scholarly journals Neighborhood-based bridge node centrality tuple for complex network analysis

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Network Analysis ◽  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Algebraic Connectivity ◽  
Neighborhood Graph ◽  
Node Centrality ◽  
Centrality Metrics ◽  
Community Detection Algorithm ◽  
Topological Position

AbstractWe define a bridge node to be a node whose neighbor nodes are sparsely connected to each other and are likely to be part of different components if the node is removed from the network. We propose a computationally light neighborhood-based bridge node centrality (NBNC) tuple that could be used to identify the bridge nodes of a network as well as rank the nodes in a network on the basis of their topological position to function as bridge nodes. The NBNC tuple for a node is asynchronously computed on the basis of the neighborhood graph of the node that comprises of the neighbors of the node as vertices and the links connecting the neighbors as edges. The NBNC tuple for a node has three entries: the number of components in the neighborhood graph of the node, the algebraic connectivity ratio of the neighborhood graph of the node and the number of neighbors of the node. We analyze a suite of 60 complex real-world networks and evaluate the computational lightness, effectiveness, efficiency/accuracy and uniqueness of the NBNC tuple vis-a-vis the existing bridgeness related centrality metrics and the Louvain community detection algorithm.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

RECODE: SOFTWARE PACKAGE REFACTORING VIA COMMUNITY DETECTION IN BIPARTITE SOFTWARE NETWORKS

2014 ◽  
Vol 17 (07n08) ◽  
pp. 1450006 ◽  
Author(s):  
WEIFENG PAN ◽  
BING LI ◽  
BO JIANG ◽  
KUN LIU
Keyword(s):  
Community Detection ◽  
Software Quality ◽  
Real World ◽  
Software Package ◽  
Object Oriented ◽  
Intrinsic Property ◽  
Detection Algorithm ◽  
Software Complexity ◽  
Novel Approach ◽  
Community Detection Algorithm

It is an intrinsic property of real-world software to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality. So successful software has to be reconditioned from time to time. Though many refactoring approaches have been proposed, only a few of them are performed at the package level. In this paper, we present a novel approach to refactor the package structure of object-oriented (OO) software. It uses weighted bipartite software networks to represent classes, packages, and their dependencies; it proposes a guidance community detection algorithm (GUIDA) to obtain the optimized package structure; and it finally provides a list of classes as refactoring candidates by comparing the optimized package structure with the real package structure. Through a set of experiments we have shown that the proposed approach is able to identify a majority of classes that experts recognize as refactoring candidates, and the benefits of our approach are illustrated in comparison with other two approaches.

Network analysis and systemic FX settlement risk

2017 ◽  
Vol 34 (3-4) ◽  
Author(s):  
José Henry León-Janampa
Keyword(s):  
Network Analysis ◽  
Financial Institutions ◽  
Detection Algorithm ◽  
Network Centrality ◽  
Settlement System ◽  
Centrality Metrics ◽  
Community Detection Algorithm ◽  
Fx Trading ◽  
Law Degree ◽  
Network Metrics

AbstractA proposal for applying network analysis to a foreign exchange (FX) settlement system is considered. In particular, network centrality metrics are used to analyse payments of financial institutions which settle through CLS Bank (CLS). Network centrality metrics provide a way to study settlement members’ connectivity, obtain a sense of their payments evolution with time, and measure their network topology variability. The analysis shows that although the continuous link settlement (CLS) network structure can be approximated with a power law degree distribution for many trade days, this is not always the case. A network community detection algorithm is applied to the FX settlement network to explore relationships between communities and to detect classification patterns in the FX trading net payments. A metric called SinkRank is used to build a ranking of the most systemic settlement risk important financial institutions trading on the FX system, and to understand how the metric depends on network’s connectivity. Since network metrics do not fully explain the dynamics of the settlement process, the CLS’ settlement system is simulated to measure the contagion of unsettled trades and its spread among network members. The effect of settlement failure and contagion on the settlement members is also explored.

