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Unravelling the structure of the CSD cocrystal network using a fast near-optimal bipartisation algorithm for large networks

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CRYSTENGCOMM 2024年第2期26卷 192-202页

作者： de Vries, Tom E. Vlieg, Elias de Gelder, Rene Radboud Univ Nijmegen Inst Mol & Mat Solid State Chem Heyendaalseweg 135 NL-6525 AJ Nijmegen Netherlands

Networks, consisting of vertices connected by edges, are an important mathematical concept used to describe relationships between people, roads between cities, reactions between chemicals, and many other interactions. Such a network can be created by extracting cocrystals from the Cambridge Structural Database (CSD). This network describes which compounds can form cocrystals together and can, for example, be used to predict new cocrystals using link-prediction techniques. Bipartiteness is an important property of some networks wherein the vertices can be separated into two groups such that edges only point from one group to the other. Knowing whether a network is bipartite can make studying its structure considerably easier. If a network is nearly bipartite except for a number of outlying edges, one might want to identify and remove those edges, thereby bipartising the network. The CSD cocrystal network was previously found to be close to bipartiteness. Truly bipartising it could improve the accuracy of link-prediction and give insight into the hidden structure of the network. Many algorithms exist for exactly finding the optimal bipartisation for a nearly-bipartite network, but the time it takes to complete such algorithms increases exponentially with the size of the problem. In some cases, an exact solution is unnecessary and a 'good enough' bipartisation is sufficient. We have developed an algorithm that can find a near-optimal bipartisation within reasonable time, even for very large networks, and used it to unravel the structure of the CSD cocrystal network. We obtained a bipartisation that leaves 96% of the network intact, and we were able to identify 'universal' coformers that do not conform to the bipartite nature of the network. By applying a clustering algorithm to the bipartised network, we were also able to identify anticommunities of coformers. Analysing the CSD cocrystal network using a fast near-optimal bipartisation algorithm reveals its hidden struc

关键词： clustering algorithms

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DACA: Distributed adaptive grid decision graph based clustering algorithm

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SOFTWARE-PRACTICE & EXPERIENCE 2022年第5期52卷 1199-1215页

作者： He, Jing Zhou, Jun Wang, Haoyu Cai, Li Yunnan Univ Natl Pilot Sch Software Kunming Yunnan Peoples R China

clustering algorithms play a very important role in machine learning. With the development of big-data artificial intelligence, distributed parallel algorithms have become an important research field. To reduce the computational complexity and running time of large-scale datasets in the clustering process, this study proposes a distributed clustering algorithm DACA (distributed adaptive grid decision graph based clustering algorithm). In a distributed environment, DACA uses relative entropy to adaptively mesh the data to form an obvious sparse grid and dense grid. Then, the decision graph is used to determine the cluster center mesh object. Finally, the KD-tree is used to accelerate the determination of the cluster center of sparse points to complete clustering. The algorithm is implemented using the popular Apache Spark computing framework, compared with other distributed clustering algorithms, DACA can adaptively divide the grid according to the data distribution to obtain better clustering effect. At the same time, KD tree algorithm is used to speed up the decision-making of clustering center. Numerous experiments show that the DACA algorithm has excellent performance and accuracy on six standard datasets and real GPS trajectory datasets.

关键词： adaptive grid division clustering algorithms decision graphs distributed KD-tree

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A Survey and Study of Signal and Data-Driven Approaches for Pipeline Leak Detection and Localization

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JOURNAL OF PIPELINE SYSTEMS ENGINEERING AND PRACTICE 2024年第2期15卷

作者： Rajasekaran, Uma Kothandaraman, Mohanaprasad Vellore Inst Technol VIT Univ Sch Elect Engn SENSE Chennai 600127 Tamil Nadu India

