Filters








9,716 Hits in 7.3 sec

Data Analytics on Graphs Part III: Machine Learning on Graphs, from Graph Topology to Applications

Ljubiša Stanković, Danilo Mandic, Miloš Daković, Miloš Brajović, Bruno Scalzo, Shengxi Li, Anthony G. Constantinides
2020 Foundations and Trends® in Machine Learning  
"A tutorial on sparse signal reconstruction and its applications in signal processing". Circuits, Systems, and Signal Processing. 38(3): 1206-1263. Stoer, M. and F. Wagner (1997).  ...  Part III of this monograph starts by a comprehensive account of ways to learn the pertinent graph topology, ranging from the simplest case where the physics of the problem already suggest a possible graph  ...  ABSTRACT Modern data analytics applications on graphs often operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, rather than  ... 
doi:10.1561/2200000078-3 fatcat:el57qukkrzgbjekeocn24d5fti

Graph Signal Processing – Part III: Machine Learning on Graphs, from Graph Topology to Applications [article]

Ljubisa Stankovic, Danilo Mandic, Milos Dakovic, Milos Brajovic, Bruno Scalzo, Shengxi Li, Anthony G. Constantinides
2020 arXiv   pre-print
Part III of this monograph starts by addressing ways to learn graph topology, from the case where the physics of the problem already suggest a possible topology, through to most general cases where the  ...  graph topology is learned from the data.  ...  Summary of Graph Learning from Data Using Probabilistic Generative Models Graph data analytics with known or given topologies is feasible for applications that involve physically meaningful structures,  ... 
arXiv:2001.00426v1 fatcat:t7323epqsve2na7q7imuyhlkeq

Data Analytics on Graphs Part I: Graphs and Spectra on Graphs

Ljubiša Stanković, Danilo Mandic, Miloš Daković, Miloš Brajović, Bruno Scalzo, Shengxi Li, Anthony G. Constantinides
2020 Foundations and Trends® in Machine Learning  
At the same time, Part I serves as a basis for Part II and Part III which deal with theory, methods and applications of processing Data on Graphs and Graph Topology Learning from data.  ...  Constantinides (2020), "Data Analytics on Graphs Part I: Graphs and Spectra on Graphs", Foundations and Trends R in Machine Learning: Vol. 13, No. 1, pp 1-157.  ...  Data Analytics on Graphs Part I: Graphs and Spectra on Graphs  ... 
doi:10.1561/2200000078-1 fatcat:r4o6df4d6retdi6kksrig2taaq

BigDataStack - D2.6 Conceptual model and Reference architecture - III

Dimosthenis Kyriazis, Mauricio Fadel Argerich, Orlando Avila-García, Ainhoa Azqueta, Bin Cheng, Ismael Cuadrado-Cordero, Christos Doulkeridis, Kostas Giannakopoulos, Gal Hammer, Ricardo Jimenez, Michele Iorio, Konstantinos Kalaboukas (+19 others)
2020 Zenodo  
when moving from an atomic / single service to an application graph.  ...  This data table is only accepting insert operations and it takes part in complex analytical queries of the engine to create recommendations based on the historical data.  ...  Having pre-analysed a sample of raw data coming from our partner Eroski, a selection of the most relevant attributes that are candidates to be used during the build of the predictive model has been done  ... 
doi:10.5281/zenodo.4004585 fatcat:tiocxddpunerdkdh67qc5hhvzq

BigDataStack - D2.3 Requirements & State of the Art Analysis – III

Orlando Avila-García, Paula Ta-Shma, Yosef Moatti, Mauricio Fadel, Bin Chen, Ismael Cuadrado, Ana Belén González, Bernat Quesada, Alberto Soler, Stathis Plitsos, Anestis Sidiropoulos, Amaryllis Raouzaiou (+16 others)
2020 Zenodo  
This is complemented with a bottom-up approach aiming to identify, collect, and analyse the rest of the stakeholder requirements as well as technical requirements from the BigDataStack technology.  ...  In the requirements analysis presented in this document, a top-down approach is taken with respect to the user requirements, which have been collected through the BigDataStack use case providers.  ...  define the synergies between characteristics of data On the one hand, Reinforcement Learning (RL) -a machine learning technique in which an agent can interact and learn from the environment in which it  ... 
doi:10.5281/zenodo.4004169 fatcat:x3x2ug47sjbxpovtsbf65bzzve

A scalable architecture for extracting, aligning, linking, and visualizing multi-Int data

Craig A. Knoblock, Pedro Szekely, Barbara D. Broome, Timothy P. Hanratty, David L. Hall, James Llinas
2015 Next-Generation Analyst III  
DIG builds on our Karma data integration toolkit, which makes it easy to rapidly integrate structured data from a variety of sources, including databases, spreadsheets, XML, JSON, and Web services.  ...  The ability to integrate Web services allows Karma to pull in live data from the various social media sites, such as Twitter, Instagram, and OpenStreetMaps.  ...  ACKNOWLEDGMENTS This research is supported in part by the Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL) under contract number FA8750-14-C-0240, and in part  ... 
doi:10.1117/12.2177119 fatcat:onqbj5a7rzaypdzkmzppzmh2ia

