Large-Scale Realistic Network Data Generation on a Budget

2018 ◽  
Author(s):  
Brian Ricks ◽  
Patrick Tague ◽  
Bhavani Thuraisingham
Keyword(s):  
Large Scale ◽  
Network Data ◽  
Data Generation

Incremental Community Detection on Large Complex Attributed Network

2021 ◽  
Vol 15 (6) ◽  
pp. 1-20
Author(s):  
Zhe Chen ◽  
Aixin Sun ◽  
Xiaokui Xiao
Keyword(s):  
Community Detection ◽  
Large Scale ◽  
Network Data ◽  
Topological Information ◽  
Community Membership ◽  
Attributed Network ◽  
Benchmark Datasets ◽  
Modularity Maximization ◽  
Large Scale Networks

Community detection on network data is a fundamental task, and has many applications in industry. Network data in industry can be very large, with incomplete and complex attributes, and more importantly, growing. This calls for a community detection technique that is able to handle both attribute and topological information on large scale networks, and also is incremental. In this article, we propose inc-AGGMMR, an incremental community detection framework that is able to effectively address the challenges that come from scalability, mixed attributes, incomplete values, and evolving of the network. Through construction of augmented graph, we map attributes into the network by introducing attribute centers and belongingness edges. The communities are then detected by modularity maximization. During this process, we adjust the weights of belongingness edges to balance the contribution between attribute and topological information to the detection of communities. The weight adjustment mechanism enables incremental updates of community membership of all vertices. We evaluate inc-AGGMMR on five benchmark datasets against eight strong baselines. We also provide a case study to incrementally detect communities on a PayPal payment network which contains users with transactions. The results demonstrate inc-AGGMMR’s effectiveness and practicability.

Proposal for the generation of network data for pedestrians in South Korea using large-scale maps

2015 ◽  
Vol 19 (5) ◽  
pp. 1483-1491 ◽  
Author(s):  
Jiyoung Kim ◽  
Yong Huh ◽  
Jung Ok Kim ◽  
Jae Bin Lee
Keyword(s):  
South Korea ◽  
Large Scale ◽  
Network Data

scholarly journals Microwave Dielectric Property Retrieval from Open-Ended Coaxial Probe Response with Deep Learning

2021 ◽  
Author(s):  
Cemanur Aydinalp ◽  
Sulayman Joof ◽  
Mehmet Nuri Akinci ◽  
Ibrahim Akduman ◽  
Tuba Yilmaz
Keyword(s):  
Deep Learning ◽  
Dielectric Property ◽  
Large Scale ◽  
Real Data ◽  
Learning Model ◽  
Data Generation ◽  
Retrieval Technique ◽  
Design Flexibility ◽  
A New Technique ◽  
Deep Learning Model

In the manuscript, we propose a new technique for determination of Debye parameters, representing the dielectric properties of materials, from the reflection coefficient response of open-ended coaxial probes. The method retrieves the Debye parameters using a deep learning model designed through utilization of numerically generated data. Unlike real data, using synthetically generated input and output data for training purposes provides representation of a wide variety of materials with rapid data generation. Furthermore, the proposed method provides design flexibility and can be applied to any desired probe with intended dimensions and material. Next, we experimentally verified the designed deep learning model using measured reflection coefficients when the probe was terminated with five different standard liquids, four mixtures,and a gel-like material.and compared the results with the literature. Obtained mean percent relative error was ranging from 1.21±0.06 to 10.89±0.08. Our work also presents a large-scale statistical verification of the proposed dielectric property retrieval technique.

Biogc: A novel framework for biological network classification via machine learning

2021 ◽  
Vol 25 (5) ◽  
pp. 1153-1168
Author(s):  
Bentian Li ◽  
Dechang Pi ◽  
Yunxia Lin ◽  
Izhar Ahmed Khan
Keyword(s):  
Large Scale ◽  
Biological Network ◽  
Structural Information ◽  
Kernel Method ◽  
Small Scale ◽  
Network Data ◽  
Graph Classification ◽  
Accuracy Rate ◽  
Proposed Model ◽  
Complex Structural

Biological network classification is an eminently challenging task in the domain of data mining since the networks contain complex structural information. Conventional biochemical experimental methods and the existing intelligent algorithms still suffer from some limitations such as immense experimental cost and inferior accuracy rate. To solve these problems, in this paper, we propose a novel framework for Biological graph classification named Biogc, which is specifically developed to predict the label of both small-scale and large-scale biological network data flexibly and efficiently. Our framework firstly presents a simplified graph kernel method to capture the structural information of each graph. Then, the obtained informative features are adopted to train different scale biological network data-oriented classifiers to construct the prediction model. Extensive experiments on five benchmark biological network datasets on graph classification task show that the proposed model Biogc outperforms the state-of-the-art methods with an accuracy rate of 98.90% on a larger dataset and 99.32% on a smaller dataset.

Affordances of Data Science in Agriculture, Manufacturing, and Education

Web Services ◽  
2019 ◽  
pp. 953-978
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

Affordances of Data Science in Agriculture, Manufacturing, and Education

2017 ◽  
pp. 14-40 ◽  
Author(s):  
Krishnan Umachandran ◽  
Debra Sharon Ferdinand-James
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Data Generation ◽  
Large Scale Data ◽  
Big Data Applications ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Text Images ◽  
Scale Data

Continued technological advancements of the 21st Century afford massive data generation in sectors of our economy to include the domains of agriculture, manufacturing, and education. However, harnessing such large-scale data, using modern technologies for effective decision-making appears to be an evolving science that requires knowledge of Big Data management and analytics. Big data in agriculture, manufacturing, and education are varied such as voluminous text, images, and graphs. Applying Big data science techniques (e.g., functional algorithms) for extracting intelligence data affords decision markers quick response to productivity, market resilience, and student enrollment challenges in today's unpredictable markets. This chapter serves to employ data science for potential solutions to Big Data applications in the sectors of agriculture, manufacturing and education to a lesser extent, using modern technological tools such as Hadoop, Hive, Sqoop, and MongoDB.

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