A pipeline is critical in conveying water, oil, gas, petrochemicals, and slurry. As the pipeline ages and corrodes, it becomes susceptible to deterioration, resulting in wastage and hazardous damages depending on the material it transports. To mitigate these risks, implementing a suitable monitoring system becomes essential, enabling the early identification of damage and minimizing waste and the potential for hazardous incidents. The pipeline monitoring system can be exterior, visual/biological, and computational. This paper surveys state-of-the-art approaches and also performs experimental analyses with a few methods in signal/data-driven approaches within computational methods. More precisely, signal processing-based leak localization methods, artificial intelligence-based leak detection methods, and combined approaches are given. This paper implements five signal processing-based methods and 17 artificial intelligence-based methods. This implementation helps to compare and understand the significance of appropriate noise removal and feature extraction. The data for this analysis is collected using acousto-optic sensors from an experimental setup. After implementation, the highest observed leak localization accuracy is 99.14% with the wavelet packet adaptive independent component analysis-based generalized cross correlation, and the highest leak detection accuracy is 98.32% with the one-dimensional convolutional neural network.

关键词： Generalized cross correlation (GCC) Basic cross-correlation (BCC) Boosting algorithms clustering algorithms Convolutional neural network Leak detection and localization

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A Decoder-Free Variational Deep Embedding for Unsupervised clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第10期33卷 5681-5693页

作者： Ji, Qiang Sun, Yanfeng Gao, Junbin Hu, Yongli Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Univ Sydney Business Sch Discipline Business Analyt Sydney NSW 2006 Australia

In deep clustering frameworks, autoencoder (AE)- or variational AE-based clustering approaches are the most popular and competitive ones that encourage the model to obtain suitable representations and avoid the tendency for degenerate solutions simultaneously. However, for the clustering task, the decoder for reconstructing the original input is usually useless when the model is finished training. The encoder-decoder architecture limits the depth of the encoder so that the learning capacity is reduced severely. In this article, we propose a decoder-free variational deep embedding for unsupervised clustering (DFVC). It is well known that minimizing reconstruction error amounts to maximizing a lower bound on the mutual information (MI) between the input and its representation. That provides a theoretical guarantee for us to discard the bloated decoder. Inspired by contrastive self-supervised learning, we can directly calculate or estimate the MI of the continuous variables. Specifically, we investigate unsupervised representation learning by simultaneously considering the MI estimation of continuous representations and the MI computation of categorical representations. By introducing the data augmentation technique, we incorporate the original input, the augmented input, and their high-level representations into the MI estimation framework to learn more discriminative representations. Instead of matching to a simple standard normal distribution adversarially, we use end-to-end learning to constrain the latent space to be cluster-friendly by applying the Gaussian mixture distribution as the prior. Extensive experiments on challenging data sets show that our model achieves higher performance over a wide range of state-of-the-art clustering approaches.

关键词： clustering algorithms Data models Neural networks Image reconstruction Decoding Training Estimation Augmented mutual information (MI) deep clustering self-supervised learning (SSL) variational embedding

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Multigraph Random Walk for Joint Learning of Multiview clustering and Semisupervised Classification

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2022年第3期9卷 926-939页

作者： Wang, Shiping Fu, Lele Wang, Zhewen Xu, Haiping Zhu, William Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 610054 Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou 350108 Peoples R China Minjiang Univ Coll Math & Data Sci Fuzhou 350108 Peoples R China

Recent researches on multiview learning have received widespread attention due to the increasing generalization of multiview data. As an effective probabilistic model, random walk has also shown encouraging performance in various fields. To further exploit the potential of utilizing random walk schemes to address multiview learning problems, this article proposes a simple yet efficient multigraph random walk scheme for both multiview clustering and semisupervised classification tasks. The proposed model integrates random walk with multiview learning, and recursively learns a globally stable probability distribution matrix from multiple views, on the basis of which the label indicator is obtained in the scene of clustering or semisupervised classification. Furthermore, an adaptive weight vector is learned to incorporate the diversity and complementarity of multiview data. Besides, the relationships between the proposed scheme and spectral clustering, neighborhood embedding and manifold embedding are analyzed theoretically. Finally, comprehensive comparative experiments are conducted with several state-of-the-art multiview clustering and semisupervised classification methods on eight real-world datasets. The experimental results demonstrate the superiority of the proposed method in terms of both clustering and classification performance.

