scholarly journals CASS: A distributed network clustering algorithm based on structure similarity for large-scale network

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0203670 ◽  
Author(s):  
Jungrim Kim ◽  
Mincheol Shin ◽  
Jeongwoo Kim ◽  
Chihyun Park ◽  
Sujin Lee ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Network Clustering ◽  
Distributed Network ◽  
Large Scale Network ◽  
Scale Network ◽  
Structure Similarity

scholarly journals A Method for Detecting Large-scale Network Anomaly Behavior

2018 ◽  
Vol 17 ◽  
pp. 01012
Author(s):  
Huimin Hu ◽  
Wenping Ma ◽  
Wei Luo
Keyword(s):  
Anomaly Detection ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Model Identification ◽  
Abnormal Behavior ◽  
Large Scale Network ◽  
Clustering Model ◽  
Detection Technology ◽  
Scale Network

A clustering model identification method based on the statistics has been proposed to improve the ability to detect scale anomaly behavior of the traditional anomaly detection technology. By analyzing the distribution of the distance between each clustering objects and clustering center to identify anomaly behavior. It ensures scale abnormal behavior identification while keeping the processing mechanism of the traditional anomaly detection technology for isolation, and breaking through the limitation of the traditional anomaly detection method assumes that abnormal data is the isolation. In order to improve the precision of clustering, we correct the Euclidean distance with the entropy value method to weight the attribute of the data, it optimizes the similarity evaluating electric of the nearest neighbor clustering algorithm, and simulated. Experimental results show that the statistical method and the improved clustering method is more efficient and self-adaptive.

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network

Attention Biases Visual Activity in Visual Short-term Memory

2014 ◽  
Vol 26 (7) ◽  
pp. 1377-1389 ◽  
Author(s):  
Bo-Cheng Kuo ◽  
Mark G. Stokes ◽  
Alexandra M. Murray ◽  
Anna Christina Nobre
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Control Mechanisms ◽  
Top Down ◽  
Short Term ◽  
Visual Activity ◽  
Visual Short Term Memory ◽  
Large Scale Network ◽  
Visual Areas ◽  
Scale Network

In the current study, we tested whether representations in visual STM (VSTM) can be biased via top–down attentional modulation of visual activity in retinotopically specific locations. We manipulated attention using retrospective cues presented during the retention interval of a VSTM task. Retrospective cues triggered activity in a large-scale network implicated in attentional control and led to retinotopically specific modulation of activity in early visual areas V1–V4. Importantly, shifts of attention during VSTM maintenance were associated with changes in functional connectivity between pFC and retinotopic regions within V4. Our findings provide new insights into top–down control mechanisms that modulate VSTM representations for flexible and goal-directed maintenance of the most relevant memoranda.

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