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Complex networks from time series data allow an efficient historical stage division of urban air quality information

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APPLIED MATHEMATICS AND COMPUTATION 2021年 410卷 126435-126435页

作者： Qiao, Honghai Deng, Zhenghong Li, Huijia Hu, Jun Song, Qun Xia, Chengyi Northwestern Polytech Univ Sch Automat Xian 710072 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou 350000 Peoples R China Northwestern Polytech Univ Yangtze River Delta Res Inst NPU Taicang Taicang 215400 Peoples R China Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin 300384 Peoples R China

Urban air quality is related to human health in modern life. The statistical features of urban air quality highly depend on the division of historical stages. Conventional division methods that use a fixed period (e.g., month) can result in confusion during statistical analysis. In this study, we propose a novel analysis technique based on time series complex network theories to divide the historical information of urban air quality by using flexible periods. First, air quality information is converted into time series complex networks via a multilayer visibility model. Thereafter, an improved community detection algorithm is proposed on the basis of network characteristics. In particular, the centrality of nodes is increased using a kernel density estimation model. An improved bidirectional search pattern results in the optimal modularity. Finally, the historical curves of urban air quality are divided into several stages in accordance with the optimal clustering results. The simulation experiments demonstrate important conclusions. The clustering accuracy of the proposed algorithm is superior to those of other evaluated methods on actual air quality networks. The number of historical stages is decreased constantly in accordance with clustering results, and this condition is beneficial for statistics. Our results can reasonably explain the relationship between valid time and air quality features. The proposed technique can provide effective and reliable division results of historical stages. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Time series complex networks community detection algorithm Urban air quality indexes Visibility graph Environmental science Historical stage division

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Online Network Partition for Voltage Control in Power Systems with Large Penetration of Renewable Energy 5

Online Network Partition for Voltage Control in Power System...

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5th Asia Conference on Power and Electrical Engineering (ACPEE)

作者： Yang, Yang Sun, Yichao Wang, Qi Zhu, Ling Wang, Yu Wang, Yiran Nanjing Normal Univ Sch Elect & Automat Engn Nanjing Peoples R China NARI Technol Co Ltd Power Syst Stabil Control Co Nanjing Peoples R China

ISBN: (数字)9781728152813

ISBN: (纸本)9781728152813

Network partition is the basis of voltage control, aiming at reducing load of communication devices and calculation modules, whilst solving var-voltage problems on the spot in larger and larger complex power networks. Power systems with large penetration of renewable energy such as solar or wind energy have time-variant operation characteristics and network partition in such networks requires efficiency and less running time to keep up with the changes. Generally, partition methods are mostly based electrical connection between buses and clustering methods, whose aim is to cluster buses that have similar voltage reaction characteristics into the same set. There are many ways to obtain electrical connection and partition networks, and their effects and efficiency vary from one to another. In this paper is proposed a practical method to do the computation of electrical connection while partitioning the power network into smaller sub-graphs online in less CPU time than traditional power network partitioning methods. Its aim is to optimize the index of partition, as well as to minimize inter-zone connection while maximizing intra-zone connection, fitting the partition results with different operation characteristics of renewable energy sources. The method to do the calculation of electrical connection matrix is based on a modified method of sensitivity while the partition method is based on the widely applied concept modularity and community detection algorithm.

关键词： Online Voltage Control Power Network Partition Renewable Energy community detection algorithm Electrical Connection

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Review of community detection over Social Media: Graph Prospective

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第2期10卷 591-602页

作者： Jain, Pranita Tomar, Deepak Singh Maulana Azad Natl Inst Technol Dept Comp Sci Bhopal 462001 India

community over the social media is the group of globally distributed end users having similar attitude towards a particular topic or product. community detection algorithm is used to identify the social atoms that are more densely interconnected relatively to the rest over the social media platform. Recently researchers focused on group-based algorithm and member-based algorithm for community detection over social media. This paper presents comprehensive overview of community detection technique based on recent research and subsequently explores graphical prospective of social media mining and social theory (Balance theory, status theory, correlation theory) over community detection. Along with that this paper presents a comparative analysis of three different state of art community detection algorithm available on I-Graph package on python i.e. walk trap, edge betweenness and fast greedy over six different social media data set. That yield intersecting facts about the capabilities and deficiency of community analysis methods.