关键词： Task analysis Correlation clustering algorithms Probability distribution Mathematical model Analytical models Tensors Multigraph multimodal fusion multiview clustering random walk semisupervised classification

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GSelf-MapReduce: A Method for Enhancing Mapreduce Performance in Distributed Heterogeneous Data Centers

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IEEE ACCESS 2024年 12卷 159503-159518页

作者： Sesen, Emin Kirisoglu, Serdar Kara, Resul Duzce Univ Dept Comp Engn TR-81620 Duzce Turkiye

Big data are often stored close to the locations where they are generated, owing to the cost of data transfer. These stored data are moved to a single location for processing or processed at that location. In the literature, it is possible to find different methods for processing data in distributed data centers. In this study, we present a new method for data processing called GSelf-MapReduce. In the proposed method, shuffling is performed among heterogeneous data center (DC) that complete the data-processing process. To calculate the data processing cost of the reduce function of the DCs, a polynomial regression model was created using the data obtained in the test environment, and the coefficients obtained from this model were used in the decision process. The key/value pairs to be shuffled are distributed according to the cost of the DCs, considering their location. In addition, not all DCs are waited to finish their job for shuffling. DCs that complete their job perform shuffling among themselves. Thus, the keys are deduplicated between these DCs. The shuffling volume in the last phase and the total job completion time are reduced. The performance of the proposed method was compared with that of four different distributed data processing methods in the literature. As a result, this work generates 15% less shuffled data than the closest work.

关键词： Data processing Distributed databases Data centers Costs Big Data Sparks Data models clustering algorithms Servers Mathematical models Big data data reduction geo-distributed mapreduce heterogenous data centers

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BiG-Fed: Bilevel Optimization Enhanced Graph-Aided Federated Learning

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IEEE TRANSACTIONS ON BIG DATA 2024年第6期10卷 903-914页

作者： Xing, Pengwei Lu, Songtao Wu, Lingfei Yu, Han Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore IBM Thomas J Watson Res Ctr Yorktown Hts NY 10562 USA JD Silicon Valley Res Ctr Mountain View CA 94043 USA

In federated learning (FL), due to the non-i.i.d. nature of distributedly owned local datasets, personalization is an important design goal. In this paper, we investigate FL scenarios in which data owners are related by a network topology (e.g., traffic prediction based on sensor networks). Existing personalized FL approaches cannot take this information into account. To address this limitation, we propose the Bilevel Optimization enhanced Graph-aided Federated Learning (BiG-Fed) approach. The inner weights enable local tasks to evolve towards personalization, and the outer shared weights on the server side target the non-i.i.d problem enabling individual tasks to evolve towards a global constraint space. To the best of our knowledge, BiG-Fed is the first bilevel optimization technique to enable FL approaches to cope with two nested optimization tasks at the FL server and FL clients simultaneously. Theoretical analysis shows that BiG-Fed is guaranteed to converge in an efficient manner. Extensive experiments on both synthetic and real-world data demonstrate significant superior performance of BiG-Fed over seven state-of-the-art methods.

关键词： Task analysis Optimization Servers Collaborative work Jacobian matrices Data models clustering algorithms Bilevel optimization federated learning graph neural networks

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Soil Moisture Retrieval Using BuFeng-1 A/B Based on Land Surface clustering Algorithm

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 4680-4689页

作者： Guo, Zhizhou Liu, Baojian Wan, Wei Lu, Feng Niu, Xinliang Ji, Rui Jing, Cheng Li, Weiqiang Chen, Xiuwan Yang, Jun Bai, Zhaoguang Peking Univ Sch Earth & Space Sci Inst Remote Sensing & GIS Beijing 100871 Peoples R China China Meteorol Adm Key Lab Radiometr Calibrat & Validat Environm Sat Natl Satellite Meteorol Ctr Natl Ctr Space Weather Beijing Peoples R China Innovat Ctr FengYun Meteorol Satellite FYSIC Beijing 100081 Peoples R China China Acad Space Technol Xian Branch CAST XIAN Xian 710100 Peoples R China CSIC Inst Space Sci ICE Earth Observat Res Grp Barcelona 08193 Spain DFH Satellite Co Ltd Beijing 100094 Peoples R China

