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16th International Conference on Computer and Automation Engineering (ICCAE)

作者： Wang, Dongqi Du, Tianqi Liu, Zhongwu Chen, Dongming Ren, Tao Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9798350370058;9798350370164

graph Convolutional Neural Networks (GCN) is a rapidly advancing deep learning algorithm for learning graph representations. One limitation of GCN is that it cannot guarantee optimal low-pass characteristics, thus struggling to effectively filter out high-frequency noises. In this study, we propose a GCN -based autoencoder that addresses this issue by incorporating Laplace-based filters for high-frequency noise reduction. To address potential overfitting caused by the mean-square error loss function used for reconstructing feature matrices, which is sensitive to the number of vector paradigms and dimensions, we introduce a cosine error loss function to mitigate this impact. Additionally, we employ a feature enhancement strategy during training. Through experiments conducted on three real-world datasets, we demonstrate that our proposed autoencoder clustering algorithm outperforms baseline graph representation learning algorithms in node clustering tasks. Furthermore, we assess the parameter sensitivity of our algorithm through extensive experiments.

关键词： Complex Network graph autoencoder graph Clustering graph Convolutional Neural Network

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graph Representation Learning of Banking Transaction Network with EdgeWeight-Enhanced Attention and Textual Information 22

Graph Representation Learning of Banking Transaction Network...

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31st ACM Web Conference (WWW)

作者： Minakawa, Naoto Izumi, Kiyoshi Sakaji, Hiroki Sano, Hitomi Univ Tokyo Tokyo Japan

ISBN: (纸本)9781450391306

In this paper, we propose a novel approach to capture inter-company relationships from banking transaction data using graph neural networks with a special attention mechanism and textual industry or sector information. Transaction data owned by financial institutions can be an alternative source of information to comprehend real-time corporate activities. Such transaction data can be applied to predict stock price and miscellaneous macroeconomic indicators as well as to sophisticate credit and customer relationship management. Although the inter-company relationship is important, traditional methods for extracting information have not captured that enough. With the recent advances in deep learning on graphs, we can expect better extraction of inter-company information from banking transaction data. Especially, we analyze common issues that arise when we represent banking transactions as a network and propose an efficient solution to such problems by introducing a novel edge weight-enhanced attention mechanism, using textual information, and designing an efficient combination of existing graph neural networks.

关键词： graph Attention Network graph Isomorphism Network graph autoencoder Doc2Vec graph Representation Learning Natural Language Processing Financial Transaction Network

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Cross-graph: Robust and Unsupervised Embedding for Attributed graphs with Corrupted Structure 20

Cross-Graph: Robust and Unsupervised Embedding for Attribute...

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20th IEEE International Conference on Data Mining (ICDM)

作者： Wang, Chun Han, Bo Pan, Shirui Jiang, Jing Niu, Gang Long, Guodong Univ Technol Sydney Australian Artificial Intelligence Inst Sydney NSW Australia Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Monash Univ Fac Informat Technol Clayton Vic Australia RIKEN Ctr Adv Intelligence Project Tokyo Japan

ISBN: (纸本)9781728183169

graph embedding has shown its effectiveness to represent graph information and capture deep relationships in graph data. Most recent graph embedding methods focus on attributed graphs, since they preserve both structure and content information in the network. However, corruption can exist in the graph structure as well as the node content of the graph, and both can lead to inferior embedding results. Unfortunately, few existing graph embedding algorithms have considered the corruption problem, and to the best of our knowledge, none has studied structural corruption in attributed graphs, including missing and redundant edges. This field is difficult for previous methods, mainly due to two challenges: (1) the existence of various corruption causes has made it difficult to recognize corruptions in graphs, and (2) the complexity of graph-structured data has increased the difficulty of handling corruption therein for graph embedding methods. These facts lead us here to propose a novel autoencoder-based graph embedding approach, which is robust against structural corruption. Our idea comes from the recent discovery of memorization effects in deep learning. Namely, deep neural networks prefer to fit clean data first, before they over-fit corrupted data. Specifically, we train two autoencoders simultaneously and let them learn the reliability of the edges in the graph from each other. The two autoencoders would evaluate the edges according to their reconstructed structure and manipulate this by devaluing those distrusted edges to update the structure information. The updated structure would be used further in the next iteration as the ground-truth of its peer-network. Experiments on different versions of real-world graphs show state-of-the-art results and demonstrate the robustness of our model against structural corruption.

