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检索条件"主题词=Louvain Algorithm"

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Geographical Entity Community Mining Based on Spatial and Semantic Association 19

Geographical Entity Community Mining Based on Spatial and Se...

引用

3rd International Conference on Computer Science and Application Engineering (CSAE)

作者： Yan, Mengyu Jing, Ning Zhong, Zhinong Wu, Ye Natl Univ Def Technol Coll Elect Sci & Technol Changsha Peoples R China

ISBN: (纸本)9781450362948

The relevance of geographic entities has always been the focus of research on geographic information retrieval, geographic knowledge graph and recommendation systems. Traditional research methods, which use spatial or semantic similarity to calculate the correlation between regions, have certain one-sidedness and limitations. The network topology can clearly represent the relationship between entities. However, semantic relationships are difficult to define, so there are few cases where network-related algorithms are used to solve the relevance of geographic entities. With the development of the Internet, web pages provide people with a huge amount of information, and geographical names as a key element are often ignored by researchers, and the rich semantic information contained in it needs further research. This study attempts to explore the geographic entity relevance of integrated semantics and spatial factors based on textual data from a network perspective. Based on the community mining algorithm, the experiment studies the aggregation characteristics of geographic entities and can find areas that are close to each other and tightly related, which is more satisfied with people's common sense.

关键词： Geographic entities Spatial similarity Semantic similarity Community mining louvain algorithm

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Analysis of Road Networks Using the Louvian Community Detection algorithm 8th

Analysis of Road Networks Using the Louvian Community Detect...

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8th International Conference on Soft Computing for Problem Solving (SocProS)

作者： Rashmi, R. Champawat, Shivani Teja, G. Varun Lavanya, K. VIT Univ Sch Comp Sci & Engn Vellore Tamil Nadu India

ISBN: (纸本)9789811501845;9789811501838

In today's world, the population is increasing rapidly that make people move in and out of the countries/states for various reasons. With the evolution of technology, there is an advancement in transportation, and traveling across places has become easier than before. Now, we have various means of transport to move around the world. Though the most preferred way to travel is the roadways, there are roads built that connect cities and states together. But we also have a few disadvantages like increase in the vehicle registrations, amount of pollution, and the number of road accidents. This paper addresses an issue related to the traffic congestion caused due to the vehicles and analyzes the traffic at a particular area based on the threshold using the louvain community detection algorithm. The louvain algorithm is a graph algorithm used to detect the communities within a particular region and then form clusters. The algorithm is implemented on a US road network in Neo4j to detect the traffic and provide an alternative route for conveyance.

关键词： Clusters Communities louvain algorithm Neo4j Road network

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Bitcoin user analysis based on address clustering and community discovery algorithm 6

Bitcoin user analysis based on address clustering and commun...

引用

6th International Conference on Blockchain Technology and Applications (ICBTA)

作者： Li Jia-Xin Yu Tian-Ci Wang Yan-Nian Sun Yue Zhengzhou Univ Sch Math & Stat Zhengzhou Henan Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Henan Peoples R China

ISBN: (纸本)9798400708671

Bitcoin's anonymity greatly protects users' privacy, but it also makes regulation difficult. In Bitcoin, a random number generates a public-private key pair, the public key generates an address, and the private key is used for digital signatures. Users can generate multiple pairs of public and private keys to trade with multiple bitcoin addresses. Discovering the relationships between these addresses and clustering the addresses of individual users helps infer the identity of the addresses. By analyzing the association of addresses in UTXO, it is found that multiple input addresses of a transaction are controlled by the same user, and thus the bitcoin addresses can be clustered. The transactions between the user data obtained after clustering are communality, so the louvain algorithm is further used to analyze the relationship between users, the visual results are used to present the association between users, and the impact of the number of users on the algorithm results is analyzed. Finally, the Leiden algorithm proposed to solve the problem that louvain algorithm may have poor connectivity or even disconnection between communities is used to discover the community of the clustered user data. Compare the results of Leiden algorithm and louvain algorithm and analyze the difference between the two results.

关键词： Bitcoin address clustering community discovery algorithm louvain algorithm Leiden algorithm user analysis

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Analysis of ResearchGate, a Community Detection Approach 6

Analysis of ResearchGate, a Community Detection Approach

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6th International Conference on Web Research (ICWR)

作者： Heydari, Mohammad Teimourpour, Babak Tarbiat Hodares Univ Dept Informat Technol Engn Sch Ind & Syst Engn Tehran Iran

