scholarly journals Study on Power Grid Partition and Attack Strategies Based on Complex Networks

2022 ◽  
Vol 9 ◽  
Author(s):  
Yanli Zou ◽  
Haoqian Li
Keyword(s):  
Maximum Degree ◽  
Power Grid ◽  
Community Discovery ◽  
Network Attack ◽  
Complex Network Theory ◽  
Grid Partition ◽  
Intentional Attack ◽  
Partition Method ◽  
Discovery Method ◽  
Better Than

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.

scholarly journals Study on Power Grid Partition Method for Wide-area Relaying Protection

2013 ◽  
Vol 19 (3) ◽  
Author(s):  
Zhenxing Li ◽  
Xianggen Yin ◽  
Zhe Zhang ◽  
Yuxue Wang
Keyword(s):  
Power Grid ◽  
Wide Area ◽  
Grid Partition ◽  
Partition Method

Community Discovery Method in Networks Based on Topological Potential

2009 ◽  
Vol 20 (8) ◽  
pp. 2241-2254 ◽  
Author(s):  
Wen-Yan GAN ◽  
Nan HE ◽  
De-Yi LI ◽  
Jian-Min WANG
Keyword(s):  
Community Discovery ◽  
Discovery Method

scholarly journals Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jibing Wu ◽  
Lianfei Yu ◽  
Qun Zhang ◽  
Peiteng Shi ◽  
Lihua Liu ◽  
...  
Keyword(s):  
Real World ◽  
Tensor Decomposition ◽  
Information Networks ◽  
Community Discovery ◽  
Star Network ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
General Network ◽  
Real World Datasets ◽  
Discovery Method

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

scholarly journals Overlapping Community Discovery Method Based on Two Expansions of Seeds

Symmetry ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 18
Author(s):  
Yan Li ◽  
Jing He ◽  
Youxi Wu ◽  
Rongjie Lv
Keyword(s):  
Symmetric Matrix ◽  
Fitness Function ◽  
Local Maximum ◽  
Community Discovery ◽  
Network Nodes ◽  
Overlapping Community ◽  
The Common ◽  
New Research ◽  
Discovery Method ◽  
Discovery Algorithms

The real world can be characterized as a complex network sto in symmetric matrix. Community discovery (or community detection) can effectively reveal the common features of network groups. The communities are overlapping since, in fact, one thing often belongs to multiple categories. Hence, overlapping community discovery has become a new research hotspot. Since the results of the existing community discovery algorithms are not robust enough, this paper proposes an effective algorithm, named Two Expansions of Seeds (TES). TES adopts the topological feature of network nodes to find the local maximum nodes as the seeds which are based on the gravitational degree, which makes the community discovery robust. Then, the seeds are expanded by the greedy strategy based on the fitness function, and the community cleaning strategy is employed to avoid the nodes with negative fitness so as to improve the accuracy of community discovery. After that, the gravitational degree is used to expand the communities for the second time. Thus, all nodes in the network belong to at least one community. Finally, we calculate the distance between the communities and merge similar communities to obtain a less- undant community structure. Experimental results demonstrate that our algorithm outperforms other state-of-the-art algorithms.

scholarly journals Pengaruh Metode Discovery terhadap Prestasi Analogi Matematis Peserta Didik SMKN 1 Sakra

FONDATIA ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 74-88
Author(s):  
Sulhiah Sulhiah
Keyword(s):  
Sampling Technique ◽  
High Achievers ◽  
Control Group ◽  
Low Grade ◽  
Mathematical Generalization ◽  
Method Class ◽  
Discovery Method ◽  
Experimental Group ◽  
Cluster Random Sampling ◽  
Better Than

The research uses factorial design, aims to find out the effect of discovery method on the analogy achievement and mathematical generalization of students of SMKN 1 Sakra. The population of this study was all students of SMKN 1 Sakra, and the research sample was taken using the Cluster Random Sampling technique, namely by looking for class XII students, two classes to be sampled. This study consisted of two groups, namely learning the discovery method and the expository method. Class XII.A was made as an experimental group, while class XII.B was made as a control group. Each class consists of 36 students who are divided into three different abilities of students, namely high achievers, medium achievers and low grade students. Research data were collected through tests, questionnaires, observations and interviews. The results of this study are an increase in the ability of mathematical analogies of students who obtain learning using the discovery method better than students who obtain the expository method.

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