关键词： graph autoencoder graph convolutional network network representation structural corruption

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DEEP MULTI-graph EMBEDDED CLUSTERING FOR COMMUNITY DETECTION IN FMRI FUNCTIONAL BRAIN NETWORKS ACROSS INDIVIDUALS 31

DEEP MULTI-GRAPH EMBEDDED CLUSTERING FOR COMMUNITY DETECTION...

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2024 International Conference on Image Processing

作者： See, Kai-Jun Ting, Chee-Ming Noman, Fuad Loo, Junn Yong Tan, Yee-Fan Ombao, Hernando Phan, Raphael C. -W. Monash Univ Sch Informat Technol Subang Jaya Malaysia King Abdullah Univ Sci & Technol KAUST Stat Program Thuwal South Africa

ISBN: (纸本)9798350349405;9798350349399

Analyzing the community structure of brain networks provides new insights into human brain function. Existing studies broadly use conventional network clustering approaches. While graph neural networks have recently shown promise in modeling brain functional connectivity (FC) networks, their applications to brain community detection still need improvement and further refinement. Moreover, identifying common community structure while resolving the single-subject partitions across multiple individual networks remains underexplored. We propose a Deep Multi-graph Embedded Clustering (DMGEC) framework to identify shared community partition in brain FC networks over a cohort of individuals. By incorporating the consensus information aggregated across network structures, DMGEC leverages a graph autoencoder to produce consensus-aware latent representations of individual networks, and applies deep embedded clustering on the multi-subject network representation to produce common community assignment of brain nodes. Simulations show superior community recovery by our method compared to conventional approaches, especially for networks with large number of communities. When applied to functional magnetic resonance imaging (fMRI) data, the DMGEC achieves outstanding alikeness over individual partitions, and uncovers group-level differences in brain community motifs between major depressive disorder patients and normal controls.

关键词： Brain connectivity community detection consensus edge graph autoencoder multi-network clustering

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A Novel graph Representation Learning Model for Drug Repositioning Using graph Transition Probability Matrix Over Heterogenous Information Networks 1

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19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Li, Dong-Xu Deng, Xun Zhao, Bo-Wei Su, Xiao-Rui Li, Guo-Dong You, Zhu-Hong Hu, Peng-Wei Hu, Lun Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Xinjiang Lab Minor Speech & Language Informat Pro Urumqi 830011 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710129 Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

Computational drug repositioning is a promising strategy in discovering new indicators for approved or experimental drugs. However, most of computational-based methods fall short of taking into account the non-Euclidean nature of biomedical network data. To address this challenge, we propose a graph representation learning model, called DDAGTP, for drug repositioning using graph transition probability matrix in heterogenous information networks (HINs), In particular, DDAGTP first integrates three different types of drug-disease, drug-protein and protein-disease association networks and their biological knowledge to construct a heterogeneous information network (HIN). Then, a graph convolution autoencoder model is adopted by combining graph transfer probabilities to learn the feature representation of drugs and diseases. Finally, DDAGTP incorporates a CatBoost classifier to complete the task of drug-disease association prediction. Experimental results demonstrate that DDAGTP achieves the excellent performance on all benchmark datasets when compared with state-of-the-art prediction models in terms of several evaluation metrics.

