Graph Applications Research Articles

Some new product operations of T-spherical fuzzy graphs and an application of T-spherical fuzzy graphs in MCGDM

Some new product operations of T-spherical fuzzy graphs and an application of T-spherical fuzzy graphs in MCGDM

The application of knowledge graphs in the Chinese cultural field: the ancient capital culture of Beijing

A methodology is proposed to introduce knowledge graphs into the study of the Chinese cultural field for use in a newly designed, complete application. At present, the combination of culture and information technology has become a trend. Among various technologies, knowledge graphs are a very promising option. The contributions of this paper are as follows: it supplies for the first time a knowledge graph in the cultural field of the ancient capital of Beijing, establishes a domain knowledge base, and develops a platform for visual analysis and interactive question and answer. In this process, a framework for applying knowledge graphs to research in the cultural field is summarized, providing ideas for research in the cultural field.

A Survey on Multimodal Knowledge Graphs: Construction, Completion and Applications

As an essential part of artificial intelligence, a knowledge graph describes the real-world entities, concepts and their various semantic relationships in a structured way and has been gradually popularized in a variety practical scenarios. The majority of existing knowledge graphs mainly concentrate on organizing and managing textual knowledge in a structured representation, while paying little attention to the multimodal resources (e.g., pictures and videos), which can serve as the foundation for the machine perception of a real-world data scenario. To this end, in this survey, we comprehensively review the related advances of multimodal knowledge graphs, covering multimodal knowledge graph construction, completion and typical applications. For construction, we outline the methods of named entity recognition, relation extraction and event extraction. For completion, we discuss the multimodal knowledge graph representation learning and entity linking. Finally, the mainstream applications of multimodal knowledge graphs in miscellaneous domains are summarized.

A Hierarchical Parallel Graph Summarization Approach Based on Ranking Nodes

Graph summarization techniques are vital in simplifying and extracting enormous quantities of graph data. Traditional static graph structure-based summarization algorithms generally follow a minimum description length (MDL) style, and concentrate on minimizing the graph storage overhead. However, these methods also suffer from incomprehensive summary dimensions and inefficiency problems. In addition, the need for graph summarization techniques often varies among different graph applications, but an ideal summary method should generally retain the important characteristics of the key nodes in the final summary graph. This paper proposes a novel method based on ranking nodes, called HRNS, that follows a hierarchical parallel graph summarization approach. The HRNS first preprocesses the node ranking using a hybrid weighted importance strategy, and introduces the node importance factor into traditional MDL-based summarization algorithms; it then leverages a hierarchical parallel process to accelerate the summary computation. The experimental results obtained using both real and simulated datasets show that HRNS can efficiently extract nodes with high importance, and that the average importance over six datasets ranges from 0.107 to 0.167; thus, HRNS can achieve a significant performance gain on speedups, as the sum error ratios are also lower than the methods traditionally used.

Group Cooperative Teaching Design With Knowledge Graphs in Project-Driven Learning

Using the educational knowledge graph to express the logical characteristics of knowledge, this paper takes project-driven learning in the teaching of “Information Technology in Secondary Schools” as an example and studies the group cooperative teaching mode based on the educational knowledge graph. The relationship between knowledge points in subject courses is described in the form of a knowledge graph, and the learning activities of coordinating the cooperation of each group are completed through the integration process of the knowledge graph. The use of a group cooperation teaching mode in the classroom can not only build a collaborative knowledge system between teachers and students but also help to carry out project-driven learning. The application of the knowledge graph of “Information Technology in Middle School” in the classroom can better show the logic between knowledge, and its graphical structure can help students to structure the knowledge they have learned.

