of herbs as the prescription to treat diseases. In addition to symptoms, the patients’ personal properties and induced diagnoses are also...
The transfer learning method, based on unsupervised domain adaptation (UDA), has been broadly utilized in research on fault diagnosis under variable working conditions with certain results. However, traditional UDA methods pay more attention to extra...
Recommending safe and effective medication (drug) combinations is a key application of artificial intelligence in healthcare. Current methods often focus solely on recommendation accuracy while neglecting drug–drug interactions (DDIs), or overl...
Chromatin–chromatin interactions and three-dimensional (3D) spatial structures are involved in transcriptional regulation and have a decisive role in DNA replication and repair. To understand how individual genes and their regulatory elements f...
The prediction of bike-sharing demand plays a pivotal role in the optimization of intelligent transportation systems, particularly amidst the COVID-19 pandemic, which has significantly altered travel behaviors and demand dynamics. In this study, we e...
Traffic flow prediction plays a vital role in intelligent transportation systems, directly affecting travel scheduling, road planning, and traffic management efficiency. However, traditional methods often struggle to capture complex spatiotemporal de...
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...
Bitcoin’s pseudonymous ledger is widely exploited for money laundering, and the Elliptic++ dataset—the only large public resource with linked transaction- and wallet-level illicit-activity labels—makes it possible to study both gran...
Graph convolution network-based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing. Each relationship represents a kind of spatial dependency, such as region-wise distance or f...
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...
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a signif...
In the field of research into vessel type recognition utilizing trajectory data, researchers have primarily concentrated on developing models based on trajectory sequences to extract the relevant information. However, this approach often overlooks th...
Real-time and accurate traffic forecasting aids in traffic planning and design and helps to alleviate congestion. Addressing the negative impacts of partial data loss in traffic forecasting, and the challenge of simultaneously capturing short-term fl...
The code of industrial management software typically features few system API calls and a high number of customized variables and structures. This makes the similarity of such codes difficult to compute using text features or traditional neural networ...
The generalized Petersen graphs are a type of cubic graph formed by connecting the vertices of a regular polygon to the corresponding vertices of a star polygon. This graph has many interesting graph properties. As a result, it has been widely resear...
Accurate ultra-short-term wind power forecasting at the turbine level is important for grid stability and dispatching. To address the time-varying spatial and temporal correlations among multiple turbines in a single wind farm, we build dynamic spati...
The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways t...
We study the problem of color-avoiding and color-favored percolation in a network, i.e., the problem of finding a path that avoids a certain number of colors, associated with vulnerabilities of nodes or links, or is attracted by them. We investigate...
It is easily observed that the vertices of a simple graph cannot have pairwise distinct degrees. This means that no simple graph of the order of at least two is, in this way, irregular. However, a multigraph can be irregular. Chartrand et al., in 198...
Suffering from the multi-view data diversity and complexity, most of the existing graph convolutional networks focus on the networks’ architecture construction or the salient graph structure preservation for node classification in citation netw...
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