scholarly journals Comparison between Louvain and Leiden Algorithm for Network Structure: A Review

2021 ◽  
Vol 2129 (1) ◽  
pp. 012028
Author(s):  
Siti Haryanti Hairol Anuar ◽  
Zuraida Abal Abas ◽  
Norhazwani Mohd Yunos ◽  
Nurul Hafizah Mohd Zaki ◽  
Nurul Akmal Hashim ◽  
...  
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Network Structure ◽  
The Real ◽  
Real Network ◽  
Detection Algorithms ◽  
Advantages And Disadvantages ◽  
Different Types ◽  
The Future

Abstract In the real network, there must be a large and complex network. The solution to understand that kind of network structure is using the community detection algorithms. There are a lot of other algorithms out there to perform community detection. Each of the algorithms has its own advantages and disadvantages with different types and scale of complex network. The Louvain has been experimented that shows bad connected in community and disconnected when running the algorithm iteratively. In this paper, two algorithm based on agglomerative method (Louvain and Leiden) are introduced and reviewed. The concept and benefit are summarized in detail by comparison. Finally, the Leiden algorithm’s property is considered the latest and fastest algorithm than the Louvain algorithm. For the future, the comparison can help in choosing the best community detection algorithms even though these algorithms have different definitions of community.

Community detection in complex network by network embedding and density clustering

2021 ◽  
pp. 1-12
Author(s):  
JinFang Sheng ◽  
Huaiyu Zuo ◽  
Bin Wang ◽  
Qiong Li
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Dimensional Space ◽  
Detection Algorithm ◽  
Superior Performance ◽  
Network Embedding ◽  
Detection Algorithms ◽  
Density Clustering ◽  
Community Detection Algorithm ◽  
Low Dimensional

 In a complex network system, the structure of the network is an extremely important element for the analysis of the system, and the study of community detection algorithms is key to exploring the structure of the complex network. Traditional community detection algorithms would represent the network using an adjacency matrix based on observations, which may contain redundant information or noise that interferes with the detection results. In this paper, we propose a community detection algorithm based on density clustering. In order to improve the performance of density clustering, we consider an algorithmic framework for learning the continuous representation of network nodes in a low-dimensional space. The network structure is effectively preserved through network embedding, and density clustering is applied in the embedded low-dimensional space to compute the similarity of nodes in the network, which in turn reveals the implied structure in a given network. Experiments show that the algorithm has superior performance compared to other advanced community detection algorithms for real-world networks in multiple domains as well as synthetic networks, especially when the network data chaos is high.

scholarly journals Amendments to the Rules

1970 ◽  
Vol 23 (2) ◽  
pp. 258-264
Author(s):  
A. N. Cockcroft
Keyword(s):  
High Speed ◽  
Large Power ◽  
International Conference ◽  
Advantages And Disadvantages ◽  
Different Types ◽  
The Future ◽  
The Subject

A Great deal has already been written about possible amendments to the Collision Regulations, but as there may be an international conference on the subject in 1972 the various schemes should be discussed as much as possible so that their advantages and disadvantages will be fully appreciated.Criticism of the present Steering and Sailing Rules is mainly concerned with Rule 21. Disadvantages of this rule include the following:(1) If a giving-way vessel on a crossing course takes no action the privileged vessel must not act until collision cannot be avoided by the giving-way vessel alone. By such time collision is likely to be inevitable.(2) Small sailing vessels which may not easily be seen, especially at night, are required to maintain course and speed for large power-driven vessels.(3) High-speed vessels such as hovercraft are required to maintain course and speed for low powered ships crossing from the port side.(4) No provision is made for different types of hampered vessel approaching one another so as to involve risk of collision. If deep-draught vessels in certain areas are to be included in this category in the future the question of priorities may have to be considered.

