Article
Computer Science, Information Systems
Judith Hermanns, Konstantinos Skitsas, Anton Tsitsulin, Marina Munkhoeva, Alexander Kyster, Simon Nielsen, Alexander M. Bronstein, Davide Mottin, Panagiotis Karras
Summary: This article introduces a method called GRASP for matching nodes of two graphs. The method establishes a correspondence between functions derived from Laplacian matrix eigenvectors to align nodes. Experimental results show that GRASP outperforms scalable state-of-the-art methods for graph alignment across noise levels and performs competitively with the best nonscalable methods.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2023)
Article
Computer Science, Artificial Intelligence
Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz, Tomasz Kajdanowicz, Nitesh V. Chawla
Summary: This paper discusses the challenges of representation learning on dynamic graphs and proposes a framework called FILDNE for incorporating advances in static representation learning into dynamic graphs. FILDNE reduces computational costs while improving quality measure gains by applying static representation learning methods to dynamic graphs.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematics
Lei Zhang, Feng Qian, Jie Chen, Shu Zhao
Summary: Network alignment aims to identify the correspondence of nodes between two or more networks, and it is the cornerstone of many network mining tasks. We propose the URNA framework based on graph neural network, which achieves an effective balance between accuracy and efficiency through model training and network alignment phases. Experimental results show that the proposed method can significantly reduce running time and memory requirements while guaranteeing alignment performance.
Article
Computer Science, Artificial Intelligence
Ziyang Zhang, Chuan Chen, Yaomin Chang, Weibo Hu, Zibin Zheng, Yuren Zhou, Lei Sun
Summary: This study introduces a disentangled framework called HIN2Grid, which transforms graph data into semantic-specific grid-like data for efficient processing by CNNs, explicitly improving the performance of heterogeneous network learning. Dual attention mechanisms are proposed to enhance interpretability and robustness.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Theory & Methods
Jongmin Park, Soohwan Jeong, Byung Suk Lee, Sungsu Lim
Summary: This paper proposes a new model called MIGTNet for heterogeneous graph embedding, which uses both metapath instances and relations between them. MIGTNet constructs a metapath instance-based graph, where a node represents a metapath instance and a link represents a relation between metapath instances, and inputs it to a hierarchical graph attention network to obtain meaningful node embeddings. Extensive experiments show that MIGTNet outperforms state-of-the-art heterogeneous graph embedding models in node classification and node clustering.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2023)
Review
Biochemical Research Methods
Hai-Cheng Yi, Zhu-Hong You, De-Shuang Huang, Chee Keong Kwoh
Summary: This article summarizes the advances of graph representation learning and its applications in bioinformatics. It provides open resource platforms and libraries for implementing these methods and discusses the challenges and opportunities in this field. Graph representation learning bridges the gap between biomedical graphs and modern machine learning methods by embedding graphs into a low-dimensional space while preserving their topology and node properties. This survey brings valuable insights for researchers to contribute their knowledge to graph representation learning and future-oriented bioinformatics studies.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Computer Science, Information Systems
Hongchan Li, Zhuang Zhu, Haodong Zhu, Baohua Jin
Summary: The purpose of entity alignment for knowledge graphs is to find pairs of entities that represent the same real-world object from different knowledge graphs. In recent years, techniques for knowledge fusion using entity alignment have gained attention. This article proposes a method for entity alignment using truncated negative sampling with attribute character embedding. The method utilizes relationship and attribute data in heterogeneous knowledge graphs to perform entity alignment.
Article
Computer Science, Artificial Intelligence
Ying Shen, Huizhi Li, Dagang Li, Jingwei Zheng, Wenmin Wang
Summary: The study focuses on graph representation learning, proposing a new learning scheme (ANGraph) that better preserves the characteristics of graph structures and achieves significant performance improvement in node classification tasks.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Hao Wei, Gang Xiong, Qiang Wei, Weiquan Cao, Xin Li
Summary: Network embedding in heterogeneous network has attracted much attention due to its effectiveness in capturing network structure and properties. Existing models mainly focus on node proximity, but they fail to consider the different types of nodes and edges in heterogeneous network. We propose a novel structure-aware Attributed Heterogeneous Network Embedding model (SAHNE), which takes into consideration the community and organization structure in heterogeneous network. We conduct extensive experiments on three real-world networks and demonstrate that SAHNE outperforms state-of-the-art methods in various data mining tasks.
KNOWLEDGE AND INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Qi Luo, Dongxiao Yu, Akshita Maradapu Vera Venkata Sai, Zhipeng Cai, Xiuzhen Cheng
Summary: Social networks have a wide range of applications, and the analysis of these applications has attracted a lot of attention from the research community. However, the high dimensionality of social network data presents a challenge in its analysis, known as the curse of dimensionality. Representation learning offers a solution by learning low-dimensional vector representations of high-dimensional network data while preserving network structural information. These representations can be utilized in various network-based applications.