关键词： Drug repositioning graph autoencoder graph transfer probabilities Drug disease associations Heterogeneous information network

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How Significant Attributes are in the Community Detection of Attributed Multiplex Networks 23

How Significant Attributes are in the Community Detection of...

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46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Cheng, Junwei He, Chaobo Han, Kunlin Ma, Wenjie Tang, Yong South China Normal Univ Sch Comp Sci Guangzhou Peoples R China South China Normal Univ Sch Comp Sci Pazhou Lab Guangzhou Peoples R China Univ Southern Calif Dept Comp Sci Los Angeles CA 90007 USA China Mobile Grp Zhejiang Co Ltd Ningbo Peoples R China

ISBN: (纸本)9781450394086

Existing community detection methods for attributed multiplex networks focus on exploiting the complementary information from different topologies, while they are paying little attention to the role of attributes. However, we observe that real attributed multiplex networks exhibit two unique features, namely, consistency and homogeneity of node attributes. Therefore, in this paper, we propose a novel method, called ACDM, which is based on these two characteristics of attributes, to detect communities on attributed multiplex networks. Specifically, we extract commonality representation of nodes through the consistency of attributes. The collaboration between the homogeneity of attributes and topology information reveals the particularity representation of nodes. The comprehensive experimental results on real attributed multiplex networks well validate that our method outperforms state-of-the-art methods in most networks.

关键词： Attributed multiplex networks Community detection graph neural networks graph autoencoder Unsupervised learning

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Mask-GVAE: Blind Denoising graphs via Partition 21

Mask-GVAE: Blind Denoising Graphs via Partition

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30th World Wide Web Conference (WWW)

作者： Li, Jia Liu, Mengzhou Zhang, Honglei Wang, Pengyun Wen, Yong Pan, Lujia Cheng, Hong Chinese Univ Hong Kong Hong Kong Peoples R China Tianjin Univ Tianjin Peoples R China Huawei Noahs Ark Lab Beijing Peoples R China

ISBN: (纸本)9781450383127

We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs. We focus on recovering graph structures via deleting irrelevant edges and adding missing edges, which has many applications in real-world scenarios, for example, enhancing the quality of connections in a co-authorship network. Mask-GVAE makes use of the robustness in low eigenvectors of graph Laplacian against random noise and decomposes the input graph into several stable clusters. It then harnesses the huge computations by decoding probabilistic smoothed subgraphs in a variational manner. On a wide variety of benchmarks, Mask-GVAE outperforms competing approaches by a significant margin on PSNR and WL similarity.

关键词： graph denoising graph clustering graph autoencoder

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GRL-LS: A learning style detection in online education using graph representation learning

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 201卷 1页

作者： Muhammad, Bello Ahmad Qi, Chao Wu, Zhenqiang Ahmad, Hafsa Kabir Minist Educ Key Lab Modern Teaching Technol Xian 710062 Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710062 Peoples R China Bayero Univ Kano Kano 700241 Nigeria

The accessibility and popularity of online learning have aided the spread of modern learning systems, which provide numerous opportunities for studying the behavior of learners and improving their learning quality. In online platforms, different learners have different learning styles based on their learning behavior. As a result, analyzing learners' behavior and detecting their learning styles is important in order to provide appropriate resource recommendations, thereby improving their learning outcomes. While several approaches for detecting learning styles have been suggested, behavioral data is collected on a large scale, making it difficult to capture learners' behavior efficiently. Furthermore, current approaches neglect relationships between learner-resource interaction and use techniques based on individual learners' features. However, considering these relations can provide additional benefits. Motivated by these limitations, we propose GRL-LS, a learning style detection approach based on graph representation learning techniques. We first construct a bipartite graph representing the interaction between the set of learners and the set of learning resources. We then introduce a graph embedding technique that learns the latent representations of learners and resources. The learned representation is mapped with the Felder-Silverman learning style model (FSLSM) to identify and group learners using K-means algorithms. The GRL-LS technique can be used in a wide range of educational settings and can be customized to fit a variety of learning style models. An experiment on real-world KDDCup datasets was conducted to evaluate the effectiveness of the proposed GRL-LS approach. The results show that the proposed GRL-LS is efficient at detecting learning style, performs best for all FSLSM dimensions with average accuracy and precision of 88.25% and 78.50% respectively, and outperforms existing approaches with an average improvement of 8.55% accuracy and 2.33% precision