Disease Prediction Using Graph Machine Learning Based on Electronic Health Data: A Review of Approaches and Trends

Graph machine-learning (ML) methods have recently attracted great attention and have made significant progress in graph applications. To date, most graph ML approaches have been evaluated on social networks, but they have not been comprehensively reviewed in the health informatics domain. Herein, a review of graph ML methods and their applications in the disease prediction domain based on electronic health data is presented in this study from two levels: node classification and link prediction. Commonly used graph ML approaches for these two levels are shallow embedding and graph neural networks (GNN). This study performs comprehensive research to identify articles that applied or proposed graph ML models on disease prediction using electronic health data. We considered journals and conferences from four digital library databases (i.e., PubMed, Scopus, ACM digital library, and IEEEXplore). Based on the identified articles, we review the present status of and trends in graph ML approaches for disease prediction using electronic health data. Even though GNN-based models have achieved outstanding results compared with the traditional ML methods in a wide range of disease prediction tasks, they still confront interpretability and dynamic graph challenges. Though the disease prediction field using ML techniques is still emerging, GNN-based models have the potential to be an excellent approach for disease prediction, which can be used in medical diagnosis, treatment, and the prognosis of diseases.

Knowledge-Aware Enhanced Network Combining Neighborhood Information for Recommendations

In recent years, the application of knowledge graphs to alleviate cold start and data sparsity problems of users and items in recommendation systems, has aroused great interest. In this paper, in order to address the insufficient representation of user and item embeddings in existing knowledge graph-based recommendation methods, a knowledge-aware enhanced network, combining neighborhood information recommendation (KCNR), is proposed. Specifically, KCNR first encodes prior information about the user–item interaction, and obtains the user’s different knowledge neighbors by propagating them in the knowledge graph, and uses a knowledge-aware attention network to distinguish and aggregate the contributions of the different neighbors in the knowledge graph, as a way to enrich the user’s description. Similarly, KCNR samples multiple-hop neighbors of item entities in the knowledge graph, and has a bias to aggregate the neighborhood information, to enhance the item embedding representation. With the above processing, KCNR can automatically discover structural and associative semantic information in the knowledge graph, and capture users’ latent distant personalized preferences, by propagating them across the knowledge graph. In addition, considering the relevance of items to entities in the knowledge graph, KCNR has designed an information complementarity module, which automatically shares potential interaction characteristics of items and entities, and enables items and entities to complement the available information. We have verified that KCNR has excellent recommendation performance through extensive experiments in three real-life scenes: movies, books, and music.

Accelerating Graph Computations on 3D NoC-Enabled PIM Architectures

Graph application workloads are dominated by random memory accesses with the poor locality. To tackle the irregular and sparse nature of computation, ReRAM-based Processing-in-Memory (PIM) architectures have been proposed recently. Most of these ReRAM architecture designs have focused on mapping graph computations into a set of multiply-and-accumulate (MAC) operations. ReRAMs also offer a key advantage in reducing memory latency between cores and memory by allowing for PIM. However, when implemented on a ReRAM-based manycore architecture, graph applications still pose two key challenges—significant storage requirements (particularly due to wasted zero cell storage), and significant amount of on-chip traffic. To tackle these two challenges, in this article, we propose the design of a 3D NoC-enabled ReRAM-based manycore architecture. Our proposed architecture incorporates a novel crossbar-aware node reordering to reduce ReRAM storage requirements. Secondly, its 3D NoC-enabled design reduces on-chip communication latency. Our architecture outperforms the state-of-the-art in ReRAM-based graph acceleration by up to 5× in performance while consuming up to 10.3× less energy for a range of graph inputs and workloads.

Construction and Application of a Knowledge Graph for Iron Deposits Using Text Mining Analytics and a Deep Learning Algorithm

Construction and Application of a Knowledge Graph for Iron Deposits Using Text Mining Analytics and a Deep Learning Algorithm

Accelerating matrix-centric graph processing on GPUs through bit-level optimizations

Even though it is well known that binary values are common in graph applications (e.g., adjacency matrix), how to leverage the phenomenon for efficiency has not yet been adequately explored. This paper presents a systematic study on how to unlock the potential of the bit-level optimizations of graph computations that involve binary values. It proposes a two-level representation named Bit-Block Compressed Sparse Row (B2SR) and presents a series of optimizations to the graph operations on B2SR by the intrinsics of modern GPUs. It additionally introduces Deep Reinforcement Learning (DRL) as an efficient way to best configure the bit-level optimizations on the fly. The DQN-based adaptive tile size selector with dedicated model training can reach 68% prediction accuracy. Evaluations on the NVIDIA Pascal and Volta GPUs show that the optimizations bring up to 40▪ and 6555▪ for essential GraphBLAS kernels SpMV and SpGEMM, respectively, accelerating GraphBLAS-based BFS by up to 433▪, SSSP, PR, and CC 35▪, and TC 52▪.