Article
Computer Science, Artificial Intelligence
Min Shi, Yufei Tang, Xingquan Zhu
Summary: In this study, a co-alignment graph convolutional learning (CoGL) paradigm is proposed to align topology and content networks for maximized consistency, which demonstrates comparable or better performance than existing state-of-the-art GNN models in experiments on six benchmarks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dongdong Chen, Yuxing Dai, Lichi Zhang, Zhihong Zhang, Edwin R. Hancock
Summary: This paper presents a novel neural framework that converts the graph matching problem into a linear assignment problem in a high-dimensional space. By leveraging relative position information at the node level and high-order structural arrangement information at the subgraph level, the method improves the performance of graph matching tasks and establishes reliable node-to-node correspondences.
PATTERN RECOGNITION
(2023)
Article
Mathematics
Yuxuan Yang, Beibei Han, Zanxi Ran, Min Gao, Yingmei Wei
Summary: Graph-embedding learning aims to represent nodes in a graph network as low-dimensional dense vectors for practical analysis tasks. Graph neural networks based on deep learning have gained attention in this field, but they often have limitations in utilizing higher-order neighborhood information effectively and considering structural properties. To address these issues, we propose centrality encoding, attention mechanism, and random walk regularization to improve the node representation. Experimental results on benchmark datasets demonstrate that our model outperforms baseline methods in node-clustering and link prediction tasks, showing highly expressive graph embedding.
Article
Mathematics
Bin Wang, Yu Chen, Jinfang Sheng, Zhengkun He
Summary: Graph embedding is significant for graph analysis and research, and the emergence of graph neural networks has greatly improved the accuracy of graph embedding. However, existing methods neglect the influence of clusters, thus this paper proposes a new approach to incorporate cluster influence into graph embedding.
Article
Automation & Control Systems
Shih-Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque
Summary: Pykg2vec is a Python library for learning representations of entities and relations in knowledge graphs, implementing 25 state-of-the-art knowledge graph embedding algorithms and designed to accelerate research in knowledge graph representation learning. Released under the MIT License, Pykg2vec is built on PyTorch and Python's multiprocessing framework.
JOURNAL OF MACHINE LEARNING RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Thanh Tam Nguyen, Thanh Dat Hoang, Minh Tam Pham, Tuyet Trinh Vu, Thanh Hung Nguyen, Quyet-Thang Huynh, Jun Jo
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Duong Chi Thang, Hoang Thanh Dat, Nguyen Thanh Tam, Jun Jo, Nguyen Quoc Viet Hung, Karl Aberer
Summary: In this paper, a hypothesis is proposed regarding the connection between features and structure in graph neural networks, suggesting that graphs can be constructed by connecting node features according to a latent function. This hypothesis has several important implications, including defining graph families, applying GNNs on featureless graphs, and predicting the existence of a latent function that can create graphs outperforming original ones for specific tasks. A graph generative model is proposed to learn such function, and experiments confirm the hypothesis and implications.
PATTERN RECOGNITION LETTERS
(2022)
Article
Computer Science, Hardware & Architecture
Thanh Tam Nguyen, Thanh Trung Huynh, Hongzhi Yin, Matthias Weidlich, Thanh Thi Nguyen, Thai Son Mai, Quoc Viet Hung Nguyen
Summary: This paper discusses the challenges of rumour detection and proposes a rumour detection method that aims to detect the majority of rumours as quickly as possible. The method combines graph-based matching techniques with effective load shedding. Experimental results demonstrate the robustness of the approach in terms of runtime performance and detection accuracy under diverse streaming conditions.
Article
Computer Science, Information Systems
Toan Nguyen Thanh, Nguyen Duc Khang Quach, Thanh Tam Nguyen, Thanh Trung Huynh, Viet Hung Vu, Phi Le Nguyen, Jun Jo, Quoc Viet Hung Nguyen
Summary: With recent advancements in graph neural networks, GNN-based recommender systems (gRS) have achieved remarkable success. However, existing research reveals that gRSs are still vulnerable to poison attacks, which manipulate recommendation results by injecting fake data. This problem arises because existing poison attacks are not tailored for gRSs, which are widely adopted in the industry. To address this issue, we propose GSPAttack, a generative surrogate-based poison attack framework specifically designed for gRSs.
