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Journal articles on the topic 'Network data representation'

Author: Grafiati

Published: 7 September 2024

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1

R.Tamilarasu and G. Soundarya Devi. "Improvising Connection In 5g By Means Of Particle Swarm Optimization Techniques." South Asian Journal of Engineering and Technology 14, no. 2 (2024): 1–6. http://dx.doi.org/10.26524/sajet.2023.14.2.

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Data and network embedding techniques are essential for representing complex data structures in a lower-dimensional space, aiding in tasks like data inference and network reconstruction by assigning nodes to concise representations while preserving the network's structure. The integration of Particle Swarm Optimization (PSO) with matrix factorization methods optimizes mapping functions and parameters during the embedding process, enhancing representation learning efficiency. Combining PSO with techniques like Deep Walk highlights its adaptability as a robust optimization tool for extracting me

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Ye, Zhonglin, Haixing Zhao, Ke Zhang, Yu Zhu, and Zhaoyang Wang. "An Optimized Network Representation Learning Algorithm Using Multi-Relational Data." Mathematics 7, no. 5 (2019): 460. http://dx.doi.org/10.3390/math7050460.

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Representation learning aims to encode the relationships of research objects into low-dimensional, compressible, and distributed representation vectors. The purpose of network representation learning is to learn the structural relationships between network vertices. Knowledge representation learning is oriented to model the entities and relationships in knowledge bases. In this paper, we first introduce the idea of knowledge representation learning into network representation learning, namely, we propose a new approach to model the vertex triplet relationships based on DeepWalk without TransE.

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Armenta, Marco, and Pierre-Marc Jodoin. "The Representation Theory of Neural Networks." Mathematics 9, no. 24 (2021): 3216. http://dx.doi.org/10.3390/math9243216.

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In this work, we show that neural networks can be represented via the mathematical theory of quiver representations. More specifically, we prove that a neural network is a quiver representation with activation functions, a mathematical object that we represent using a network quiver. Furthermore, we show that network quivers gently adapt to common neural network concepts such as fully connected layers, convolution operations, residual connections, batch normalization, pooling operations and even randomly wired neural networks. We show that this mathematical representation is by no means an app

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Aristizábal Q, Luz Angela, and Nicolás Toro G. "Multilayer Representation and Multiscale Analysis on Data Networks." International journal of Computer Networks & Communications 13, no. 3 (2021): 41–55. http://dx.doi.org/10.5121/ijcnc.2021.13303.

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The constant increase in the complexity of data networks motivates the search for strategies that make it possible to reduce current monitoring times. This paper shows the way in which multilayer network representation and the application of multiscale analysis techniques, as applied to software-defined networks, allows for the visualization of anomalies from "coarse views of the network topology". This implies the analysis of fewer data, and consequently the reduction of the time that a process takes to monitor the network. The fact that software-defined networks allow for the obtention of a

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Nguyễn, Tuấn, Nguyen Hai Hao, Dang Le Dinh Trang, Nguyen Van Tuan, and Cao Van Loi. "Robust anomaly detection methods for contamination network data." Journal of Military Science and Technology, no. 79 (May 19, 2022): 41–51. http://dx.doi.org/10.54939/1859-1043.j.mst.79.2022.41-51.

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Recently, latent representation models, such as Shrink Autoencoder (SAE), have been demonstrated as robust feature representations for one-class learning-based network anomaly detection. In these studies, benchmark network datasets that are processed in laboratory environments to make them completely clean are often employed for constructing and evaluating such models. In real-world scenarios, however, we can not guarantee 100% to collect pure normal data for constructing latent representation models. Therefore, this work aims to investigate the characteristics of the latent representation of

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Du, Xin, Yulong Pei, Wouter Duivesteijn, and Mykola Pechenizkiy. "Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 3809–16. http://dx.doi.org/10.1609/aaai.v34i04.5792.