A novel fuzzy community detection method based on improved community adjacency list

2021 ◽  
pp. 2150186
Author(s):  
Xinyue Zhou
Keyword(s):  
Network Analysis ◽  
Community Detection ◽  
Large Scale ◽  
Detection Algorithm ◽  
Good Effect ◽  
Network Applications ◽  
Adjacency List ◽  
Membership Value ◽  
Community Detection Algorithm ◽  
Partition Structure

With the development of economy and society, network analysis is widely used in more and more fields. Signed network has a good effect in the process of representation and display. As an important part of network analysis, fuzzy community detection plays an increasingly important role in analyzing and visualizing the real world. Fuzzy community detection helps to detect nodes that belong to some communities but are still closely related to other communities. These nodes are helpful for mining information from the network more realistically. However, there is little research in this field. This paper proposes a fuzzy community detection algorithm based on pointer and adjacency list. The model adopts a new ICALF network data structure, which can achieve the effect of storing community partition structure and membership value between community and node at the same time, with low time complexity and storage space. Experiments on real networks verify the correctness of the method, and prove that the method is suitable for large-scale network applications.

scholarly journals Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density

2021 ◽  
Vol 13 (4) ◽  
pp. 89
Author(s):  
Yubo Peng ◽  
Bofeng Zhang ◽  
Furong Chang
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Point Of View ◽  
Bipartite Network ◽  
Bipartite Networks ◽  
Overlapping Community Detection ◽  
The Real ◽  
Overlapping Community ◽  
Community Detection Algorithm

Community detection plays an essential role in understanding network topology and mining underlying information. A bipartite network is a complex network with more important authenticity and applicability than a one-mode network in the real world. There are many communities in the network that present natural overlapping structures in the real world. However, most of the research focuses on detecting non-overlapping community structures in the bipartite network, and the resolution of the existing evaluation function for the community structure’s merits are limited. So, we propose a novel function for community detection and evaluation of the bipartite network, called community density D. And based on community density, a bipartite network community detection algorithm DSNE (Density Sub-community Node-pair Extraction) is proposed, which is effective for overlapping community detection from a micro point of view. The experiments based on artificially-generated networks and real-world networks show that the DSNE algorithm is superior to some existing excellent algorithms; in comparison, the community density (D) is better than the bipartite network’s modularity.

Community detection based on preferred mode in bipartite networks

2018 ◽  
Vol 32 (27) ◽  
pp. 1850330
Author(s):  
Guolin Wu ◽  
Changgui Gu ◽  
Lu Qiu ◽  
Huijie Yang
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Bipartite Networks ◽  
Community Structures ◽  
Large Size ◽  
Community Information ◽  
Community Detection Algorithm

Identifying community structures in bipartite networks is a popular topic. People usually focus on one of two modes in bipartite networks when uncovering their community structures. According to this understanding, we design a community detection algorithm based on preferred mode in bipartite networks. This algorithm can select corresponding preferred mode according to specific application scenario and effectively extract community information in bipartite networks. The trials in artificial and real-world networks show that the algorithm based on preferred mode has better performances in both small size of bipartite networks and large size of bipartite networks.

Parameter-free Community Detection through Distance Dynamic Synchronization

2019 ◽  
Vol 62 (11) ◽  
pp. 1625-1638
Author(s):  
Jianbin Huang ◽  
Qingquan Bian ◽  
Heli Sun ◽  
Yaming Yang ◽  
Yu Zhou
Keyword(s):  
Community Detection ◽  
Significant Role ◽  
Real World ◽  
Detection Algorithm ◽  
Parameter Setting ◽  
Cohesion Parameter ◽  
Community Detection Algorithm ◽  
Synthetic Networks

Abstract Community detection plays a significant role in understanding the essence of a network. A recently proposed algorithm Attractor, which is based on distance dynamics, can spot communities effectively, but it depends on a cohesion parameter. Moreover, no efficient way is provided to find an optimal cohesion parameter setting. In this paper, we propose a parameter-free community detection algorithm by synchronizing distances iteratively. In each iteration, the distance of each edge will change dynamically according to the effect generated by its related neighbours. Several iterations later, distances between vertices belonging to the same community will synchronize to 0, while distances between vertices not in the same community will synchronize to 1. Besides, merging and division strategies are built up in the process of community detection. Experiments on both real-world and synthetic networks demonstrate benefits of our method compared to the baseline methods.