SPG: Structure-Private Graph Database via SqueezePIR

Many relational data in our daily life are represented as graphs, making graph application an important workload. Because of the large scale of graph datasets, moving graph data to the cloud becomes a popular option. To keep the confidential and private graph secure from an untrusted cloud server, many cryptographic techniques are leveraged to hide the content of the data. However, protecting only the data content is not enough for a graph database. Because the structural information of the graph can be revealed through the database accessing track. In this work, we study the graph neural network (GNN), an important graph workload to mine information from a graph database. We find that the server is able to infer which node is processing during the edge retrieving phase and also learn its neighbor indices during GNN's aggregation phase. This leads to the leakage of the information of graph structure data. In this work, we present SPG, a structure-private graph database with SqueezePIR. Our SPG is built on top of Private Information Retrieval (PIR), which securely hides which nodes/neighbors are accessed. In addition, we propose SqueezePIR, a compression technique to overcome the computation overhead of PIR. Based on our evaluation, our SqueezePIR achieves 11.85× speedup on average with less than 2% accuracy loss when compared to the state-of-the-art FastPIR protocol.

Development and Application of Knowledge Graphs for the Injection Molding Process

Injection molding, the most common method used to process plastics, is a technique with a high knowledge content; however, relevant knowledge has not been systematically organized, and as a result, there have been many bottlenecks in talent cultivation. Moreover, most of the knowledge stored in books and online articles remains in the form of unstructured data, while some even remains unwritten, resulting in many difficulties in the construction of knowledge bases. Therefore, how to extract knowledge from unstructured data and engineers’ statements is a common goal of many enterprises. This study introduced the concept of a Knowledge Graph, a triplet extraction model based on bidirectional encoder representations from transformers (BERT) which was used to extract injection molding knowledge entities from text data, as well as the relationships between such entities, which were then stored in the form of knowledge graphs after entity alignment and classification with sentence-bidirectional encoder representations from transformers. In a test, the triplet extraction model achieved an F1 score of 0.899, while the entity alignment model and the entity classification model achieved accuracies of 0.92 and 0.93, respectively. Finally, a web platform was built to integrate the functions to allow engineers to expand the knowledge graphs by inputting learning statements.

Defending against adversarial attacks on graph neural networks via similarity property

Graph Neural Networks (GNNs) are powerful tools in graph application areas. However, recent studies indicate that GNNs are vulnerable to adversarial attacks, which can lead GNNs to easily make wrong predictions for downstream tasks. A number of works aim to solve this problem but what criteria we should follow to clean the perturbed graph is still a challenge. In this paper, we propose GSP-GNN, a general framework to defend against massive poisoning attacks that can perturb graphs. The vital principle of GSP-GNN is to explore the similarity property to mitigate negative effects on graphs. Specifically, this method prunes adversarial edges by the similarity of node feature and graph structure to eliminate adversarial perturbations. In order to stabilize and enhance GNNs training process, previous layer information is adopted in case a large number of edges are pruned in one layer. Extensive experiments on three real-world graphs demonstrate that GSP-GNN achieves significantly better performance compared with the representative baselines and has favorable generalization ability simultaneously.