关键词： community detection social media social media mining homophily influence confounding social theory community detection algorithm

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Network Partition-Based Zonal Voltage Control for Distribution Networks With Distributed PV Systems

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IEEE TRANSACTIONS ON SMART GRID 2018年第5期9卷 4087-4098页

作者： Zhao, Bo Xu, Zhicheng Xu, Chen Wang, Caisheng Lin, Feng Zhejiang Elect Power Corp Res Inst Hangzhou 310014 Zhejiang Peoples R China Hefei Univ Technol Dept Elect Engn & Automat Hefei 230009 Anhui Peoples R China Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA

As the penetration level of distributed photovoltaic (PV) systems keeps increasing in distribution networks, overvoltage due to reverse power flow is an urgent issue to be addressed. This paper proposes a voltage regulation method by utilizing the voltage control capability of PV inverters. A novel network partition approach based on a community detection algorithm is presented to realize zonal voltage control in a shorter control response time using the minimum amount of reactive power compensation and active power curtailment. An improved modularity index that considers local reactive power balance is introduced to partition a distribution network into several clusters/communities with PVs based on the node voltage sensitivity analysis. An optimal reactive and active power control strategy is proposed for voltage control in each cluster. The voltage management of the overall system can he achieved by controlling each cluster separately. The proposed approach is applied to the voltage control of a practical 10 kV, 37-node feeder. Case studies on the real distribution network and a modified IEEE 123-node system are carried out to verify the feasibility and effectiveness of the proposed method.

关键词： PV system generation zonal voltage control optimal power management community detection algorithm

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Efficient Vector Influence Clustering Coefficient Based Directed community detection Method

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IEEE ACCESS 2017年 5卷 17106-17116页

作者： Deng, Xiaolong Zhai, Jiayu Lv, Tiejun Yin, Luanyu Beijing Univ Posts & Telecommun Key Lab Trustworthy Distributed Comp & Serv Educ Minist Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Int Sch Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing 100876 Peoples R China Beijing Normal Univ China Acad Social Management Beijing 100875 Peoples R China

community detection algorithms are important for determining the character statistics of complex networks. Compared with the conventional community detection algorithms, which always focus on undirected networks, our algorithm is concentrated on directed networks such as the WeChat moments relationship network and the Sina Micro-Blog follower relationship network. To address disadvantages such as lower execution efficiency and higher deviation of precision that current directed community detection algorithms always have, we propose a newapproach that is based on the triangle structure of community basis and modeled on the local information transfer process to precisely detect communities in directed networks. Based on the directed vector theory in probability graphs and the dynamic information transfer gain (ITG) of vertices in directed networks, we propose the novel ITG method and the corresponding target optimal function for evaluating the partition quality in a community detection algorithm. Then, we combine ITG and the target function to create the new community detection algorithm ITG-directed weighted community clustering for directed networks. With extensive experiments using artificial network data sets and large, real-world network data sets derived from online social media, our algorithm proved to be more accurate and faster in directed networks than several traditional, well-known community detection methods, such as FastGN, order statistics local optimization method, and Infomap.

关键词： community detection algorithm information transfer gain target function optimization community detection in directed network

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Common strategy to improve community detection performance based on the nodes' property

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2017年第2期2卷 68-72页

作者： Du, Wei He, Xiaochen Xi An Jiao Tong Univ Sch Publ Policy & Adm Xian Shaanxi Peoples R China Xi An Jiao Tong Univ Ctr Adm Complex Sci Xian Shaanxi Peoples R China

Improving the community detection algorithm is of great importance. The authors propose a novel method based on the nodes' property in order to detect the community structure. Given a detected community structure, in which nodes have their community signals, the value of the modularity can be changed if a node's community sign change to other communities' signs. Accordingly, the new method readjusts the affiliation between a node and its community in order to raise the modularity value. Experimental results of the detection for a list of open-source networks show that the proposed algorithm can detect better community structure than classic methodologies based on modularity.

关键词： network theory (graphs) graph theory social networking (online) pattern clustering complex networks common strategy community detection performance nodes community detection algorithm detected community structure community signals community sign change communities modularity value

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A recommender system based on local topology features and message rate 2

A recommender system based on local topology features and me...

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2nd International Congress on Technology, Communication and Knowledge (ICTCK)

作者： Rokni, Mohadeseh Moattar, Mohammad. H. Jalali, Mehrdad Islamic Azad Univ Mashhad Branch Dept Comp Engn Mashhad Iran

ISBN: (纸本)9781467397629

Social networks have become an indispensable part of every day's life. A vast majority of people use at least one social network to communicate with friends or business partners. Different applications of social networks are used to meet diverse needs of users. Social networks use recommender systems to provide wider experience for their users. Friend recommendation has been the most popular and important application used to expand the circle of friends and social communication. Social networks are very dynamic. Every moment new people are added and new forms of relationship are formed. Therefore, evaluating social networks is a complicated task. In this paper, the focus is both on the characteristics of social networks as well as the messages sent between and among the nodes. A mixed method will be suggested which will initially use a community detection algorithm based on message rate as a pruning algorithm. It will then divide the network into several communities. Afterwards, the FriendLink algorithm will be employed in order to estimate the similarity score between all users based on the properties of the network structure and the rate of the messages sent. Finally, users with maximal similarity will be suggested as friends to the target user. The results of testing a Facebook-like dataset revealed that the suggested method is of a great precision and accuracy as compared to FriendLink and CSM algorithms.