Community detection by enhancing community structure in bipartite networks

2019 ◽  
Vol 33 (07) ◽  
pp. 1950076 ◽  
Author(s):  
Wenjie Zhou ◽  
Xingyuan Wang ◽  
Chuan Zhang ◽  
Rui Li ◽  
Chunpeng Wang
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Real World ◽  
State Of The Art ◽  
Detection Algorithm ◽  
Original Structure ◽  
Bipartite Networks ◽  
Detection Algorithms ◽  
Low Weight ◽  
Community Detection Algorithm

Community detection is one of the primary tools to discover useful information that is hidden in complex networks. Some community detection algorithms for bipartite networks have been proposed from various viewpoints. However, the performance of these algorithms deteriorates when the community structure becomes unclear. Enhancing community structure remains a nontrivial task. In this paper, we propose a community detection algorithm, called ECD, that enhances community structure in bipartite networks. In the proposed ECD, the topology of a network is modified by reducing unnecessary edges that are connected to neighboring low-weight communities. Therefore, an ambiguous community structure is converted into a structure that is much clearer than the original structure. The experimental results on both artificial and real-world networks verify the accuracy and reliability of our algorithm. Compared with existing community detection algorithms using state-of-the-art methods, our algorithm has better performance.

scholarly journals Community detection in complex networks using link prediction

2018 ◽  
Vol 32 (01) ◽  
pp. 1850004 ◽  
Author(s):  
Hui-Min Cheng ◽  
Yi-Zi Ning ◽  
Zhao Yin ◽  
Chao Yan ◽  
Xin Liu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Complex Networks ◽  
Community Detection ◽  
Link Prediction ◽  
Detection Algorithm ◽  
The Other ◽  
Data Sets ◽  
Topological Structures ◽  
Benchmark Data ◽  
Community Detection Algorithm

Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from different perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improving the accuracy of community partition. For link prediction, we propose two novel indices to compute the similarity between each pair of nodes, one of which aims to add missing links, and the other tries to remove spurious edges. Extensive experiments are conducted on benchmark data sets, and the results of our proposed algorithm are compared with two classes of baselines. In conclusion, our proposed algorithm is competitive, revealing that link prediction does improve the precision of community detection.

FluidC+: A novel community detection algorithm based on fluid propagation

2019 ◽  
Vol 30 (04) ◽  
pp. 1950021
Author(s):  
Jinfang Sheng ◽  
Kai Wang ◽  
Zejun Sun ◽  
Jie Hu ◽  
Bin Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Real World ◽  
Seed Set ◽  
State Of The Art ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Original Algorithm ◽  
Community Detection Algorithm ◽  
Better Than

In recent years, community detection has gradually become a hot topic in the complex network data mining field. The research of community detection is helpful not only to understand network topology structure but also to explore network hiding function. In this paper, we improve FluidC which is a novel community detection algorithm based on fluid propagation, by ameliorating the quality of seed set based on positive feedback and determining the node update order. We first summarize the shortcomings of FluidC and analyze the reasons result in these drawbacks. Then, we took some effective measures to overcome them and proposed an efficient community detection algorithm, called FluidC+. Finally, experiments on the generated network and real-world network show that our method not only greatly improves the performance of the original algorithm FluidC but also is better than many state-of-the-art algorithms, especially in the performance on real-world network with ground truth.

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