PERBANDINGAN PENERAPAN GRAF KOMPATIBEL DENGAN REALITA DILAPANGAN DALAM PENGATURAN LAMPU LALU LINTAS PERSIMPANGAN BANDARA PEKANBARU

To minimize traffic congestion, the application of compatible graphs is used to establish a more optimal total waiting duration at the traffic flow at the Pekanbaru airport intersection. Traffic flow at the Pekanbaru airport intersection is more congested on Sunday due to weekends, where this study was conducted on Sunday afternoon, May 29, 2022. The purpose is to optimize the total waiting duration of the existing traffic lights in the area, waiting for the vehicle stop time obtained in all aspects of the lane, from the observations that have been made obtained compatible graphs. By applying compatible graphs, two different hypotheses are used. The first hypothesis turns left following a traffic light which is 100 seconds. Meanwhile, the second hypothesis of turning left does not follow a traffic light of 120 seconds. Where the wait duration obtained is the optimum waiting duration with a compatible graph, the waiting duration in the current field is much more optimal than the waiting time after using the compatible graph

Maximal Quasi-Cliques Mining in Uncertain Graphs

Cohesive subgraph mining is a fundamental problem in the field of graph data analysis. Many existing cohesive graph mining algorithms are mainly tailored to deterministic graphs. Real-world graphs, however, are often not deterministic, but uncertain in nature. Applications of such uncertain graphs include protein-protein interactions networks with experimentally inferred links and sensor networks with uncertain connectivity links. In this article, we study the problem of mining cohesive subgraphs from an uncertain graph. Specifically, we introduce a new <inline-formula><tex-math notation="LaTeX">$(\alpha,\gamma)$</tex-math></inline-formula> -quasi-clique model to model the cohesive subgraphs in an uncertain graph, and propose a basic enumeration algorithm to find all maximal <inline-formula><tex-math notation="LaTeX">$(\alpha,\gamma)$</tex-math></inline-formula> -quasi-cliques. We also develop an advanced enumeration algorithm based on several novel pruning rules, including early termination and candidate set reduction. To further improve the efficiency, we propose several optimization techniques. Extensive experiments on five real-world datasets demonstrate that our solutions are almost three times faster than the baseline approach.

An Optimisation Method of Construction for Warping Copper Plates and Engines Using Complete Block Designs with Some Special Types of Graphs

The application of bipartite and regular graphs plays a vital role in the area of engineering, mathematical sciences, design of experiments, and medical fields. This study proposes an optimization method for constriction of randomized block design and Latin square design using bipartite and regular graphs with applications of warping copper plates in specimens and comparing them to burners and engines on different days. The construction methods and analysis are performed as follows: the first method is a construction of randomized block design using bipartite and complete bipartite graphs with applications for the amount of warping copper plates and different laboratories are taken to test any significant difference that exists between the mean number of responses for the labs and copper specimens. The second method is the construction of a Latin square design using regular graphs to test whether there is any significant difference between the burners, engines, and some days in statistical analysis of interaction plots, contour plots, and 3D surface plots.

Automatic knowledge graph population with model-complete text comprehension for pre-clinical outcomes in the field of spinal cord injury

The paradigm of evidence-based medicine requires that medical decisions are made on the basis of the best available knowledge published in the literature. Existing evidence is often summarized in the form of systematic reviews and/or meta-reviews and is rarely available in a structured form. Manual compilation and aggregation is costly, and conducting a systematic review represents a high effort. The need to aggregate evidence arises not only in the context of clinical trials, but is also important in the context of pre-clinical animal studies. In this context, evidence extraction is important to support translation of the most promising pre-clinical therapies into clinical trials or to optimize clinical trial design. Aiming at developing methods that facilitate the task of aggregating evidence published in pre-clinical studies, in this paper a new system is presented that automatically extracts structured knowledge from such publications and stores it in a so-called domain knowledge graph. The approach follows the paradigm of model-complete text comprehension by relying on guidance from a domain ontology creating a deep relational data-structure that reflects the main concepts, protocol, and key findings of studies. Focusing on the domain of spinal cord injuries, a single outcome of a pre-clinical study is described by up to 103 outcome parameters. Since the problem of extracting all these variables together is intractable, we propose a hierarchical architecture that incrementally predicts semantic sub-structures according to a given data model in a bottom-up fashion. At the heart of our approach is a statistical inference method that relies on conditional random fields to infer the most likely instance of the domain model given the text of a scientific publication as input. This approach allows modeling dependencies between the different variables describing a study in a semi-joint fashion. We present a comprehensive evaluation of our system to understand the extent to which our system can capture a study in the depth required to enable the generation of new knowledge. We conclude the article with a brief description of some applications of the populated knowledge graph and show the potential implications of our work for supporting evidence-based medicine.

