We investigate a class of challenging general semidefinite programming problems with extra nonconvex constraints such as matrix rank constraints. This problem has extensive applications, including combinatorial graph problems, such as MAX-CUT and com...
In recent years, the rapid development of the internet and the advancement of information technology have produced a large amount of large-scale data, some of which are presented in the form of large-scale graphs, such as social networks and sensor n...
How to learn the embedding vectors of nodes in unsupervised large-scale heterogeneous networks is a key problem in heterogeneous network embedding research. This paper proposes an unsupervised embedding learning model, named LHGI (Large-scale Heterog...
This paper investigates the formation problem of an array of large-scale mobile sensor networks. A new framework for the dynamic of mobile sensors as a continuum described by the parabolic system with boundary disturbance is proposed. The communicati...
In large-scale public security equipment projects, long-term equipment operation often leads to equipment failures and other problems, so it is particularly important to choose the appropriate operation and maintenance (O&M) scheme based on the c...
A novel algorithm was proposed for solving the max-cut problem, which seeks to identify the cut with the maximum weight in a given graph. Our technique is based on the bundle approach, applied to a newly formulated semidefinite relaxation. This resea...
For the task of answering complex logical queries on large-scale incomplete knowledge graphs, the promising approach is to embed the knowledge graph and complex logical queries into a low-dimensional space and perform iterative reasoning to find the...
At present, with the explosive growth of data scale, subgraph matching for massive graph data is difficult to satisfy with efficiency. Meanwhile, the graph index used in existing subgraph matching algorithm is difficult to update and maintain when fa...
Given a simple, undirected graph G, a biclique is a subset of vertices inducing a complete bipartite subgraph in G. In this paper, we consider two associated optimization problems, the maximum biclique problem, which asks for a biclique of the maximu...
Link prediction is a crucial problem in the analysis of graph-structured data, and graph neural networks (GNNs) have proven to be effective in addressing this problem. However, the computational and temporal costs associated with large-scale graphs r...
The poor timeliness of routing has always been an urgent problem in practical operator networks, especially in situations with large-scale networks and multiple network domains. In this article, a pruning idea of routing integrated with Dijkstra&rsqu...
Link prediction is a fundamental problem for graphs, which can reveal the potential relationships between users. Graph embedding can easily encode graph structural relations, and heterogeneous attribute features in a continuous vector space, which is...
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...
A graph is a very important structure to describe many applications in the real world. In many applications, such as dependency graphs and debt graphs, it is an important problem to find and remove cycles to make these graphs be cycle-free. The commo...
Path planning is receiving considerable interest in mobile robot research; however, a large number of redundant nodes are typically encountered in the path search process for large-scale maps, resulting in decreased algorithmic efficiency. To address...
For network graphs, numerous graph features are intimately linked to eigenvalues of the Laplacian matrix, such as connectivity and diameter. Thus, it is very important to solve eigenvalues of the Laplacian matrix for graphs. Similarly, for higher-ord...
The graph theory is one of the fundamental structures in computer science used to model various scientific and engineering problems. Many problems within the graph theory are categorized as NP-hard and NP-complete. One such problem is the minimum dom...
Large-scale botnet detection using graph neural networks (GNNs) often requires subgraph sampling to reduce computational and memory costs. However, conventional sampling strategies may fail to simultaneously preserve the global topology and local com...
A new sweep-search algorithm (SSA) is developed and tested to identify the channel geometry transitions responsible for numerical convergence failure in a Saint-Venant equation (SVE) simulation of a large-scale open-channel network. Numerical instabi...
With the growing deployment of Flying Ad hoc Networks (FANETs) in military and civilian applications, constructing a stable and efficient communication backbone has become a critical challenge. This paper tackles the Cluster Head (CH) optimization pr...
Solving maximum matching problems in bipartite graphs is critical in fields such as computational biology and social network analysis. This study introduces HybridGNN, a novel Graph Neural Network model designed to efficiently address complex matchin...
A Bayesian network (BN) is an uncertainty processing model that simulates human cognitive thinking on the basis of probability theory and graph theory. Its network topology is a directed acyclic graph (DAG) that can be manually constructed through ex...
The ability to quickly calculate or query the shortest path distance between nodes on a road network is essential for many real-world applications. However, the traditional graph traversal shortest path algorithm methods, such as Dijkstra and Floyd&n...
AI provides a new method for massive simulated data calculations in molecular dynamics, materials, and other scientific computing fields. However, the complex structures and large-scale parameters of neural network models make them difficult to devel...
Diversified top-k weight clique (DTKWC) search problem is an important generalization of the diversified top-k clique (DTKC) search problem with practical applications. The diversified top-k weight clique search problem aims to search k maximal cliqu...
