Using bootstrap procedures for testing the modular partition inferred via leading eigenvector community detection algorithm

2021 ◽  
Author(s):  
Oksana Vertsimakha ◽  
Igor Dzeverin
Keyword(s):  
Community Detection ◽  
Detection Algorithm ◽  
Fine Tuning ◽  
Modular Structure ◽  
Biological Organization ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Numeric Data ◽  
Bootstrap Algorithm ◽  
Modular Structures

AbstractModularity and modular structures can be recognized at various levels of biological organization and in various domains of studies. Recently, algorithms based on network analysis came into focus. And while such a framework is a powerful tool in studying modular structure, those methods usually pose a problem of assessing statistical support for the obtained modular structures. One of the widely applied methods is the leading eigenvector, or Newman’s spectral community detection algorithm. We conduct a brief overview of the method, including a comparison with some other community detection algorithms and explore a possible fine-tuning procedure. Finally, we propose an adapted bootstrap-based procedure based on Shimodaira’s multiscale bootstrap algorithm to derive approximately unbiased p-values for the module partitions of observations datasets. The proposed procedure also gives a lot of freedom to the researcher in constructing the network construction from the raw numeric data, and can be applied to various types of data and used in diverse problems concerning modular structure. We provide an R language code for all the calculations and the visualization of the obtained results for the researchers interested in using the procedure.

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.

Recent trends on community detection algorithms: A survey

2020 ◽  
Vol 34 (35) ◽  
pp. 2050408
Author(s):  
Sumit Gupta ◽  
Dhirendra Pratap Singh
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Data Sets ◽  
Detection Algorithms ◽  
Life Problems ◽  
Application Data ◽  
Art Community ◽  
Community Detection Algorithm ◽  
Two Parameters

In today’s world scenario, many of the real-life problems and application data can be represented with the help of the graphs. Nowadays technology grows day by day at a very fast rate; applications generate a vast amount of valuable data, due to which the size of their representation graphs is increased. How to get meaningful information from these data become a hot research topic. Methodical algorithms are required to extract useful information from these raw data. These unstructured graphs are not scattered in nature, but these show some relationships between their basic entities. Identifying communities based on these relationships improves the understanding of the applications represented by graphs. Community detection algorithms are one of the solutions which divide the graph into small size clusters where nodes are densely connected within the cluster and sparsely connected across. During the last decade, there are lots of algorithms proposed which can be categorized into mainly two broad categories; non-overlapping and overlapping community detection algorithm. The goal of this paper is to offer a comparative analysis of the various community detection algorithms. We bring together all the state of art community detection algorithms related to these two classes into a single article with their accessible benchmark data sets. Finally, we represent a comparison of these algorithms concerning two parameters: one is time efficiency, and the other is how accurately the communities are detected.

scholarly journals Evaluating the role of community detection in improving influence maximization heuristics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
László Hajdu ◽  
Miklós Krész ◽  
András Bóta
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Influence Maximization ◽  
Overlapping Community Detection ◽  
Detection Algorithms ◽  
Overlapping Community ◽  
Community Detection Algorithm ◽  
The Cost

AbstractBoth community detection and influence maximization are well-researched fields of network science. Here, we investigate how several popular community detection algorithms can be used as part of a heuristic approach to influence maximization. The heuristic is based on the community value, a node-based metric defined on the outputs of overlapping community detection algorithms. This metric is used to select nodes as high influence candidates for expanding the set of influential nodes. Our aim in this paper is twofold. First, we evaluate the performance of eight frequently used overlapping community detection algorithms on this specific task to show how much improvement can be gained compared to the originally proposed method of Kempe et al. Second, selecting the community detection algorithm(s) with the best performance, we propose a variant of the influence maximization heuristic with significantly reduced runtime, at the cost of slightly reduced quality of the output. We use both artificial benchmarks and real-life networks to evaluate the performance of our approach.

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.

Gravitational community detection by predicting diameter

2021 ◽  
pp. 2150145
Author(s):  
Himansu Sekhar Pattanayak ◽  
Harsh K. Verma ◽  
Amrit Lal Sangal
Keyword(s):  
Community Detection ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Local Information ◽  
Ground Truth Data ◽  
Overlapping Communities ◽  
Detection Algorithms ◽  
Np Hard Problem ◽  
Community Detection Algorithm ◽  
The Individual

Community detection is a pivotal part of network analysis and is classified as an NP-hard problem. In this paper, a novel community detection algorithm is proposed, which probabilistically predicts communities’ diameter using the local information of random seed nodes. The gravitation method is then applied to discover communities surrounding the seed nodes. The individual communities are combined to get the community structure of the whole network. The proposed algorithm, named as Local Gravitational community detection algorithm (LGCDA), can also work with overlapping communities. LGCDA algorithm is evaluated based on quality metrics and ground-truth data by comparing it with some of the widely used community detection algorithms using synthetic and real-world networks.