Clustering Algorithm for Community Detection in Complex Network: A Comprehensive Review

2020 ◽  
Vol 13 (4) ◽  
pp. 542-549
Author(s):  
Smita Agrawal ◽  
Atul Patel
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Detection Algorithm ◽  
Connected Subgraph ◽  
Comprehensive Review ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Large Scale Networks

Many real-world social networks exist in the form of a complex network, which includes very large scale networks with structured or unstructured data and a set of graphs. This complex network is available in the form of brain graph, protein structure, food web, transportation system, World Wide Web, and these networks are sparsely connected, and most of the subgraphs are densely connected. Due to the scaling of large scale graphs, efficient way for graph generation, complexity, the dynamic nature of graphs, and community detection are challenging tasks. From large scale graph to find the densely connected subgraph from the complex network, various community detection algorithms using clustering techniques are discussed here. In this paper, we discussed the taxonomy of various community detection algorithms like Structural Clustering Algorithm for Networks (SCAN), Structural-Attribute based Cluster (SA-cluster), Community Detection based on Hierarchical Clustering (CDHC), etc. In this comprehensive review, we provide a classification of community detection algorithm based on their approach, dataset used for the existing algorithm for experimental study and measure to evaluate them. In the end, insights into the future scope and research opportunities for community detection are discussed.

Fenton Reaction Induced by Fe-Based Nanoparticles for Tumor Therapy

2021 ◽  
Vol 17 (8) ◽  
pp. 1510-1524
Author(s):  
Jian Zhou ◽  
Miao Lei ◽  
Xue-Liang Peng ◽  
Dai-Xu Wei ◽  
Lu-Ke Yan
Keyword(s):  
Wastewater Treatment ◽  
Cancer Cells ◽  
Fenton Reaction ◽  
Human Cancer ◽  
Tumor Therapy ◽  
Iron Ions ◽  
Advantages And Disadvantages ◽  
Different Types ◽  
The Future ◽  
Iron Based

Fenton reaction, a typical inorganic reaction, is broadly utilized in the field of wastewater treatment. Recently In case of its ability to inhibit the growth of cancer cells, it has been frequently reported in cancer treatment. Using the unique tumor microenvironment in cancer cells, many iron-based nanoparticles have been developed to release iron ions in cancer cells to induce Fenton reaction. In this mini review, we outline several different types of iron-based nanoparticles and several main means to enhance Fenton reaction in cancer cells. Finally, we discussed the advantages and disadvantages of iron-based nanoparticles for cancer therapy, prospected the future development of iron-based nanoparticles. It is believed that iron-based nanoparticles can make certain contribution to the cause of human cancer in the future.

Active semisupervised community detection based on asymmetric similarity measure

2015 ◽  
Vol 29 (13) ◽  
pp. 1550078 ◽  
Author(s):  
Mingwei Leng ◽  
Liang Huang ◽  
Longjie Li ◽  
Hanhai Zhou ◽  
Jianjun Cheng ◽  
...  
Keyword(s):  
Community Structure ◽  
Prior Knowledge ◽  
Complex Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Directed Network ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Similarity Method ◽  
Asymmetric Similarity

Semisupervised community detection algorithms use prior knowledge to improve the performance of discovering the community structure of a complex network. However, getting those prior knowledge is quite expensive and time consuming in many real-world applications. This paper proposes an active semisupervised community detection algorithm based on the similarities between nodes. First, it transforms a given complex network into a weighted directed network based on the proposed asymmetric similarity method, some informative nodes are selected to be the labeled nodes by using an active mechanism. Second, the proposed algorithm discovers the community structure of a complex network by propagating the community labels of labeled nodes to their neighbors based on the similarity between a node and a community. Finally, the performance of the proposed algorithm is evaluated with three real networks and one synthetic network and the experimental results show that the proposed method has a better performance compared with some other community detection algorithms.

scholarly journals Network Analysis Based on Important Node Selection and Community Detection

Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2294
Author(s):  
Attila Mester ◽  
Andrei Pop ◽  
Bogdan-Eduard-Mădălin Mursa ◽  
Horea Greblă ◽  
Laura Dioşan ◽  
...  
Keyword(s):  
Complex Network ◽  
Community Detection ◽  
Centrality Measures ◽  
Complementary Information ◽  
Detection Algorithms ◽  
Node Importance ◽  
Dual Perspective ◽  
The Stability ◽  
Important Nodes ◽  
Empirical Means