With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable...
Large-scale geospatial data pave the way for geospatial machine learning algorithms, and a good representation is related to whether the machine learning model is effective. Hence, it is a critical task to learn effective feature representation for g...
Optimizing traffic signal control (TSC) at intersections continues to pose a challenging problem, particularly for large-scale traffic networks. It has been shown in past research that it is feasible to optimize the operations of individual TSC syste...
This paper presents a set of graph optimization problems related to free-space optical communication networks. Such laser-based wireless networks require a line of sight to enable communication, thus a visibility graph model is used herein. The main...
In graph theory, a “dominating vertex set” is a subset of vertices in a graph such that every vertex in the graph is either a member of the subset or adjacent to a member of the subset. In other words, the vertices in the dominating set &...
Open domain relation prediction is an important task in triples extraction. When faced with the task of constructing large-scale knowledge graph systems, with the exception of structured data, it is necessary to automatically extract triples from a l...
With the increasing number of network security threats and the frequent occurrence of software vulnerability attacks, the effective management and large-scale retrieval of vulnerability data have become urgent needs. Existing vulnerability informatio...
Scene reconstruction uses images or videos as input to reconstruct a 3D model of a real scene and has important applications in smart cities, surveying and mapping, military, and other fields. Structure from motion (SFM) is a key step in scene recons...
Nonlinear state estimation in electric power systems remains challenging under mixed-measurement conditions due to the coexistence of legacy SCADA and PMU data with markedly different reliability levels, the sensitivity of classical Gauss–Newto...
Static Timing Analysis (STA) plays a crucial role in realizing timing convergence of integrated circuits. In recent years, there has been growing research on pre-routing timing prediction using Graph Neural Networks (GNNs). However, existing approach...
Link prediction, as an important research direction in complicated network analysis, has broad application prospects. However, traditional link prediction algorithms are generally designed by the sparse expression of the adjacency matrix, which is co...
The problem of finding key players in a graph, also known as network dismantling, or network disintegration, aims to find an optimal removal sequence of nodes (edges, substructures) through a certain algorithm, ultimately causing functional indicator...
Multiple unmanned aerial vehicles (Multi-UAV) systems have recently demonstrated significant advantages in some real-world scenarios, but the limited communication range of UAVs poses great challenges to multi-UAV collaborative decision-making. By co...
Task allocation plays an important role in multi-robot systems regarding team efficiency. Conventional heuristic or meta-heuristic methods face difficulties in generating satisfactory solutions in a reasonable computational time, particularly for lar...
Link prediction in knowledge graph is the task of utilizing the existing relations to infer new relations so as to build a more complete knowledge graph. The inferred new relations plus original knowledge graph is the symmetry of completion knowledge...
Quantum computing is a promising technology that may provide breakthrough solutions to today’s difficult problems such as breaking encryption and solving large-scale combinatorial optimization problems. An algorithm referred to as Quantum Appro...
The dynamic flexible job-shop problem (DFJSP) is a realistic and challenging problem that many production plants face. As the product line becomes more complex, the machines may suddenly break down or resume service, so we need a dynamic scheduling f...
Hyperspectral image classification is a challenging and significant domain in the field of remote sensing with numerous applications in agriculture, environmental science, mineralogy, and surveillance. In the past years, a growing number of advanced...
The increasing penetration of renewable energy resources has amplified variability and uncertainty in power systems, reducing the effectiveness of conventional single-period Optimal Power Flow (OPF) strategies. Multi-period AC-OPF offers a more compr...
Meaningful representation of large-scale non-Euclidean structured data, especially in complex domains like network security and IoT system, is one of the critical problems of contemporary machine learning and deep learning. Many successful cases of g...
Bipartite networks are widely used to model interactions between two types of entities in many real-world applications, such as recommendation systems, social networks, and academic networks. Community search aims to identify cohesive groups containi...
Efficiently identifying subgraphs that match a given query graph within large-scale graphs has become a critical focus in both academic and industrial research. Subgraph matching, a fundamental problem in graph algorithms, facilitates the effective q...
With the application of graph neural network (GNN) in the communication physical layer, GNN-based channel decoding algorithms have become a research hotspot. Compared with traditional decoding algorithms, GNN-based channel decoding algorithms have a...
The Unit Commitment Problem (UCP) is a critical component of power market decision-making and is typically formulated as Mixed Integer Programming (MIP). Given the complexity of solving MIPs, efficiently solving large-scale UCPs remains a significant...
In many applications, data streams are indispensable to describe the relationships between nodes in networks, such as social networks, computer networks, and hyperlink networks. Fundamentally, a graph stream is a dynamic representation of a graph, wh...
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