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International Joint Conference on Neural networks (IJCNN)

作者： Zhu, Zihong Zhang, Weiyu Guo, Xinchao Qiao, Xinxiao Qilu Univ Technol Shandong Acad Sci Sch Comp Sci & Technol Jinan Peoples R China

ISBN: (纸本)9781665488679

Recently, recommendation methods based on graph convolutional networks (GCNs) have received much attention and have shown competitive performance. These methods usually perform graph convolutions of user and item embeddings in Euclidean space. However, user-item interaction graphs usually present a tree-like hierarchy in large-scale recommender systems. To better fit user and item data to the embedding space, hyperbolic space becomes a good choice, which grows exponentially in volume with radius and is well suited for embedding hierarchical data. In addition, most previous works aggregate neighborhoods through projection operations on tangent spaces, which inevitably cause distortion. In this paper, we propose a Lorentzian graph convolutional network model for collaborative filtering (LCF), which makes the learned user and item representations strictly follow hyperbolic geometry, and the learned embeddings are optimized based on margin ranking loss and geometric views. Specifically, we first pass the initialized user and item information through multiple Lorentzian graph convolutional layers, and ensure that the learned node embeddings are not out of the hyperbolic space, while capturing rich high-order neighborhood information. Then, the model is effectively learned under the composite loss function to take full advantage of the hyperbolic space. We conduct extensive experiments on three datasets and compare with many baselines. Experimental results show that our method performs better than many state-of-the-art baseline methods.

关键词： Hyperbolic Space Recommendation Systems Collaborative Filtering graph convolution networks

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Rethinking the Feature Iteration Process of graph convolution networks

Rethinking the Feature Iteration Process of Graph Convolutio...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Tang, Bisheng Chen, Xiaojun Wang, Dakui Zhao, Zhendong Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Node classification is a fundamental research problem in graph neural networks(GNNs), which uses node's feature and label to capture node embedding in a low dimension. The existing graph node classification approaches mainly focus on GNNs from global and local perspectives. The relevant research is relatively insufficient for the micro perspective, which refers to the feature itself. In this paper, we prove that deeper GCNs' features will be updated with the same coefficient in the same dimension, limiting deeper GCNs' expression. To overcome the limits of the deeper GCN model, we propose a zero feature (k-ZF) method to train GCNs. Specifically, k-ZF randomly sets the initial k feature value to zero, acting as a data rectifier and augmenter, and is also a skill equipped with GCNs models and other GCNs skills. Extensive experiments based on three public datasets show that k-ZF significantly improves GCNs in the feature aspect and achieves competitive accuracy.

关键词： graph convolution networks Zero Feature Micro Perspective

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JAMMING STRATEGY GENERATION FOR HIDDEN COMMUNICATION MODES VIA graph convolution networks

JAMMING STRATEGY GENERATION FOR HIDDEN COMMUNICATION MODES V...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Kong, Fanxiang Li, Qiang Shao, Huaizong Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781728176055

Optimal jamming has important applications in both military and civil communications. There have been a brunch of works investigating the optimal jamming signal design when the signal modes of the opponent are known. In this work, we focus on the less studied hidden mode jamming problem. That is, the jammer has partially recorded the signal modes of the opponent, but there are some hidden modes not revealed to the jammer as of the appearance of these modes. As such, when the hidden modes appear, the jammer has to quickly adapt its jamming strategy to achieve effective jamming. However, it is challenging to do so due to incomplete knowledge of the intrinsic relation between the known and the hidden modes. In this work, a learning-based approach is proposed to attack this problem. Specifically, we custom-devise a jamming network (J-Net) to automatically learn the intrinsic relation among different modes and transfer the jamming strategy from the known modes to the hidden ones. Experimental results demonstrate that the J-Net attains much better jamming effect than pulsed Gaussian jamming and random jamming, and is comparable to the reinforcement learning-based approach, which assumes all the (known and hidden) modes available at the jammer.

关键词： Jamming graph convolution networks bit error rate

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SearchGCN: Powering Embedding Retrieval by graph convolution networks for E-Commerce Search 21

SearchGCN: Powering Embedding Retrieval by Graph Convolution...

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44th International ACM SIGIR Conference on Research and Development in Information Retrieval

作者： Xia, Xinlin Wang, Shang Zhang, Han Wang, Songlin Xu, Sulong Xiao, Yun Long, Bo Yang, Wen-Yun JD Com Beijing Peoples R China JD Com Silicon Valley Res Ctr Mountain View CA 94043 USA

ISBN: (纸本)9781450380379

graph convolution networks (GCN), which recently becomes new state-of-the-art method for graph node classification, recommendation and other applications, has not been successfully applied to industrial-scale search engine yet. In this proposal, we introduce our approach, namely SearchGCN, for embedding-based candidate retrieval in one of the largest e-commerce search engine in the world. Empirical studies demonstrate that SearchGCN learns better embedding representations than existing methods, especially for long tail queries and items. Thus, SearchGCN has been deployed into ***'s search production since July 2020.

