discovery method
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2022 ◽  
Vol 16 (2) ◽  
pp. 1-18
Author(s):  
Xueyuan Wang ◽  
Hongpo Zhang ◽  
Zongmin Wang ◽  
Yaqiong Qiao ◽  
Jiangtao Ma ◽  
...  

Cross-network anchor link discovery is an important research problem and has many applications in heterogeneous social network. Existing schemes of cross-network anchor link discovery can provide reasonable link discovery results, but the quality of these results depends on the features of the platform. Therefore, there is no theoretical guarantee to the stability. This article employs user embedding feature to model the relationship between cross-platform accounts, that is, the more similar the user embedding features are, the more similar the two accounts are. The similarity of user embedding features is determined by the distance of the user features in the latent space. Based on the user embedding features, this article proposes an embedding representation-based method Con&Net(Content and Network) to solve cross-network anchor link discovery problem. Con&Net combines the user’s profile features, user-generated content (UGC) features, and user’s social structure features to measure the similarity of two user accounts. Con&Net first trains the user’s profile features to get profile embedding. Then it trains the network structure of the nodes to get structure embedding. It connects the two features through vector concatenating, and calculates the cosine similarity of the vector based on the embedding vector. This cosine similarity is used to measure the similarity of the user accounts. Finally, Con&Net predicts the link based on similarity for account pairs across the two networks. A large number of experiments in Sina Weibo and Twitter networks show that the proposed method Con&Net is better than state-of-the-art method. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve predicted by the anchor link is 11% higher than the baseline method, and Precision@30 is 25% higher than the baseline method.


2022 ◽  
Vol 9 ◽  
Author(s):  
Yanli Zou ◽  
Haoqian Li

Based on the community discovery method in complex network theory, a power grid partition method considering generator nodes and network weightings is proposed. Firstly, the weighted network model of a power system is established, an improved Fast-Newman hierarchical algorithm and a weighted modular Q function index are introduced, and the partitioning algorithm process is practically improved combined with the characteristics of the actual power grid. Then, the partition results of several IEEE test systems with the improved algorithm and with the Fast-Newman algorithm are compared to demonstrate its effectiveness and correctness. Subsequently, on the basis of subnet partition, two kinds of network attack strategies are proposed. One is attacking the maximum degree node of each subnet, and the other is attacking the maximum betweenness node of each subnet. Meanwhile, considering the two traditional intentional attack strategies, that is, attacking the maximum degree nodes or attacking the maximum betweenness nodes of the whole network, the cascading fault survivability of different types of networks under four attack strategies is simulated and analyzed. It was found that the proposed two attack strategies based on subnet partition are better than the two traditional intentional attack strategies.


2022 ◽  
Vol 21 (1) ◽  
pp. 33-59
Author(s):  
Ryeongkyung Yoon ◽  
Harish S. Bhat ◽  
Braxton Osting

2021 ◽  
Vol 9 (4) ◽  
pp. 501-513
Author(s):  
Kunalfi Reza Luthfiana ◽  
Abdurrahman Abdurrahman ◽  
Rezi Ariawan, ◽  
Dedek Andrian

The purpose of this research is to produce a mathematics learning tool in the form of the lesson plan and student's worksheet using the guided discovery method on the circle material of class VIII junior high school which has been tested valid. This development refers to the Research and Development (R&D) model which includes 10 stages, namely potential and problems, data collection, product design, design validation, design revisions, product trials, design revisions, usage trials, product revisions, and mass production. Modified according to the needs of this research into 6 stages, namely potential and problems, data collection, product design, design validation, design revision, and final product. The data analysis technique used is the descriptive analysis technique. The validation sheet was used as a data collection instrument in this research. The validation sheet is filled out by 3 validators consisting of 2 lecturers of the mathematics education study program FKIP UIR and 1 mathematics teacher. Based on the results of the lesson plan validation, a percentage of 85.6% was obtained with very valid criteria, while the students’ worksheet validation results were 82.6% with fairly valid criteria. This research concludes that mathematics learning tools have been obtained using the guided discovery method in class VII junior high school on the circle material which has been tested valid.