ISBN: (数字)9781728110516

ISBN: (纸本)9781728110516

We are living in the data age. Communications over scientific networks creates new opportunities for researchers who aim to discover the hidden pattern in these huge repositories. This study utilizes network science to create collaboration network of Iranian Scientific Institutions. A modularity-based approach applied to find network communities. To reach a big picture of science production flow, analysis of the collaboration network is crucial. Our results demonstrated that geographic location closeness and ethnic attributes has important roles in academic collaboration network establishment. Besides, it shows that famous scientific centers in the capital city of Iran, Tehran has strong influence on the production flow of scientific activities. These academic papers are mostly viewed and downloaded from the United State of America, China, India, and Iran. The motivation of this research is that by discovering hidden communities in the network and finding the structure of intuitions communications, we can identify each scientific center research potential separately and clear mutual scientific fields. Therefore, an efficient strategic program can be design, develop and test to keep scientific institutions in progress path and navigate their research goals into a straight useful roadmap to identify and fill the unknown gaps.

关键词： Social Network Analysis Scientific Network louvain algorithm Network Centrality

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Research on the Nodes Importance in Complex Networks Based on Community Partition 19

Research on the Nodes Importance in Complex Networks Based o...

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2nd International Conference on Big Data Technologies (ICBDT) / 3rd International Conference on Business Information Systems Workshop (ICBIS)

作者： Chen, Feng Huang, Yajing Beijing Forestry Univ Sch Sci China 35 Tsinghua East Rd Beijing Peoples R China

ISBN: (纸本)9781450371926

In complex network analysis, the importance of nodes has always been one of the main research fields of graph theory and the most common calculation method is the PageRank algorithm. The existing research is mainly based on the whole network level to rank all nodes, which will bring a huge amount of computation when the network is quite large. In fact, a node is more likely to be affected by the nodes that are closely connected to it, rather than by the nodes that have the highest importance in the entire network. So, it is necessary to decompose the networks into communities, whose main feature is that the nodes within a community are closely connected, while the nodes between different communities are sparsely connected. In this paper, the classical louvain algorithm will be applied to achieve community partition, for the further study of nodes importance. Firstly, the statistical characteristics of PageRank values of nodes between different communities are studied for four different types of networks: regular network, random network, small-world network and scale-free network. Secondly, a real enterprise investment network is studied based on a large number of Chinese enterprise investment data. The nodes in the same community are extracted to form a sub-network and a new set of PageRank values will be calculated for these nodes. Comparison results of two sets of PageRank values of the same group of nodes show that the rank of nodes importance in the whole network is highly consistent with that in the sub-network. For the network model established by big data, community partition method can greatly reduce the computational load of nodes important and simplify the research process. The conclusion of this paper is applicable to large-scale complex networks in reality, and has vital practical significance.

关键词： Complex network Community partition Node importance louvain algorithm PageRank algorithm

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Research on Hierarchical Division of Topology in Communication Network

Research on Hierarchical Division of Topology in Communicati...

引用

IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Liu, Hongyu Liang, Mangui Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Techno Beijing Peoples R China

ISBN: (纸本)9781728117089

The routing of large-scale communication network is a NP problem. There is a consensus in the industry that hierarchical division of network can solve this problem and ensure the scalability of the network system. Vector Network(VN), as a new network architecture, requires the network to meet the requirements of QoS, reliability and security under scalable condition. This paper studies the division method of multi-level communication network, which is the basic technology for the excellent performance of vector network. Based on the louvain community division algorithm, this paper proposes an improved louvain algorithm. This algorithm can effectively divide the network into hierarchical structure. And the experimental results show that the new algorithm improves the execution efficiency and achieves good results.

关键词： communication network hierarchical division Vector Network louvain algorithm

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Comparison of Community Detection algorithms on Contracts Networks 40

Comparison of Community Detection Algorithms on Contracts Ne...

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40th Chinese Control Conference (CCC)

作者： Xiong, Peichen Ping, Wei Chen, Hao Zhejiang Univ Finance & Econ Dongfang Coll Informat Inst Haining 314408 Peoples R China Ningbo Univ Sch Informat Engn Coll Sci & Technol Ningbo 315300 Peoples R China Ningbo Univ Ningbo 315211 Peoples R China

ISBN: (纸本)9789881563804

Community discovery is an important research content in complex network analysis and an important tool for community analysis. It divides nodes into clusters according to the similarity of nodes or similarities in structure. This paper takes the EU public procurement contract network from 2008 to 2016 as an example, selects 6 countries with different data sizes, Austria, Cyprus, Germany, France, Hungary and Italy, analyzes the characteristics of the contract network and evaluates different community classification algorithms Performance. Taking the running speed and modularity as the evaluation criteria, comparing the louvain algorithm and Girvan Newman algorithm the speed and accuracy of these 2 methods on different scale data sets, analyze the most suitable contract network community classification algorithm. Through analysis, the contract network is a sparse bipartite network, louvain algorithm is a more suitable community detection method than Girvan Newman algorithm for contract networks. It has a fast running speed and high accuracy.