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While recent advances in machine learning put many focuses on fairness of algorithmic decision making, topics about fairness of representation, especially fairness of network representation, are still underexplored. Network representation learning learns a function mapping nodes to low-dimensional vectors. Structural properties, e.g. communities and roles, are preserved in the latent embedding space. In this paper, we argue that latent structural heterogeneity in the observational data could bias the classical network representation model. The unknown heterogeneous distribution across subgroup

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Dongming Chen, Dongming Chen, Mingshuo Nie Dongming Chen, Jiarui Yan Mingshuo Nie, Jiangnan Meng Jiarui Yan, and Dongqi Wang Jiangnan Meng. "Network Representation Learning Algorithm Based on Community Folding." 網際網路技術學刊 23, no. 2 (2022): 415–23. http://dx.doi.org/10.53106/160792642022032302020.

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<p>Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space, which can reduce the temporal and spatial complexity of downstream network data mining such as node classification and graph clustering. This paper addresses the problem that neighborhood information-based network representation learning algorithm ignores the global topological information of the network. We propose the Network Representation Learning Algorithm Based on Community Folding (CF-NRL) considering the influence of community structur

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Zhang, Xiaoxian, Jianpei Zhang, and Jing Yang. "Large-scale dynamic social data representation for structure feature learning." Journal of Intelligent & Fuzzy Systems 39, no. 4 (2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.

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The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social netwo

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Kapoor, Maya, Michael Napolitano, Jonathan Quance, Thomas Moyer, and Siddharth Krishnan. "Detecting VoIP Data Streams: Approaches Using Hidden Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15519–27. http://dx.doi.org/10.1609/aaai.v37i13.26840.

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The use of voice-over-IP technology has rapidly expanded over the past several years, and has thus become a significant portion of traffic in the real, complex network environment. Deep packet inspection and middlebox technologies need to analyze call flows in order to perform network management, load-balancing, content monitoring, forensic analysis, and intelligence gathering. Because the session setup and management data can be sent on different ports or out of sync with VoIP call data over the Real-time Transport Protocol (RTP) with low latency, inspection software may miss calls or parts o

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Giannarakis, Nick, Alexandra Silva, and David Walker. "ProbNV: probabilistic verification of network control planes." Proceedings of the ACM on Programming Languages 5, ICFP (2021): 1–30. http://dx.doi.org/10.1145/3473595.

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ProbNV is a new framework for probabilistic network control plane verification that strikes a balance between generality and scalability. ProbNV is general enough to encode a wide range of features from the most common protocols (eBGP and OSPF) and yet scalable enough to handle challenging properties, such as probabilistic all-failures analysis of medium-sized networks with 100-200 devices. When there are a small, bounded number of failures, networks with up to 500 devices may be verified in seconds. ProbNV operates by translating raw CISCO configurations into a probabilistic and functional pr

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Hyvönen, Jörkki, Jari Saramäki, and Kimmo Kaski. "Efficient data structures for sparse network representation." International Journal of Computer Mathematics 85, no. 8 (2008): 1219–33. http://dx.doi.org/10.1080/00207160701753629.

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Wong, S. V., and A. M. S. Hamouda. "Machinability data representation with artificial neural network." Journal of Materials Processing Technology 138, no. 1-3 (2003): 538–44. http://dx.doi.org/10.1016/s0924-0136(03)00143-2.

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Buckles, Bill P., Frederick E. Petry, and Jayadev Pillai. "Network data models for representation of uncertainty." Fuzzy Sets and Systems 38, no. 2 (1990): 171–90. http://dx.doi.org/10.1016/0165-0114(90)90148-y.

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Zhan, Huixin, and Victor S. Sheng. "Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16143–44. http://dx.doi.org/10.1609/aaai.v37i13.26932.

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Although recent network representation learning (NRL) works in text-attributed networks demonstrated superior performance for various graph inference tasks, learning network representations could always raise privacy concerns when nodes represent people or human-related variables. Moreover, standard NRLs that leverage structural information from a graph proceed by first encoding pairwise relationships into learned representations and then analysing its properties. This approach is fundamentally misaligned with problems where the relationships involve multiple points, and topological structure

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Zhang, Hu, Jingjing Zhou, Ru Li, and Yue Fan. "Network representation learning method embedding linear and nonlinear network structures." Semantic Web 13, no. 3 (2022): 511–26. http://dx.doi.org/10.3233/sw-212968.