Hardware Developments Iii

Alan Ó Cais, Liang Liang, Jony Castagna
2018 Zenodo  
platforms; and, - detailed output from direct face-to-face session between the project endusers, developers and hardware vendors.  ...  Update on "Hardware Developments II" (Deliverable 7.3: https://doi.org/10.5281/zenodo.1207613) which covers: - Report on hardware developments that will affect the scientific areas of interest to E-CAM  ...  In Fig. 2 is a topology graph of the NVSwitch connected to 16 GPUs.  ... 
doi:10.5281/zenodo.1304087 fatcat:itkihkoikvas5ajgxzqyswsez4

From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics* [article]

Gabriel Spadon, Jose F. Rodrigues-Jr
2022 arXiv   pre-print
Graphs have often been used to answer questions about the interaction between real-world entities by taking advantage of their capacity to represent complex topologies.  ...  , and patient monitoring in intensive care units; (ii) a machine-learning methodology for analyzing and predicting links in the scope of human mobility between all the cities of Brazil; and, (iii) techniques  ...  Acknowledgments The thesis work and the products derived from it were supported (directly or indirectly) by the Coordenac ¸ão de Aperfeic ¸oamento de Pessoal de Nível Superior -Brazil (CAPES) -Finance  ... 
arXiv:2206.01176v1 fatcat:cfbwb7ew7vg2ze4gvm2tutks6m

The Power of Graph Convolutional Networks to Distinguish Random Graph Models: Short Version [article]

Abram Magner and Mayank Baranwal and Alfred O. Hero III
2020 arXiv   pre-print
To prove our results, we exploit a connection to random walks on graphs.  ...  Graph convolutional networks (GCNs) are a widely used method for graph representation learning.  ...  This research was partially supported by grants from ARO W911NF-19-1026, ARO W911NF-15-1-0479, and ARO W911NF-14-1-0359 and the Blue Sky Initiative from the College of Engineering at the University of  ... 
arXiv:2002.05678v1 fatcat:lavqp6bznregxd5pzodgqewigi

Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks [article]

Sai Narayanan, Akhilesh Ramachandran, Sathyanarayanan N. Aakur, Arunkumar Bagavathi
2020 arXiv   pre-print
Advancements of data analytics and machine learning and applications over metagenome sequencing are setting trends on several applications.  ...  With experiments conducted on two different simulated datasets, we show that networks-based machine learning approaches can detect pathogen signature with up to 89.7% accuracy.  ...  ACKNOWLEDGMENT This research was supported in part by the US Department of Agriculture (USDA) grants AP20VSD and B000C011.  ... 
arXiv:2007.12791v1 fatcat:bf7yd75mdrhf5hsc7f6asqx3zu

III Advanced Topics [chapter]

2016 Soft Computing Applications in Sensor Networks  
Acknowledgments This work has been supported in part by AGAUR Project under Grant  ...  ., packet loss, delay, and throughput) depend on the requirements of the executed application. (iii) Data Packet Size: The sizes of data packets usually depend on the sensor type and the application.  ...  In case of data intensive fault detection mechanisms, various machine learning based approaches, such as support vector machine and Bayesian approaches, are applied [9] .  ... 
doi:10.1201/9781315372020-13 fatcat:ze5voikcfbfi5lubz5t5oismua

Message-Passing Neural Networks Learn Little's Law

Krzysztof Rusek, Piotr Cholda
2019 IEEE Communications Letters  
In consequence, the proposed solution makes it possible to effectively apply methods elaborated in the field of machine learning in communications.  ...  The paper presents a solution to the problem of universal representation of graphs exemplifying communication network topologies with the help of neural networks.  ...  CONCLUSIONS AND FUTURE WORK The large variety of COMNET sizes and topologies makes it nontrivial to construct a general machine learning model of sparse graph structured data.  ... 
doi:10.1109/lcomm.2018.2886259 fatcat:g765mqkzpvhatih3bnzgghutmi

Optimization for Data-Driven Learning and Control

Usman A. Khan, Waheed U. Bajwa, Angelia Nedic, Michael G. Rabbat, Ali H. Sayed
2020 Proceedings of the IEEE  
learning from streams of data samples.  ...  Real-world examples are presented on smart power systems, robotics, machine learning, and data analytics, highlighting domainspecific challenges and solutions.  ... 
doi:10.1109/jproc.2020.3031225 fatcat:6ibimo2s2zgepbyeya2fjq7flu

Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level

Ziyu Zhu, Du Lei, Kun Qin, Xueling Suo, Wenbin Li, Lingjiang Li, Melissa P. DelBello, John A. Sweeney, Qiyong Gong
2021 Diagnostics  
To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate  ...  Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients.  ...  Acknowledgments: We thank all participants in Wenchuan for their time and willingness to take part in this study. Conflicts of Interest: Sweeney consults to VeriSci.  ... 
doi:10.3390/diagnostics11081416 fatcat:pa4qe77fujc5tpgtmyhdu6oggm

Dynamic Coverage Verification in Mobile Sensor Networks Via Switched Higher Order Laplacians

A. Muhammad, A. Jadbabaie
2007 Robotics: Science and Systems III  
These switched systems describe the flow of discrete differential forms on time-evolving simplicial complexes.  ...  The enabling mathematical technique for this result is the theory of higher order Laplacian operators, which is a generalization of the graph Laplacian used in spectral graph theory and continuous-time  ...  Combinatorial Laplacians The graph Laplacian [28] has various applications in image segmentation, graph embedding, dimensionality reduction for large data sets, machine learning , and more recently in  ... 
doi:10.15607/rss.2007.iii.039 dblp:conf/rss/MuhammadJ07 fatcat:q336zpvsvvdana6x4thm4vinza
« Previous Showing results 1 — 15 out of 9,716 results