关键词： graph learning graph autoencoder Learning style Interactive learning environment Learners behavior

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High-order autoencoder with data augmentation for collaborative filtering

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KNOWLEDGE-BASED SYSTEMS 2022年 240卷 107773-107773页

作者： Nguyen, Mo Yu, Jian Nguyen, Tung Yongchareon, Sira Auckland Univ Technol 2 Wakefield St Auckland Cbd New Zealand

Early DNN-based collaborative filtering (CF) approaches have demonstrated their superior performance than traditional CF such as Matrix Factorization. However, such approaches treat each user-item interaction as separate data and thus overlook the intrinsic relationships among data instances. Inspired by the discovery that the autoencoder architecture can force the hidden representation to capture information about the structure of the graph data, in this work, we propose a novel framework called High-order autoencoder based Collaborative Filtering (HACF) that enhances the classic NeuMF framework with autoencoders for capturing latent high-order connectivity signals in the user-item interaction graph. Specifically, each user-item pair is augmented with higher-order neighbours and input to two sets of autoencoders, one set for the users and the other for the items. All the autoencoders in one set share parameters so increasing the number of autoencoders does not increase the model *** have conducted extensive experiments on four popular public benchmark datasets with different sparsity. The overall comparison results demonstrate the advantages of autoencoder-based methods and show that our framework outperforms some state-of-the-art DNN-based collaborative filtering approaches.(c) 2022 Elsevier B.V. All rights reserved.

关键词： graph autoencoder High-order connectivity Data augmentation Hidden representation

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Adversarial random graph neural network for anomaly detection

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DIGITAL SIGNAL PROCESSING 2024年 146卷

作者： Tuzen, Ahmet Yaslan, Yusuf Aselsan Inc Ankara Turkiye Istanbul Tech Univ Istanbul Turkiye

Anomaly detection is distinguishing unusual objects from normal patterns. It is a complex task due to unpredictable nature of anomalies, which can appear in many forms or they can be hidden by mimicking normal behaviors in a graph structure. Such diversity makes this Deep learning approaches can solve these problems by extracting complex patterns from networks. However, addressing different forms of anomalous instances is essential for successfully implementing these approaches, as different anomaly types require further analysis. Additionally, it is challenging to interpret anomalies beforehand without focusing on every aspect of anomalies. Our objective is to propose an architecture capable of handling all types of anomalous entities by tackling challenges across various domains. In this paper, we introduce ARNAD, a novel framework that integrates three deep models to identify anomalies in graphs: graph neural network, autoencoder, and adversarial autoencoder. ARNAD approaches graph anomaly detection by utilizing the features of the deep parts, and four key elements stand out: (1) the autoencoder learns the overall graph structure and identifies highly deviated ones, (2) the graph neural network exploits graph structure to detect anomalies among the communities, (3) a fixed -size randomized neighborhood that prevents overfitting while reducing complexity (4) the adversarial autoencoder improves the robustness of the framework and discriminates anomalies. To detect anomalies, four receptive components assign risk scores to objects in the attributed network. We evaluated the framework with three synthetic datasets that simulate different behaviors of anomalies and six widely used real attributed networks. Our experimental results show that ARNAD performs competitively with other state-of-the-art models in detecting anomalous entities while minimizing false positives, demonstrating ARNAD's effectiveness in detecting graph anomalies.

关键词： Anomalous node detection graph neural network Random neighborhood graph autoencoder Adversarial autoencoder

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