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With the rapid development of neural networks, much attention has been focused on network embedding for complex network data, which aims to learn low-dimensional embedding of nodes in the network and how to effectively apply learned network representations to various graph-based analytical tasks. Two typical models exist namely the shallow random walk network representation method and deep learning models such as graph convolution networks (GCNs). The former one can be used to capture the linear structure of the network using depth-first search (DFS) and width-first search (BFS), whereas Hiera

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Vernon, Matthew C., and Matt J. Keeling. "Representing the UK's cattle herd as static and dynamic networks." Proceedings of the Royal Society B: Biological Sciences 276, no. 1656 (2008): 469–76. http://dx.doi.org/10.1098/rspb.2008.1009.

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Network models are increasingly being used to understand the spread of diseases through sparsely connected populations, with particular interest in the impact of animal movements upon the dynamics of infectious diseases. Detailed data collected by the UK government on the movement of cattle may be represented as a network, where animal holdings are nodes, and an edge is drawn between nodes where a movement of animals has occurred. These network representations may vary from a simple static representation, to a more complex, fully dynamic one where daily movements are explicitly captured. Using

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Iddianozie, Chidubem, and Gavin McArdle. "Towards Robust Representations of Spatial Networks Using Graph Neural Networks." Applied Sciences 11, no. 15 (2021): 6918. http://dx.doi.org/10.3390/app11156918.

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The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogene

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Hu, Hao, Mengya Gao, and Mingsheng Wu. "Relieving the Incompatibility of Network Representation and Classification for Long-Tailed Data Distribution." Computational Intelligence and Neuroscience 2021 (December 27, 2021): 1–10. http://dx.doi.org/10.1155/2021/6702625.

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In the real-world scenario, data often have a long-tailed distribution and training deep neural networks on such an imbalanced dataset has become a great challenge. The main problem caused by a long-tailed data distribution is that common classes will dominate the training results and achieve a very low accuracy on the rare classes. Recent work focuses on improving the network representation ability to overcome the long-tailed problem, while it always ignores adapting the network classifier to a long-tailed case, which will cause the “incompatibility” problem of network representation and netw

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Xu, Jian, Thanuka L. Wickramarathne, and Nitesh V. Chawla. "Representing higher-order dependencies in networks." Science Advances 2, no. 5 (2016): e1600028. http://dx.doi.org/10.1126/sciadv.1600028.

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To ensure the correctness of network analysis methods, the network (as the input) has to be a sufficiently accurate representation of the underlying data. However, when representing sequential data from complex systems, such as global shipping traffic or Web clickstream traffic as networks, conventional network representations that implicitly assume the Markov property (first-order dependency) can quickly become limiting. This assumption holds that, when movements are simulated on the network, the next movement depends only on the current node, discounting the fact that the movement may depend

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Zhang, Yixin, Lizhen Cui, Wei He, Xudong Lu, and Shipeng Wang. "Behavioral data assists decisions: exploring the mental representation of digital-self." International Journal of Crowd Science 5, no. 2 (2021): 185–203. http://dx.doi.org/10.1108/ijcs-03-2021-0011.

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Purpose The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell. Design/methodology/approach In this paper

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Decker, Kevin T., and Brett J. Borghetti. "Hyperspectral Point Cloud Projection for the Semantic Segmentation of Multimodal Hyperspectral and Lidar Data with Point Convolution-Based Deep Fusion Neural Networks." Applied Sciences 13, no. 14 (2023): 8210. http://dx.doi.org/10.3390/app13148210.