ACM TRANSACTIONS ON INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Thanh Cong Phan, Nguyen Duc Khang Quach, Thanh Tam Nguyen, Thanh Toan Nguyen, Jun Jo, Quoc Viet Hung Nguyen
Summary: Wildfire detection is crucial for preventing environmental disasters. Existing methods lack timeliness and explainability. We propose a real-time wildfire detection system using streaming capability and semantic annotation, which outperforms baselines in terms of efficiency, accuracy, explainability, and adaptivity.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Chi Thang Duong, Thanh Tam Nguyen, Hongzhi Yin, Matthias Weidlich, Thai Son Mai, Karl Aberer, Quoc Viet Hung Nguyen
Summary: The heterogeneity of today's Web sources requires information retrieval systems to handle multi-modal queries. Existing methods for handling multi-modal queries are either inefficient or ineffective. To address this issue, we propose an information retrieval system based on heterogeneous network embedding, which can accurately answer multi-modal queries with a single pass over the data.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Nguyen Thanh Tam, Huynh Thanh Trung, Hongzhi Yin, Tong Van Vinh, Darnbi Sakong, Bolong Zheng, Nguyen Quoc Viet Hung
Summary: This paper proposes an unsupervised entity alignment framework for cross-lingual KGs, which fuses different types of information to exploit the richness of KG data. The model captures relation-based and attribute-based correlations using a graph convolutional neural network, and utilizes late-fusion mechanism for enhancing alignment results.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Thanh Tam Nguyen, Thanh Cong Phan, Minh Hieu Nguyen, Matthias Weidlich, Hongzhi Yin, Jun Jo, Quoc Viet Hung Nguyen
Summary: The propagation of rumours on social media poses a significant threat to societies, and there have been recent efforts to develop techniques for rumour detection. This study proposes a query-by-example approach to provide explanations for detected rumours by offering examples of related rumours. The findings demonstrate that this approach outperforms baseline techniques in delivering meaningful explanations for various rumour propagation behaviours.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Chi Thang Duong, Thanh Tam Nguyen, Trung-Dung Hoang, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen
Summary: We present Deep MinCut (DMC), an unsupervised approach for learning node embeddings in graph-structured data. DMC derives node representations based on their membership in communities, eliminating the need for a separate clustering step. By minimizing the mincut loss, which captures connections between communities, DMC learns both node embeddings and communities simultaneously. Our empirical evidence demonstrates that the communities learned by DMC are meaningful and that the node embeddings perform well in various node classification benchmarks.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Thanh Tam Nguyen, Thanh Trung Huynh, Minh Tam Pham, Thanh Dat Hoang, Thanh Thi Nguyen, Quoc Viet Hung Nguyen
Summary: Data redundancy is a significant problem in data-intensive applications. This study introduces a new concept called functional redundancy, which overcomes the limitations of existing works on continuous data. An efficient algorithm based on generative adversarial networks is designed to validate any functional redundancy, regardless of the number of attributes and tuples. Experimental results demonstrate the superiority and applicability of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Darnbi Sakong, Thanh Trung Huynh, Thanh Tam Nguyen, Thanh Toan Nguyen, Jun Jo, Quoc Viet Hung Nguyen
Summary: This paper proposes a novel KG alignment framework, ComplexGCN, which learns the embeddings of both entities and relations in complex spaces while capturing both semantic and neighborhood information simultaneously. The proposed model ensures richer expressiveness and more accurate embeddings by successfully capturing various relation structures in complex spaces with high-level computation. The model further incorporates relation label and direction information with a low degree of freedom. Empirical results show the efficiency and effectiveness of the proposed method.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Xuan Truong Du Chau, Thanh Tam Nguyen, Jun Jo, Quoc Viet Hung Nguyen
Summary: This paper studies over 1000 rumors, including over 4 million tweets from about 3 million users. Through this research, we aim to gain a better understanding of the distribution, correlation, and propagation characteristics of rumors, as well as user behaviors and spatial-temporal characteristics.
ADVANCED DATA MINING AND APPLICATIONS, ADMA 2021, PT I
(2022)
Proceedings Paper
Computer Science, Information Systems
Nguyen Thanh Tam, Huynh Thanh Trung, Hongzhi Yin, Tong Van Vinh, Darnbi Sakong, Bolong Zheng, Nguyen Quoc Viet Hung
Summary: This paper proposes an end-to-end, unsupervised entity alignment framework for cross-lingual knowledge graphs, which outperforms existing techniques in terms of accuracy, efficiency, and label saving based on evaluations using real datasets.
2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021)
(2021)
Proceedings Paper
Computer Science, Information Systems
Thanh Tam Nguyen, Matthias Weidlich, Hongzhi Yin, Bolong Zheng, Quang Huy Nguyen, Quoc Viet Hung Nguyen
PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20)
(2020)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)