关键词： Social Network Analysis Community Detection Girvan Newman algorithm louvain algorithm

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Discovering Combination Patterns of Traditional Chinese Medicine for the Treatment of Gouty Arthritis with Renal Dysfunction 8th

Discovering Combination Patterns of Traditional Chinese Medi...

引用

8th Annual China Conference on Health Information Processing (CHIP)

作者： Chen, Wenying Qiu, Weihan Chen, Tao Xu, Yiyong Zhang, Xiaolin Chen, Xiumin Huang, Runyue Wang, Maojie Guangzhou Univ Chinese Med Guangdong Prov Hosp Chinese Med Affiliated Hosp 2 Guangzhou Peoples R China South China Normal Univ Sch Comp Sci Guangzhou Peoples R China Guangdong Prov Key Lab Clin Res Tradit Chinese Me Guangzhou Peoples R China Guangzhou Univ Chinese Med Affiliated Hosp 2 State Key Lab Dampness Syndrome Chinese Med Guangzhou Peoples R China

ISBN: (纸本)9789811998645;9789811998652

Traditional Chinese Medicine (TCM) can effectively suppress inflammation, reduce serum uric acid levels and improve renal function. However, there is a major challenge to explore effective drug combinations due to the complexity of TCM components and the wide variation in drug prescriptions. Data mining technology provides more accurate drug screening and disease prediction than classical statistical methods. This study explores the usage patterns of TCM for the treatment of gouty arthritis with renal dysfunction for the first time. The original data is the outpatient medical records from the Guangdong Provincial Traditional Chinese Medicine Hospital. The Apriori algorithm and louvain algorithm are applied to discover how the usages of traditional Chinese medicine are associated and connected. According to the result of Apriori analysis, the Chinese medicine combination Tufuling and Fenbixie is the most frequently used drug pair for the disease. According to the result of louvain algorithm, 119 communities are divided and core communities are further extracted. The discoveries are mapped and interpreted for clinical practices.

关键词： Gouty arthritis with renal dysfunction Traditional Chinese Medicine Association rule mining louvain algorithm

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Decentralized Model Predictive Voltage Control for Distributed Generation Clusters of Power Systems

Decentralized Model Predictive Voltage Control for Distribut...

引用

IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Cheng, Wei Gong, Guangjing Huang, Jungao Xu, Junjun Chen, Kang NARI Grp Corp State Grid Elect Power Res Inst Nanjing Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (纸本)9798350339345

With the extensive usage of distributed resources, the voltage fluctuations in the power system are frequent, and the distributed resources are scattered, which also increases the difficulty of unified management. A decentralized model predictive voltage control method for the power system based on the collaboration of clusters is proposed. The large-scale decentralized resources are aggregated into interrelated clusters, and the voltage control can be adjusted regionally. Firstly, because of the complex structure of the power system and the wide range of distributed power points, a power grid cluster division scheme based on the improved louvain algorithm is designed. The modularity function is used and the voltage sensitivity of the cluster and the capacity of distributed power supply are taken into account. Secondly, because the power flow of the grid becomes more complex and variable after the access of distributed power supply, a decentralized model predictive control method is proposed based the division of clusters, which can cooperate with the regulation ability of each optical storage to suppress the voltage fluctuation of the power grid caused by the power fluctuation. Finally, a study case is given to verify the effectiveness of the proposed method.

关键词： cluster decentralized model predictive control louvain algorithm renewable energy

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HyDetect: A Hybrid CPU-GPU algorithm for Community Detection 26

HyDetect: A Hybrid CPU-GPU Algorithm for Community Detection

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26th International Conference on High Performance Computing, Data and Analytics (HiPCW)

作者： Bhowmik, Anwesha Vadhiyar, Sathish Indian Inst Sci Dept Computat & Data Sci Bangalore Karnataka India Indian Inst Sci Supercomp Educ & Res Ctr Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (纸本)9781728145358

Community detection is an important problem that is widely applied for finding cluster patterns in brain, social, biological and many other kinds of networks. In this work, we propose a divide-and-conquer community detection algorithm for hybrid CPU-GPU systems. The graph representing a network is partitioned among the CPU and GPU devices of a node, and independent community detection using louvain's algorithm is carried out in both the parts. The communities are iteratively refined by a novel strategy for identifying and moving "doubtful" vertices between the devices. The resulting accuracy is found comparable with the single device parallel louvain algorithms. Our hybrid algorithm helped to explore large graphs that cannot be accommodated in a single device. By harnessing the power of GPUs, our hybrid algorithm is able to provide 42-73% smaller execution times over state-of-art CPU-only algorithms.

关键词： Community detection Hybrid CPU-GPU executions louvain algorithm

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