关键词： Search Neural networks graph convolution networks Representation learning

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Protein Interaction Prediction on PHI networks Using graph convolution networks 29

Protein Interaction Prediction on PHI Networks Using Graph C...

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29th IEEE Conference on Signal Processing and Communications Applications (SIU)

作者： Koca, M. Burak Karadeniz, Ilknur Nourani, Esmaeil Sevilgen, F. Erdogan Gebze Tekn Univ Bilgisayar Muhendisligi Bolumu Kocaeli Turkey Isik Univ Bilgisayar Muhendisligi Bolumu Istanbul Turkey Azathaijan Shahid Madani Univ Bilgisayar Muhendisligi Bolumu Tebriz Iran

ISBN: (纸本)9781665436496

Proteins are biological molecules that play a critical role in vital biological processes. Interactions between pathogen proteins and host proteins form pathogen-host interaction (Pitt) networks. These bipartite interaction networks have great importance in determining which vital activities are affected by the pathogen and the diseases it may cause. Experimental detection of the protein interactions in wet labs is both time-consuming and costly. The limited number of experimentally detectable interactions and overlook of some potential interactions lead to development of computational methods. In this study, a graph convolution network (GCN) based method is presented that enables to predict protein-protein interactions in Pill networks. The unsupervised trained GCN model (graphSAGE) uses amino acid sequences as node features as well as the topological information. This is the first study to the best of our knowledge which provides GCN models to do protein-protein interaction prediction in Pill networks. The experimental results show that the developed model performs 10% better than the state-of-art algorithms on the benchmark Pill dataset and it predicts interactions with 96% accuracy.

关键词： bioinformatics protein-protein interaction prediction PHI networks graph convolution networks

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WGNet: Wider graph convolution networks for 3D point cloud classification with local dilated connecting and context-aware

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2022年 110卷

作者： Chen, Yiping Luo, Zhipeng Li, Wen Lin, Haojia Nurunnabi, Abdul Lin, Yaojin Wang, Cheng Zhang, Xiao-Ping Li, Jonathan Sun Yat sen Univ Sch Geospatial Engn & Sci Zhuhai 519082 Peoples R China Xiamen Univ Sch Informat Fujian Key Lab Sensing & Comp Smart Cities Xiamen Peoples R China Univ Luxembourg Inst Civil & Environm Engn Dept Geodesy & Geospatial Engn Esch Sur Alzette Luxembourg Minnan Normal Univ Sch Comp Sci & Engn Zhangzhou Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON Canada Univ Waterloo Dept Geog & Environm Management Waterloo ON Canada Univ Waterloo Dept Syst Design Engn Waterloo ON Canada

graph convolution networks (GCNs) have been proven powerful in describing unstructured data. Currently, most of existing GCNs aim on more accuracy by constructing deeper models. However, these methods show limited benefits, and they often suffer from the common drawbacks brought by deep networks, such as large model size, high memory consumption and slow training speed. In this paper, different from these methods, we widen GCNs to improve the descriptiveness by expanding the width of input to avoid the above drawbacks. Specifically, we present a wider GCNs based model, WGNet, for 3D point cloud classification. A local dilated connecting (LDC) module is designed to obtain the adjacency matrix, while a context information aware (CIA) module is proposed to generate initial node representation. These two modules provide a way to transform 3D point cloud into graph structure with larger receptive field and rich initial node features. These two properties widen the channels of input and provide more rich information to describe the samples precisely. Besides, we provide analysis to formulate the above idea as the sample precision description. Then, we adopt ChebyNet as our basic network, and present a skip-connection-based GCNs to improve efficiency of feature reuse. WGNet was evaluated on two datasets. One was acquired by a mobile laser scanning system under the real road environments, while the other was the well-known public artificial dataset, ModelNet40. Experimental results show that WGNet achieves better performance than the state-of-the-art in terms of descriptiveness, efficiency and robustness. Ablation studies also demonstrate the effectiveness of our designed LDC and CIA modules.

关键词： 3D point cloud graph convolution networks 3D object classification Dilated connecting Context information aware

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GCN-ICF: graph convolution networks for Item-based Recommendation

GCN-ICF: Graph Convolution Networks for Item-based Recommend...

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IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Deng, Kai Huang, Jiajin Qin, Jin Guizhou Univ Guiyang Peoples R China Beijing Univ Technol Beijing Peoples R China

ISBN: (纸本)9781665419246

Item-based Collaborative Filtering (ICF) is wildly used recommendation method. Under the assumption that a user tends to choose an item similar to what she has interacted before, ICF uses items that a user has interacted to learn a user's profile, and then makes recommendations by calculating the similarity between a target item and the user's profile. Therefore, the method of learning item representations from data is crucial in ICF. However, most existing ICF methods only consider the low-order connectivity between a target item and a target user's interacted items. The consideration limits the improvement of recommendation qualities. In this paper, by using the message-passing idea of graph convolution networks (GCN), we propose GCN-ICF to capture the high-order connectivity between users and items in the user-item interaction bipartite graph structure. The method enriches the representation of each item through its neighbors in the bipartite graph structure. The experimental results on three public datasets verify the highly positive effect of the performance of the GCN-ICF method.