Network size, structure, and pathogen transmission: a simulation study comparing different community detection algorithms

Behaviour ◽  
2018 ◽  
Vol 155 (7-9) ◽  
pp. 639-670 ◽  
Author(s):  
Kelsey M. Sumner ◽  
Collin M. McCabe ◽  
Charles L. Nunn
Keyword(s):  
Community Detection ◽  
Disease Transmission ◽  
Size Structure ◽  
Disease Risk ◽  
Detection Algorithm ◽  
Network Size ◽  
Pathogen Transmission ◽  
Small Scale ◽  
Detection Algorithms ◽  
Community Detection Algorithm

Abstract Social substructure can influence pathogen transmission. Modularity measures the degree of social contact within versus between “communities” in a network, with increasing modularity expected to reduce transmission opportunities. We investigated how social substructure scales with network size and disease transmission. Using small-scale primate social networks, we applied seven community detection algorithms to calculate modularity and subgroup cohesion, defined as individuals’ interactions within subgroups proportional to the network. We found larger networks were more modular with higher subgroup cohesion, but the association’s strength varied by community detection algorithm and substructure measure. These findings highlight the importance of choosing an appropriate community detection algorithm for the question of interest, and if not possible, using multiple algorithms. Disease transmission simulations revealed higher modularity and subgroup cohesion resulted in fewer infections, confirming that social substructure has epidemiological consequences. Increased subdivision in larger networks could reflect constrained time budgets or evolved defences against disease risk.

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 Local Community Detection Algorithm Based on Minimal Cluster

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Yong Zhou ◽  
Guibin Sun ◽  
Yan Xing ◽  
Ranran Zhou ◽  
Zhixiao Wang
Keyword(s):  
Community Detection ◽  
Local Community ◽  
Detection Algorithm ◽  
Single Node ◽  
Initial Node ◽  
Detection Algorithms ◽  
Local Community Detection ◽  
Two Phases ◽  
Community Detection Algorithm

In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

A Community Detection Algorithm Based on Node Degree Difference and Node Similarity

2013 ◽  
Vol 462-463 ◽  
pp. 458-461
Author(s):  
Jian Jun Cheng ◽  
Peng Fei Wang ◽  
Qi Bin Zhang ◽  
Zheng Quan Zhang ◽  
Ming Wei Leng ◽  
...  
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Detection Algorithm ◽  
Coarse Grained ◽  
Node Degree ◽  
Detection Algorithms ◽  
Starting Point ◽  
Node Similarity ◽  
Feasible Algorithm ◽  
Community Detection Algorithm

This paper proposes an algorithm called DDSCDA, which is based on the concepts of the node degree difference and the node similarity. In the algorithm, we iteratively extract the node from the network with larger degree and certified the node as a kernel node, then take the kernel node as the founder or initiator of a community to attract its neighbors to join in that community; by doing so, we obtain a partition corresponding to a coarse-grained community structure of the network. Finally taken the coarse-grained community as a starting point, we use the strategy of LPA to propagate labels through the network further. At the end of the algorithm, we obtain the final community structure. We compared the performance with classical community detection algorithms such as LPA, LPAm, FastQ, etc., the experimental results have manifested that our proposal is a feasible algorithm, can extract higher quality communities from the network, and outperforms the previous algorithms significantly.

scholarly journals A bi-directional approach to comparing the modular structure of networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Daniel Straulino ◽  
Mattie Landman ◽  
Neave O’Clery
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Distance Measure ◽  
Detection Algorithm ◽  
The Other ◽  
Modular Structure ◽  
Quality Function ◽  
Normalized Mutual Information ◽  
Connectivity Structure ◽  
Detection Algorithms

AbstractHere we propose a new method to compare the modular structure of a pair of node-aligned networks. The majority of current methods, such as normalized mutual information, compare two node partitions derived from a community detection algorithm yet ignore the respective underlying network topologies. Addressing this gap, our method deploys a community detection quality function to assess the fit of each node partition with respect to the other network’s connectivity structure. Specifically, for two networks A and B, we project the node partition of B onto the connectivity structure of A. By evaluating the fit of B’s partition relative to A’s own partition on network A (using a standard quality function), we quantify how well network A describes the modular structure of B. Repeating this in the other direction, we obtain a two-dimensional distance measure, the bi-directional (BiDir) distance. The advantages of our methodology are three-fold. First, it is adaptable to a wide class of community detection algorithms that seek to optimize an objective function. Second, it takes into account the network structure, specifically the strength of the connections within and between communities, and can thus capture differences between networks with similar partitions but where one of them might have a more defined or robust community structure. Third, it can also identify cases in which dissimilar optimal partitions hide the fact that the underlying community structure of both networks is relatively similar. We illustrate our method for a variety of community detection algorithms, including multi-resolution approaches, and a range of both simulated and real world networks.

Sign in / Sign up

Export Citation Format

Share Document