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The fusion of dissimilar data modalities in neural networks presents a significant challenge, particularly in the case of multimodal hyperspectral and lidar data. Hyperspectral data, typically represented as images with potentially hundreds of bands, provide a wealth of spectral information, while lidar data, commonly represented as point clouds with millions of unordered points in 3D space, offer structural information. The complementary nature of these data types presents a unique challenge due to their fundamentally different representations requiring distinct processing methods. In this wo

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Liang, Sen, Zhi-ze Zhou, Yu-dong Guo, Xuan Gao, Ju-yong Zhang, and Hu-jun Bao. "Facial landmark disentangled network with variational autoencoder." Applied Mathematics-A Journal of Chinese Universities 37, no. 2 (2022): 290–305. http://dx.doi.org/10.1007/s11766-022-4589-0.

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AbstractLearning disentangled representation of data is a key problem in deep learning. Specifically, disentangling 2D facial landmarks into different factors (e.g., identity and expression) is widely used in the applications of face reconstruction, face reenactment and talking head et al.. However, due to the sparsity of landmarks and the lack of accurate labels for the factors, it is hard to learn the disentangled representation of landmarks. To address these problem, we propose a simple and effective model named FLD-VAE to disentangle arbitrary facial landmarks into identity and expression

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Craven, Mark W., and Jude W. Shavlik. "Understanding Time-Series Networks: A Case Study in Rule Extraction." International Journal of Neural Systems 08, no. 04 (1997): 373–84. http://dx.doi.org/10.1142/s0129065797000380.

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A significant limitation of neural networks is that the representation they learn are usually incomprehensible to humans. We have developed an algorithm, called TREPAN, for extracting comprehensible, symbolic representations from trained neural networks. Given a trained network, TREPAN produces a decision tree that approximates the concept represented by the network. In this article, we discuss the application of TREPAN to a neural network trained on a noisy time series task: predicting the Dollar–Mark exchange rate. We present experiments that show that TREPAN is able to extract a decision tr

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Bast, Hannah, and Sabine Storandt. "Frequency Data Compression for Public Transportation Network Algorithms (Extended Abstract)." Proceedings of the International Symposium on Combinatorial Search 4, no. 1 (2021): 205–6. http://dx.doi.org/10.1609/socs.v4i1.18302.

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Timetable information in public transportation networks exhibit a large degree of redundancy; e.g. consider a bus going from station A to station B at 6:00, 6:15, 6:30, 6:45, 7:00, 7:15, 7:30, . . . , 20:00, the very same data can be provided by a frequency-based representation as ’6:00-20:00, every 15 minutes’ in considerably less space. Nevertheless a common graph model for routing in public transportation networks is the time-expanded representation where for each arrival/departure event a single node is created. We will introduce a frequency-based graph model which allows for a significant

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Xu, Liang, Yue Zhao, Xiaona Xu, Yigang Liu, and Qiang Ji. "Latent Regression Bayesian Network for Speech Representation." Electronics 12, no. 15 (2023): 3342. http://dx.doi.org/10.3390/electronics12153342.

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In this paper, we present a novel approach for speech representation using latent regression Bayesian networks (LRBN) to address the issue of poor performance in low-resource language speech systems. LRBN, a lightweight unsupervised learning model, learns data distribution and high-level features, unlike computationally expensive large models, such as Wav2vec 2.0. To evaluate the effectiveness of LRBN in learning speech representations, we conducted experiments on five different low-resource languages and applied them to two downstream tasks: phoneme classification and speech recognition. Our

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Naseer, Sheraz, Rao Faizan Ali, P. D. D. Dominic, and Yasir Saleem. "Learning Representations of Network Traffic Using Deep Neural Networks for Network Anomaly Detection: A Perspective towards Oil and Gas IT Infrastructures." Symmetry 12, no. 11 (2020): 1882. http://dx.doi.org/10.3390/sym12111882.