关键词： Collaborative Filtering high-order connectivity graph convolution networks

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Neighbor Interaction Aware graph convolution networks for Recommendation 20

Neighbor Interaction Aware Graph Convolution Networks for Re...

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43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Sun, Jianing Zhang, Yingxue Guo, Wei Guo, Huifeng Tang, Ruiming He, Xiuqiang Ma, Chen Coates, Mark Montreal Res Ctr Huawei Noahs Ark Lab Montreal PQ Canada Huawei Noahs Ark Lab Shenzhen Peoples R China McGill Univ Sch Comp Sci Montreal PQ Canada McGill Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9781450380164

Personalized recommendation plays an important role in many online services. Substantial research has been dedicated to learning embeddings of users and items to predict a user's preference for an item based on the similarity of the representations. In many settings, there is abundant relationship information, including user-item interaction history, user-user and item-item similarities. In an attempt to exploit these relationships to learn better embeddings, researchers have turned to the emerging field of graph convolutional Neural networks (GCNs), and applied GCNs for recommendation. Although these prior works have demonstrated promising performance, directly apply GCNs to process the user-item bipartite graph is suboptimal because the GCNs do not consider the intrinsic differences between user nodes and item nodes. Additionally, existing large-scale graph neural networks use aggregation functions such as sum/mean/max pooling operations to generate a node embedding that considers the nodes' neighborhood (i.e., the adjacent nodes in the graph), and these simple aggregation strategies fail to preserve the relational information in the neighborhood. To resolve the above limitations, in this paper, we propose a novel framework NIA-GCN, which can explicitly model the relational information between neighbor nodes and exploit the heterogeneous nature of the user-item bipartite graph. We conduct empirical studies on four public benchmarks, demonstrating a significant improvement over state-of-the-art approaches. Furthermore, we generalize our framework to a commercial App store recommendation scenario. We observe significant improvement on a large-scale commercial dataset, demonstrating the practical potential for our proposed solution as a key component of a large scale commercial recommender system. Furthermore, online experiments are conducted to demonstrate that NIA-GCN outperforms the baseline by 10.19% and 9.95% in average in terms of CTR and CVR during ten-day AB

关键词： graph convolution networks Recommendation Collaborative Filtering

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ACP-GCN: The Identification of Anticancer Peptides Based on graph convolution networks

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IEEE ACCESS 2020年 8卷 176005-176011页

作者： Rao, Bing Zhang, Lichao Zhang, Guoying China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Shenzhen Inst Informat Technol Sch Intelligent Mfg & Equipment Shenzhen 518172 Peoples R China

Anticancer peptide (ACP) is a class of anti-cancer peptide which can inhibit and kill tumor cells. Identification of ACPs is of great significance for the development of new anti-cancer drugs. However, most of computational methods make predictions based on machine learning using hand-crafted features. In this article, we propose a new graph learning based computational model, named ACP-GCN, to automatically and accurately predict ACPs based on graph convolution networks. In this model, we for the first time take the ACP prediction as a graph classification task, where each peptide sample is represented as a graph. The experimental results show that the proposed model outperforms most of state-of-the-art methods, demonstrating that the proposed method can effectively distinguish ACPs from non-ACPs. The excellent predictive ability will rapidly push forward their applications in cancer therapy.

关键词： Peptides Amino acids Feature extraction convolution Training Predictive models Machine learning Anti-cancer peptides graph convolution networks machine learning prediction methods

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Quasi-framelets: robust graph neural networks via adaptive framelet convolution

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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2025年第2期16卷 755-770页

作者： Yang, Mengxi Shi, Dai Zheng, Xuebin Yin, Jie Gao, Junbin Univ Sydney Camperdown Business Sch Discipline Business Analyt Sydney NSW 2006 Australia

This paper aims to provide a novel design of a multiscale framelet convolution for spectral graph neural networks (GNNs). While current spectral methods excel in various graph learning tasks, they often lack the flexibility to adapt to noisy, incomplete, or perturbed graph signals, making them fragile in such conditions. Our newly proposed framelet convolution addresses these limitations by decomposing graph data into low-pass and high-pass spectra through a finely-tuned multiscale approach. Our approach directly designs filtering functions within the spectral domain, allowing for precise control over the spectral components. The proposed design excels in filtering out unwanted spectral information and significantly reduces the adverse effects of noisy graph signals. Our approach not only enhances the robustness of GNNs but also preserves crucial graph features and structures. Through extensive experiments on diverse, real-world graph datasets, we demonstrate that our framelet convolution achieves superior performance in node classification tasks. It exhibits remarkable resilience to noisy data and adversarial attacks, highlighting its potential as a robust solution for real-world graph applications. This advancement opens new avenues for more adaptive and reliable spectral GNN architectures.

关键词： graph neural networks Framelets Quasi-framelets graph convolution networks

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