The stability and robustness of a complex network can be significantly improved by determining important nodes and by analyzing their tendency to group into clusters. Several centrality measures for evaluating the importance of a node in a complex network exist in the literature, each one focusing on a different perspective. Community detection algorithms can be used to determine clusters of nodes based on the network structure. This paper shows by empirical means that node importance can be evaluated by a dual perspective—by combining the traditional centrality measures regarding the whole network as one unit, and by analyzing the node clusters yielded by community detection. Not only do these approaches offer overlapping results but also complementary information regarding the top important nodes. To confirm this mechanism, we performed experiments for synthetic and real-world networks and the results indicate the interesting relation between important nodes on community and network level.

scholarly journals How Mobility and Sociality Reshape the Context: A Decade of Experience in Mobile CrowdSensing

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6397
Author(s):  
Michele Girolami ◽  
Dimitri Belli ◽  
Stefano Chessa ◽  
Luca Foschini
Keyword(s):  
Community Detection ◽  
Urban Areas ◽  
Human Mobility ◽  
Primary Source ◽  
Contact Tracing ◽  
Mobile Crowdsensing ◽  
Detection Algorithms ◽  
The Future ◽  
Building Information ◽  
Edge Based

The possibility of understanding the dynamics of human mobility and sociality creates the opportunity to re-design the way data are collected by exploiting the crowd. We survey the last decade of experimentation and research in the field of mobile CrowdSensing, a paradigm centred on users’ devices as the primary source for collecting data from urban areas. To this purpose, we report the methodologies aimed at building information about users’ mobility and sociality in the form of ties among users and communities of users. We present two methodologies to identify communities: spatial and co-location-based. We also discuss some perspectives about the future of mobile CrowdSensing and its impact on four investigation areas: contact tracing, edge-based MCS architectures, digitalization in Industry 5.0 and community detection algorithms.

scholarly journals Community Detection by Node Betweenness and Similarity in Complex Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chong Feng ◽  
Jianxu Ye ◽  
Jianlu Hu ◽  
Hui Lin Yuan
Keyword(s):  
Complex Networks ◽  
Complex Network ◽  
Community Detection ◽  
Experimental Results ◽  
Detection Algorithms ◽  
Simulation Results ◽  
Novel Algorithm

Community detection of complex networks has always been a hot issue. With the mixed parameters μ increase in network complexity, community detection algorithms need to be improved. Based on previous work, the paper designs a novel algorithm from the perspective of node betweenness properties and gives the detailed steps of the algorithm and simulation results. We compare the proposed algorithm with a series of typical algorithms through experiments on synthetic and actual networks. Experimental results on artificial and real networks demonstrate the effectiveness and superiority of our algorithm.

Complex network structure extraction based on community relevance

2020 ◽  
Vol 31 (04) ◽  
pp. 2050062
Author(s):  
Jingyi Ding ◽  
Licheng Jiao ◽  
Jianshe Wu ◽  
Fang Liu
Keyword(s):  
Computational Complexity ◽  
Community Detection ◽  
Network Structure ◽  
Link Prediction ◽  
Detection Algorithm ◽  
Node Degree ◽  
Network Function ◽  
Detection Algorithms ◽  
New Perspective ◽  
Community Detection Algorithm

One way to understand the network function and analyze the network structure is to find the communities of the network accurately. Now, there are many works about designing algorithms for community detection. Most community detection algorithms are based on modularity optimization. However, these methods not only have disadvantages in computational complexity, but also have the problem of resolution restriction. Designing a community detection algorithm that is fast and effective remains a challenge in the field. We attempt to solve the community detection problem in a new perspective in this paper, believing that the assumption used to solve the link prediction problem is useful for the problem of community detection. By using the similarity between modules of the network, we propose a new method to extract the community structure in this paper. Our algorithm consists of three steps. First, we initialize a community partition based on the distribution of the node degree; second, we calculate the similarity between different communities, where the similarity is the index to describe the closeness of the different communities. We assume that the much closer the two different communities are, the greater the likelihood of being divided together; finally, merge the pairs of communities which has the highest similarity value as possible as we can and stop when the condition is not satisfied. Because the convergence of our algorithm is very fast in the process of merging, we find that our method has advantages both in the computational complexity and in the accuracy when compared with other six classical algorithms. Moreover, we design a new measure to describe how difficulty the network division is.

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