An intelligent analysis method of security and stability control strategy based on the knowledge graph

The security and stability control system is the guarantee of the security and stability operation of the power grid. With the increasing scale of distributed new energy access to the power grid, the security and stability control strategy of the power grid is becoming more complex, and it is becoming increasingly important to correctly analyze and implement the security and stability control strategy. In order to ensure the correctness of the security and stability control strategy implemented by the security and stability control device, it is necessary to analyze the security and stability control strategy in detail. Therefore, this article proposes an intelligent analysis method of the security and stability control strategy based on the knowledge graph. First, this article introduces the ontology design method of the security and stability control strategy based on the knowledge graph, combines the characteristics and applications of the knowledge graph, analyzes the relationship between the elements of the strategy, and designs a clear-structured knowledge network. Second, this article analyzes the automatic construction technology of the graph, constructs the six-element ontology model of the security and stability control strategy, and realizes the human–computer interaction functions such as auxiliary decision making, strategy reasoning, and intelligent search based on the knowledge graph. Using artificial intelligence technology, this article takes the security and stability control strategy of a certain area’s security and stability control system as an example to model and manage. The results show that it can assist the tester to quickly retrieve the strategy, effectively improve the detection efficiency of the security and stability control strategy, avoid the omission and ambiguity caused by the manual understanding of the strategy, and ensure the accuracy and comprehensiveness of the security and stability control strategy detection.

Modeling adoption of genetically modified foods: Application of Rough Set Theory and Flow Network Graph

IntroductionThe main purpose of this study is to extract the rules and patterns governing the behavioral intention of consumers towards the adoption of genetically modified foods (GMFs).MethodThe proposed method is a combination of Rough Set Theory (RST) and Flow Network Graph (FNG). Data was collected from 386 consumers to extract rough rules. 13 rules have been chosen from 289 original rules that were divided into three groups: low, medium, and high intention to use GMFs. They were chosen because of the support values and other indexes that were used in the RST. Eventually, to interpret the performance of the generated rules, FNG were illustrated for each decision-making class, and seven patterns were extracted.ResultsThe findings confirm that corporate social responsibilities, consumer concerns, occupational status, and consumer autonomy are more important than other observed dimensions in consumers' decision-making. Moreover, the findings illustrate that combining Rough Set Theory and Flow Network Graph could predict customers' intentions and provide valuable information for policy-makers in related active industries.DiscussionBased on the analysis outcomes, the most significant factors that affect consumers' intention to use GMFs are: “consumer perception of CSR”; “consumer concerns”; “occupational status”; and “consumer autonomy”. Thus, managers and policymakers must pay more attention to these concepts when they survey consumer intention behavior.

Regularity Index of Uncertain Random Graph

A graph containing some edges with probability measures and other edges with uncertain measures is referred to as an uncertain random graph. Numerous real-world problems in social networks and transportation networks can be boiled down to optimization problems in uncertain random graphs. Actually, information in optimization problems in uncertain random graphs is always asymmetric. Regularization is a common optimization problem in graph theory, and the regularity index is a fundamentally measurable indicator of graphs. Therefore, this paper investigates the regularity index of an uncertain random graph within the framework of chance theory and information asymmetry theory. The concepts of k-regularity index and regularity index of the uncertain random graph are first presented on the basis of the chance theory. Then, in order to compute the k-regularity index and the regularity index of the uncertain random graph, a simple and straightforward calculating approach is presented and discussed. Furthermore, we discuss the relationship between the regularity index and the k-regularity index of the uncertain random graph. Additionally, an adjacency matrix-based algorithm that can compute the k-regularity index of the uncertain random graph is provided. Some specific examples are given to illustrate the proposed method and algorithm. Finally, we conclude by highlighting some potential applications of uncertain random graphs in social networks and transportation networks, as well as the future vision of its combination with symmetry.