Finding network communities (i.e. community detection) is a famous topic in network science. By far, many widely concerned community detection approaches are designed by using evolutionary computation methods. Recent ...
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ISBN:
(数字)9781728158570
ISBN:
(纸本)9781728158587
Finding network communities (i.e. community detection) is a famous topic in network science. By far, many widely concerned community detection approaches are designed by using evolutionary computation methods. Recent years, a new evolutionary algorithm called state transition algorithm (STA) was created and developed. In our previous work, a population-based discrete STA (MDSTA) has been put forwarded to settle network community detection task. Similar to most population-based evolutionary algorithms, MDSTA has a relatively complex algorithm structure which may limit the application of the algorithm. To address this problem, a backtracking-based discrete STA (BDSTA) is designed in this study. BDSTA is an individual-based method, and two kinds of substitute operators based on label-based representation strategy and locus-based representation strategy are used in BDSTA for global search and local search, respectively. Owing to that the individual-based algorithms often fall into a stagnation solution, we employ a backtracking search strategy in the global search procedure. Finally, five real-world networks and the extended GN artificial networks are used to test BDSTA and some state-of-art algorithms. Experimental results prove that BDSTA often get high-quality community partitions and it is more efficient than these state-of-art algorithms.
Many real-world networks are described by both connectivity information and features for every node. To better model and understand these networks, we present structure preserving metric learning (SPML), an algorithm ...
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ISBN:
(纸本)9781618395993
Many real-world networks are described by both connectivity information and features for every node. To better model and understand these networks, we present structure preserving metric learning (SPML), an algorithm for learning a Mahalanobis distance metric from a network such that the learned distances are tied to the inherent connectivity structure of the network. Like the graph embedding algorithm structure preserving embedding, SPML learns a metric which is structure preserving, meaning a connectivity algorithm such as k-nearest neighbors will yield the correct connectivity when applied using the distances from the learned metric. We show a variety of synthetic and real-world experiments where SPML predicts link patterns from node features more accurately than standard techniques. We further demonstrate a method for optimizing SPML based on stochastic gradient descent which removes the running-time dependency on the size of the network and allows the method to easily scale to networks of thousands of nodes and millions of edges.
With the development of Internet of Things technology,more and more devices are connected to the Internet,including not only traditional computers,mobile phones and other smart terminal devices,but also various sensor...
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With the development of Internet of Things technology,more and more devices are connected to the Internet,including not only traditional computers,mobile phones and other smart terminal devices,but also various sensor *** sensor devices can collect a variety of environmental information and physical quantities,such as temperature,humidity,air pressure,light intensity,vibration,*** data have the characteristics of real-time,scale and diversity,and need to be processed and analyzed by appropriate *** the basis of previous studies,this project summarized the application of various machine learning algorithms in device state detection,compared the differences of various machine learning algorithms in sensor device detection and made comparative analysis,calculated the evaluation parameters of MSE,RMSE,MAE,MAPE,R and other aspects of the machine learning regression *** the effects of various regression models for better monitoring and prediction of equipment *** the analysis of a large number of historical data,different equipment state models can be established,and these models can be used to monitor and predict the current equipment *** can effectively avoid production line downtime or other losses caused by equipment failures or *** the same time,through the in-depth analysis of historical data,we can find some potential problems and take corresponding measures to prevent *** project aims to summarize the application of various machine learning algorithms in device status detection,compare and contrast the differences of various machine learning algorithms in sensor device detection,realize efficient processing and analysis of sensor data,calculate MSE,RMSE,MAE,MAPE,R and other evaluation parameters,and evaluate and compare each *** provide more accurate,reliable and efficient equipment condition monitoring and forecasting services for enterprises and individuals.
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