A new land surface clustering algorithm is developed to retrieve soil moisture (SM) using the Global Navigation Satellite System reflectometry (GNSS-R) technique. Data from the BuFeng-1 (BF-1) twin satellites A/B, a pilot mission for the Chinese GNSS-R constellation, is used for SM retrieval. The core concept of the algorithm is to cluster global land areas into different types according to the land properties and calculate the SM type by type, based on the linear relationship between equivalent specular reflectivity and SM. The global comparison between the results and SM product from the Soil Moisture Active Passive mission shows the correlation coefficient (R) is 0.82, and unbiased root mean square error (ubRMSE) is 0.070 cm(3)center dot cm(-3). The results also show good agreement compared with in situ SM measurements with the mean ubRMSE of 0.036 cm(3)center dot cm(-3). This study proves that the global SM can be retrieved successfully from the BF-1 mission with the land surface clustering algorithm. By taking full advantage of the similarity of land surface physical properties in different regions, the algorithm provides a practical approach for global SM retrieval using spaceborne GNSS-R data.

关键词： Land surface Sea surface Surface roughness Rough surfaces Satellite broadcasting clustering algorithms Vegetation mapping Bufeng-1 (BF-1) global navigation satellite system reflectometry (GNSS-R) land surface clustering soil moisture (SM)

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Experimenting with Feature Constraints Using the NBC clustering Algorithm 28th

Experimenting with Feature Constraints Using the NBC Cluster...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Lasek, Piotr Lasek, Krzysztof University of Rzeszów Pigonia 1 Rzeszów35-310 Poland

clustering is a widely utilized data mining technique aimed at discovering unknown patterns in datasets, typically performed in an unsupervised manner. However, the incorporation of external knowledge, transforming it into a semi-supervised process, has shown promising results in improving clustering performance. This study explores the application of feature constraints in clustering algorithms, specifically focusing on relative constraints, which are effective in embedding domain knowledge. We introduce a method to use relative constraints as features, integrating them into the Neighborhood-Based clustering algorithm. Our experiments demonstrate the impact of these constraints on clustering accuracy and efficiency using various benchmark datasets. The results indicate that the proposed method enhances clustering quality by effectively incorporating domain-specific constraints, offering a novel approach to semi-supervised clustering. © 2024 The Authors.

关键词： clustering algorithms

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A Novel Hybrid Model Based on Sentiment Analysis Using Community Guided Link Prediction for Connecting People With Similar Interest

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IEEE ACCESS 2024年 12卷 139435-139455页

作者： Luthra, Snigdha Kumar, Rakesh Gupta, Meenu Chandigarh Univ Dept Comp Sci & Engn Mohali 140413 Punjab India

In this study, a novel hybrid recommendation model is designed for popular social media platforms such as Facebook, Twitter, WhatsApp, and Instagram. Recognizing the global significance of these platforms, the model aims to facilitate connections among users with similar interests, leveraging community detection algorithms. By integrating sentimental analysis, link prediction, and community detection techniques, a comprehensive network is constructed that enhances user experience and addresses the escalating issue of cyberbullying in the era of increasing connectivity. The proposed model strategically combines nodes and edges to form communities tailored to individual user interests. Social media comments, gathered randomly from the Facebook API, undergo sentimental analysis to evaluate the model's performance. The innovative Community-guided Link Prediction algorithm using Sentimental Analysis (CLPSA) for multilayer networks is employed, aiding in the identification of nodes associated with fake comments and thereby mitigating cyberbullying problems. Integrating sentiment analysis enhances understanding of human emotions, and users in the same cluster are more likely to connect in the future, promoting positive engagement. The model's novelty lies in its utilization of the Proposed Cluster Edge Betweenness algorithm, which identifies clusters of users with denser connections among them while being loosely connected to other separate modules. This approach helps create a network structure that fosters positive interactions and reduces the occurrence of intolerable comments. The experimental comparison of community detection algorithms underscores the effectiveness of our approach, particularly the superior performance of the Cluster Edge Betweenness algorithm. It achieves high accuracy in identifying user interests and forming cohesive clusters, essential for enhancing user engagement and community cohesion within social media networks. The qualitative analysis of c

关键词： Cyberbullying Prediction algorithms clustering algorithms Blogs Detection algorithms Analytical models Semantics Sentiment analysis Community detection social media networks (SMNs) sentimental analysis cyberbullying link prediction

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