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Oil and Gas organizations are dependent on their IT infrastructure, which is a small part of their industrial automation infrastructure, to function effectively. The oil and gas (O&G) organizations industrial automation infrastructure landscape is complex. To perform focused and effective studies, Industrial systems infrastructure is divided into functional levels by The Instrumentation, Systems and Automation Society (ISA) Standard ANSI/ISA-95:2005. This research focuses on the ISA-95:2005 level-4 IT infrastructure to address network anomaly detection problem for ensuring the security and

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Gatts, C., and A. Mariano. "Data Categorization and Neural Pattern Recognition." Microscopy and Microanalysis 3, S2 (1997): 933–34. http://dx.doi.org/10.1017/s1431927600011557.

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The natural ability of Artificial Neural Networks to perform pattern recognition tasks makes them a valuable tool in Electron Microscopy, especially when large data sets are involved. The application of Neural Pattern Recognition to HREM, although incipient, has already produced interesting results both for one dimensional spectra and 2D images.In the case of ID spectra, e.g. a set of EELS spectra acquired during a line scan, given a “vigilance parameter” (which sets the threshold for the correlation between two spectra to be high enough to consider them as similar) an ART-like network can dis

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Altuntas, Volkan. "NodeVector: A Novel Network Node Vectorization with Graph Analysis and Deep Learning." Applied Sciences 14, no. 2 (2024): 775. http://dx.doi.org/10.3390/app14020775.

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Network node embedding captures structural and relational information of nodes in the network and allows for us to use machine learning algorithms for various prediction tasks on network data that have an inherently complex and disordered structure. Network node embedding should preserve as much information as possible about important network properties where information is stored, such as network structure and node properties, while representing nodes as numerical vectors in a lower-dimensional space than the original higher dimensional space. Superior node embedding algorithms are a powerful

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Zhang, Ye, Yanqi Gao, Yupeng Zhou, Jianan Wang, and Minghao Yin. "MRMLREC: A Two-Stage Approach for Addressing Data Sparsity in MOOC Video Recommendation (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23709–11. http://dx.doi.org/10.1609/aaai.v38i21.30536.

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With the abundance of learning resources available on massive open online courses (MOOCs) platforms, the issue of interactive data sparsity has emerged as a significant challenge.This paper introduces MRMLREC, an efficient MOOC video recommendation which consists of two main stages: multi-relational representation and multi-level recommendation, aiming to solve the problem of data sparsity. In the multi-relational representation stage, MRMLREC adopts a tripartite approach, constructing relational graphs based on temporal sequences, courses-videos relation, and knowledge concepts-video relation

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Milano, Marianna, Giuseppe Agapito, and Mario Cannataro. "Challenges and Limitations of Biological Network Analysis." BioTech 11, no. 3 (2022): 24. http://dx.doi.org/10.3390/biotech11030024.

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High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms’ properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein–Protein Interactio

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Rossi, R. A., L. K. McDowell, D. W. Aha, and J. Neville. "Transforming Graph Data for Statistical Relational Learning." Journal of Artificial Intelligence Research 45 (October 30, 2012): 363–441. http://dx.doi.org/10.1613/jair.3659.

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Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this article, we examine and categorize techniques for transforming graph-based relational data to improve SRL algorithms. In particular, appropriate transformations of the nodes, links, and/or features of the data can dramatically affect the capabilities and results of SRL algorithms. We introduce an

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Zhang, Sen, Shaobo Li, Xiang Li, and Yong Yao. "Representation of Traffic Congestion Data for Urban Road Traffic Networks Based on Pooling Operations." Algorithms 13, no. 4 (2020): 84. http://dx.doi.org/10.3390/a13040084.

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In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks a

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Shcherbakov, A. V., V. G. Kharitonenko, A. I. Chuprov, and A. E. Gainov. "ENSURING DATA UNIQUENESS IN SEMANTIC NETWORKS." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 228 (June 2023): 36–40. http://dx.doi.org/10.14489/vkit.2023.06.pp.036-040.