2021 ◽  
Author(s):  
Rebecca Lindsey ◽  
Nir Goldman ◽  
Laurence Fried ◽  
Sorin Bastea

There is significant interest in establishing a capability for tailored synthesis of next-generation carbon-based nanomaterials due to their broad range of applications and high degree of tunability. High pressure (e.g. shockwave-driven) synthesis holds promise as an effective discovery method, but experimental challenges preclude elucidating the processes governing nanocarbon production from carbon-rich precursors that could otherwise guide efforts through the prohibitively expansive design space. Here we report findings from large scale atomistically-resolved simulations of carbon condensation from C/O mixtures subjected to extreme pressures and temperatures, made possible by machine-learned reactive interatomic potentials. We find that liquid nanocarbon formation follows classical growth kinetics driven by Ostwald ripening (i.e. growth of large clusters at the expense of shrinking small ones) and obeys dynamical scaling in a process mediated by carbon chemistry in the surrounding reactive fluid. The results provide direct insight into carbon condensation in a representative system and pave the way for its exploration in higher complexity organic materials. They also suggest that simulations using machine-learned interatomic potentials could eventually be employed as in-silico design tools for new nanomaterials.


2021 ◽  
Author(s):  
MUHAMMAD QAMARUZZAMAN

The aim of this action research is increasing the activity and learning through the application of sharing knowledge actively learning model with guided discovery method on statistics courses tutorial at open university (UT) study group in Banjarbaru. The subject of this study were Students of UT at, Pendas S-1 class A of PGSD, who took an educational statistics courses (PEMA - 4210) semesters VII, 2011.1 registered.Action research activities divided students into small groups, and each time of the meeting, the tutor provides the Group Worksheet (LKK), which contains materials summary to be studied by each group. Tutor only facilitated groups who had barriers, with reminded to reread LKK, the tutor was not allowed to give a direct answer.During the research activities carried out by using LKK, it could be concluded that students really learned in groups, and they also helped each other between a group with other groups to discuss the material that was less mastered by them.The results showed the use of sharing knowledge actively learning model with guided discovery method, capable to provide assistance students in learning statistical material, and outcomes indicated that PGSD Pendas S-1 class A, who took education statistics (Pema 4210) semesters VII, period 2011.1 were graduated 100 percent. (*)


Author(s):  
Artur Mrowca ◽  
Florian Gyrock ◽  
Stephan Günnemann

AbstractMany systems can be expressed as multivariate state sequences (MSS) in terms of entities and their states with evolving dependencies over time. In order to interpret the temporal dynamics in such data, it is essential to capture relationships between entities and their changes in state and dependence over time under uncertainty. Existing probabilistic models do not explicitly model the evolution of causality between dependent state sequences and mostly result in complex structures when representing complete causal dependencies between random variables. To solve this, Temporal State Change Bayesian Networks (TSCBN) are introduced to effectively model interval relations of MSSs under evolving uncertainty. Our model outperforms competing approaches in terms of parameter complexity and expressiveness. Further, an efficient structure discovery method for TSCBNs is presented, that improves classical approaches by exploiting temporal knowledge and multiple parameter estimation approaches for TSCBNs are introduced. Those are expectation maximization, variational inference and a sampling based maximum likelihood estimation that allow to learn parameters from partially observed MSSs. Lastly, we demonstrate how TSCBNs allow to interpret and infer patterns of captured sequences for specification mining in automotive.


2021 ◽  
Vol 33 (6) ◽  
pp. 1-12
Author(s):  
Kangning Shen ◽  
Rongrong Tu ◽  
Rongju Yao ◽  
Sifeng Wang ◽  
Ashish K. Luhach

With the continuous developments of real estates and the increasing personalization of people, more and more house owners are willing to search for and discover their preferred decorative art patterns via various house decoration cases sharing websites or platforms. Through browsing and analyzing existing house decoration cases on the Web, a new house owner can find out his or her interested decorative art patterns; however, the above decorative art pattern mining and discovery process is often time-consuming and boring due to the big volume of existing house decoration cases on the Web. Therefore, it is becoming a challenging task to develop a time-efficient decorative art pattern mining and discovery method based on the available house decoration cases provided by historical users. Considering this challenge, a novel LSH-based similar house owners clustering approach is proposed. A set of experiments are designed to validate the effectiveness and efficiency of our proposal.


2021 ◽  
Vol 33 (6) ◽  
pp. 0-0

With the continuous developments of real estates and the increasing personalization of people, more and more house owners are willing to search for and discover their preferred decorative art patterns via various house decoration cases sharing websites or platforms. Through browsing and analyzing existing house decoration cases on the Web, a new house owner can find out his or her interested decorative art patterns; however, the above decorative art pattern mining and discovery process is often time-consuming and boring due to the big volume of existing house decoration cases on the Web. Therefore, it is becoming a challenging task to develop a time-efficient decorative art pattern mining and discovery method based on the available house decoration cases provided by historical users. Considering this challenge, a novel LSH-based similar house owners clustering approach is proposed. A set of experiments are designed to validate the effectiveness and efficiency of our proposal.


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