关键词： social networks Friend recommendation network structure community detection algorithm

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community detection on Large Graph Datasets for Recommender Systems 14

Community Detection on Large Graph Datasets for Recommender ...

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14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： Parimi, Rohit Caragea, Doina Kansas State Univ CIS Dept Manhattan KS 66506 USA

ISBN: (纸本)9781479942749

The explosion of content on World Wide Web (WWW) means that consumers are presented with a wide variety of items to choose from ( items that concur with their taste and requirements). The generation of personalized consumer recommendations has become a crucial functionality for many web applications, yet a challenging task, given the scale and nature of the data. One popular solution to creating personalized item suggestions to users is recommender systems. In this work, we propose an approach that integrates community detection with neighborhood-based recommender systems, specifically, the Adsorption algorithm, for recommending items using implicit user preferences. Network communities represent a principled way of organizing real-world networks into densely connected clusters of nodes. We believe that these dense clusters identified by the community detection algorithm will be helpful to construct user neighborhoods for Adsorption algorithm for recommending collaborators and books to users. Through comprehensive experimental evaluations on the DBLP co-author dataset and BookCrossing dataset, the proposed approach of integrating community detection with the Adsorption algorithm is shown to deliver good performance.

关键词： Internet database management systems graph theory recommender systems Book Crossing dataset DBLP coauthor dataset WWW Web applications World Wide Web adsorption algorithm community detection algorithm dense clusters implicit user preferences items recommendation large graph datasets neighborhood-based recommender systems network communities personalized consumer recommendations personalized item suggestions real-world networks user neighborhoods Adsorption algorithm design and analysis Communities detection algorithms Image edge detection Recommender systems Training Adsorption algorithm Collaborative Filtering community detection Neighborhood-based Approaches Recommender systems World Wide Web detection algorithms algorithm design and analysis Edge detection web applications collaborative filtering database management systems Network communities Graph theory Adsorption Consumers Internets

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community detection for directional neural networks inferred from EEG data

Community detection for directional neural networks inferred...

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33rd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBS)

作者： Liu, Ying Moser, Jason Aviyente, Selin Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA Michigan State Univ Dept Psychol E Lansing MI 48824 USA

ISBN: (纸本)9781424441228

One major challenge in neuroscience is to identify the functional modules from multichannel, multiple subjects recordings. Most research on community detection has focused on finding the association matrix based on functional connectivity, instead of effective connectivity, thus not capturing the causality in the network. In this paper, we propose a community detection algorithm suitable for weighted and asymmetric (directed) networks representing effective connectivity, and apply the algorithm to multichannel electroencephalogram (EEG) data. In addition, we extend the algorithm to find one common community structure from multiple subjects.

关键词： algorithm design and analysis Clustering algorithms Communities detection algorithms EEG data Electroencephalography Neuroscience Partitioning algorithms asymmetric networks community detection algorithm directional neural networks electroencephalography

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Detecting Communities in Sparse MANETs

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IEEE-ACM TRANSACTIONS ON NETWORKING 2011年第5期19卷 1434-1447页

作者： Drugan, Ovidiu Valentin Plagemann, Thomas Munthe-Kaas, Ellen Univ Oslo Dept Informat N-0316 Oslo Norway

In sparse mobile ad hoc networks, placement of services and data is crucial to assure their availability to all nodes because sparse population of nodes can lead to (frequent) network partitions. If these dynamic networks display a fairly stable cluster structure, it is possible to utilize this structure to improve service and data availability. However, clustering in a dynamic network is a very challenging task due to the ever-changing topology and irregular density of such a network. In this paper, we investigate clustering of dynamic networks with the help of community detection mechanisms, using only topology information from the local routing table. The main aim of our approach is to reduce to "zero" the communication overhead needed for cluster management and to dynamically adapt to the size and layout of the network. We have performed extensive experiments to evaluate the consistency, quality, and stability of the clustering returned by our algorithms. The results show that our nonintrusive clustering indeed discovers temporary groups of nodes that form stable clusters in the network. Moreover, even though the local routing tables in general reflect slightly diverging topologies, our results still show only small differences between the communities detected at different nodes.

关键词： Clustering community detection algorithm network topology rescue operations sparse mobile ad hoc networks (MANETs) wireless communications

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