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The article gives a brief description of knowledge representation models. Atoms of meaning (basic, minimal informational units) combined with each other to express a common meaning represent knowledge (data about data, metadata). It is shown that most of the existing knowledge representation models are based on the network representation model. A method is proposed to ensure the uniqueness (originality) of a set of data underlying the network model of knowledge representation. To ensure the uniqueness of knowledge representation by a set of data, the article proposes to use the main theorem of

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Heo, Seongsil, Sungsik Kim, and Jaekoo Lee. "BIMO: Bootstrap Inter–Intra Modality at Once Unsupervised Learning for Multivariate Time Series." Applied Sciences 14, no. 9 (2024): 3825. http://dx.doi.org/10.3390/app14093825.

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It is difficult to learn meaningful representations of time-series data since they are sparsely labeled and unpredictable. Hence, we propose bootstrap inter–intra modality at once (BIMO), an unsupervised representation learning method based on time series. Unlike previous works, the proposed BIMO method learns both inter-sample and intra-temporal modality representations simultaneously without negative pairs. BIMO comprises a main network and two auxiliary networks, namely inter-auxiliary and intra-auxiliary networks. The main network is trained to learn inter–intra modality representations se

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Idiart, Marco, Barry Berk, and L. F. Abbott. "Reduced Representation by Neural Networks with Restricted Receptive Fields." Neural Computation 7, no. 3 (1995): 507–17. http://dx.doi.org/10.1162/neco.1995.7.3.507.

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Model neural networks can perform dimensional reductions of input data sets using correlation-based learning rules to adjust their weights. Simple Hebbian learning rules lead to an optimal reduction at the single unit level but result in highly redundant network representations. More complex rules designed to reduce or remove this redundancy can develop optimal principal component representations, but they are not very compelling from a biological perspective. Neurons in biological networks have restricted receptive fields limiting their access to the input data space. We find that, within thi

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Bautista, John Lorenzo, Yun Kyung Lee, and Hyun Soon Shin. "Speech Emotion Recognition Based on Parallel CNN-Attention Networks with Multi-Fold Data Augmentation." Electronics 11, no. 23 (2022): 3935. http://dx.doi.org/10.3390/electronics11233935.

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In this paper, an automatic speech emotion recognition (SER) task of classifying eight different emotions was experimented using parallel based networks trained using the Ryeson Audio-Visual Dataset of Speech and Song (RAVDESS) dataset. A combination of a CNN-based network and attention-based networks, running in parallel, was used to model both spatial features and temporal feature representations. Multiple Augmentation techniques using Additive White Gaussian Noise (AWGN), SpecAugment, Room Impulse Response (RIR), and Tanh Distortion techniques were used to augment the training data to furth

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Liu, Hao, Jindong Han, Yanjie Fu, Jingbo Zhou, Xinjiang Lu, and Hui Xiong. "Multi-modal transportation recommendation with unified route representation learning." Proceedings of the VLDB Endowment 14, no. 3 (2020): 342–50. http://dx.doi.org/10.14778/3430915.3430924.

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Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learni

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Zhang, Kainan, Zhipeng Cai, and Daehee Seo. "Privacy-Preserving Federated Graph Neural Network Learning on Non-IID Graph Data." Wireless Communications and Mobile Computing 2023 (February 3, 2023): 1–13. http://dx.doi.org/10.1155/2023/8545101.

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Since the concept of federated learning (FL) was proposed by Google in 2017, many applications have been combined with FL technology due to its outstanding performance in data integration, computing performance, privacy protection, etc. However, most traditional federated learning-based applications focus on image processing and natural language processing with few achievements in graph neural networks due to the graph’s nonindependent identically distributed (IID) nature. Representation learning on graph-structured data generates graph embedding, which helps machines understand graphs effecti

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Wang, Jing, Songhe Feng, Gengyu Lyu, and Jiazheng Yuan. "SURER: Structure-Adaptive Unified Graph Neural Network for Multi-View Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15520–27. http://dx.doi.org/10.1609/aaai.v38i14.29478.

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Deep Multi-view Graph Clustering (DMGC) aims to partition instances into different groups using the graph information extracted from multi-view data. The mainstream framework of DMGC methods applies graph neural networks to embed structure information into the view-specific representations and fuse them for the consensus representation. However, on one hand, we find that the graph learned in advance is not ideal for clustering as it is constructed by original multi-view data and localized connecting. On the other hand, most existing methods learn the consensus representation in a late fusion m

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Poulton, Mary M., Ben K. Sternberg, and Charles E. Glass. "Location of subsurface targets in geophysical data using neural networks." GEOPHYSICS 57, no. 12 (1992): 1534–44. http://dx.doi.org/10.1190/1.1443221.

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Neural networks were used to estimate the offset, depth, and conductivity‐area product of a conductive target given an electromagnetic ellipticity image of the target. Five different neural network paradigms and five different representations of the ellipticity image were compared. The networks were trained with synthetic images of the target and tested on field data and more synthetic data. The extrapolation capabilities of the networks were also tested with synthetic data lying outside the spatial limits of the training set. The data representations consisted of the whole image, the subsampl

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Bartsev, S. I., P. M. Baturina, and G. M. Markova. "Neural Network-Based Decoding Input Stimulus Data Based on Recurrent Neural Network Neural Activity Pattern." Doklady Biological Sciences 502, no. 1 (2022): 1–5. http://dx.doi.org/10.1134/s001249662201001x.

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Abstract The paper reports the assessment of the possibility to recover information obtained using an artificial neural network via inspecting neural activity patterns. A simple recurrent neural network forms dynamic excitation patterns for storing data on input stimulus in the course of the advanced delayed match to sample test with varying duration of pause between the received stimuli. Information stored in these patterns can be used by the neural network at any moment within the specified interval (three to six clock cycles), whereby it appears possible to detect invariant representation o

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Liu, Xinlong, Chu He, Dehui Xiong, and Mingsheng Liao. "Pattern Statistics Network for Classification of High-Resolution SAR Images." Remote Sensing 11, no. 16 (2019): 1942. http://dx.doi.org/10.3390/rs11161942.

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The classification of synthetic aperture radar (SAR) images is of great importance for rapid scene understanding. Recently, convolutional neural networks (CNNs) have been applied to the classification of single-polarized SAR images. However, it is still difficult due to the random and complex spatial patterns lying in SAR images, especially in the case of finite training data. In this paper, a pattern statistics network (PSNet) is proposed to address this problem. PSNet borrows the idea from the statistics and probability theory and explicitly embeds the random nature of SAR images in the repr

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Ye, Zhonglin, Haixing Zhao, Ke Zhang, and Yu Zhu. "Multi-View Network Representation Learning Algorithm Research." Algorithms 12, no. 3 (2019): 62. http://dx.doi.org/10.3390/a12030062.

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Network representation learning is a key research field in network data mining. In this paper, we propose a novel multi-view network representation algorithm (MVNR), which embeds multi-scale relations of network vertices into the low dimensional representation space. In contrast to existing approaches, MVNR explicitly encodes higher order information using k-step networks. In addition, we introduce the matrix forest index as a kind of network feature, which can be applied to balance the representation weights of different network views. We also research the relevance amongst MVNR and several e

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Sun, Hanlin, Wei Jie, Jonathan Loo, et al. "Network Representation Learning Enhanced by Partial Community Information That Is Found Using Game Theory." Information 12, no. 5 (2021): 186. http://dx.doi.org/10.3390/info12050186.

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Presently, data that are collected from real systems and organized as information networks are universal. Mining hidden information from these data is generally helpful to understand and benefit the corresponding systems. The challenges of analyzing such data include high computational complexity and low parallelizability because of the nature of complicated interconnected structure of their nodes. Network representation learning, also called network embedding, provides a practical and promising way to solve these issues. One of the foremost requirements of network embedding is preserving netw

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Monterubbiano, Andrea, Raphael Azorin, Gabriele Castellano, Massimo Gallo, Salvatore Pontarelli, and Dario Rossi. "SPADA: A Sparse Approximate Data Structure Representation for Data Plane Per-flow Monitoring." Proceedings of the ACM on Networking 1, CoNEXT3 (2023): 1–25. http://dx.doi.org/10.1145/3629149.

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Accurate per-flow monitoring is critical for precise network diagnosis, performance analysis, and network operation and management in general. However, the limited amount of memory available on modern programmable devices and the large number of active flows force practitioners to monitor only the most relevant flows with approximate data structures, limiting their view of network traffic. We argue that, due to the skewed nature of network traffic, such data structures are, in practice, heavily underutilized, i.e. sparse, thus wasting a significant amount of memory. This paper proposes a Spars

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Kominakis, A. P. "Graph analysis of animals' pedigrees." Archives Animal Breeding 44, no. 5 (2001): 521–30. http://dx.doi.org/10.5194/aab-44-521-2001.

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Abstract. In the present work an attempt to apply graph analysis on visual representations of animals' pedigrees is presented. Analysis of pedigree networks of moderate size (tens or hundreds of points) can substantially contribute to revealing the relational structures between animals. Partitioning graphic representations of pedigree networks to smaller parts (blocks) by means of network decomposition methods resulted in better handling and understanding of horse genealogical data. Analysis of pedigree networks could be used to estimate shortest kinship paths among animals, determinate all pr

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Tu, Wenxuan, Sihang Zhou, Xinwang Liu, et al. "Deep Fusion Clustering Network." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (2021): 9978–87. http://dx.doi.org/10.1609/aaai.v35i11.17198.

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Deep clustering is a fundamental yet challenging task for data analysis. Recently we witness a strong tendency of combining autoencoder and graph neural networks to exploit structure information for clustering performance enhancement. However, we observe that existing literature 1) lacks a dynamic fusion mechanism to selectively integrate and refine the information of graph structure and node attributes for consensus representation learning; 2) fails to extract information from both sides for robust target distribution (i.e., “groundtruth” soft labels) generation. To tackle the above issues, w

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Tian, Hao, and Reza Zafarani. "Higher-Order Networks Representation and Learning: A Survey." ACM SIGKDD Explorations Newsletter 26, no. 1 (2024): 1–18. http://dx.doi.org/10.1145/3682112.3682114.

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Network data has become widespread, larger, and more complex over the years. Traditional network data is dyadic, capturing the relations among pairs of entities. With the need to model interactions among more than two entities, significant research has focused on higher-order networks and ways to represent, analyze, and learn from them. There are two main directions to studying higher-order networks. One direction has focused on capturing higher-order patterns in traditional (dyadic) graphs by changing the basic unit of study from nodes to small frequently observed subgraphs, called motifs. As

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Esser, Pascal, Maximilian Fleissner, and Debarghya Ghoshdastidar. "Non-parametric Representation Learning with Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 11910–18. http://dx.doi.org/10.1609/aaai.v38i11.29077.

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Unsupervised and self-supervised representation learning has become popular in recent years for learning useful features from unlabelled data. Representation learning has been mostly developed in the neural network literature, and other models for representation learning are surprisingly unexplored. In this work, we introduce and analyze several kernel-based representation learning approaches: Firstly, we define two kernel Self-Supervised Learning (SSL) models using contrastive loss functions and secondly, a Kernel Autoencoder (AE) model based on the idea of embedding and reconstructing data.

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Jing, Dongsheng, Yu Yang, Zhimin Gu, Renjun Feng, Yan Li, and Haitao Jiang. "Multi-Feature Fusion in Graph Convolutional Networks for Data Network Propagation Path Tracing." Electronics 13, no. 17 (2024): 3412. http://dx.doi.org/10.3390/electronics13173412.

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With the rapid development of information technology, the complexity of data networks is increasing, especially in electric power systems, where data security and privacy protection are of great importance. Throughout the entire distribution process of the supply chain, it is crucial to closely monitor the propagation paths and dynamics of electrical data to ensure security and quickly initiate comprehensive traceability investigations if any data tampering is detected. This research addresses the challenges of data network complexity and its impact on the security of power systems by proposin

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