Find modules in signed networks based on modularity optimization

2019 ◽  
Vol 33 (32) ◽  
pp. 1950387
Author(s):  
Kai Yu ◽  
Yongping Yu ◽  
Lei Wu ◽  
Di Liu ◽  
Wenqiang Guo
Keyword(s):  
Complex Networks ◽  
High Efficiency ◽  
Detection Algorithm ◽  
Signed Networks ◽  
Module Detection ◽  
Signed Network ◽  
Hostile Attitude

The signed network depicts individual cooperative or hostile attitude in a system. It is very important to study the characteristics of complex networks and predict individual attitudes by analyzing the attitudes of individuals and their neighbors, which can divide individuals into different modules or communities. To detect the modules in signed networks, first, a modularity function for signed networks is utilized on the basis of the existing modularity function. Then, a new module detection algorithm for signed networks has also been put forward, which has high efficiency. Finally, the algorithm has been applied on both artificial and real networks. The results show that the number of modules given by our proposed algorithm is consistent with that of the number of actual modules.

An efficient graph clustering algorithm in signed graph based on modularity maximization

2019 ◽  
Vol 30 (11) ◽  
pp. 1950095
Author(s):  
Kefan Zhuo ◽  
Zhuoxuan Yang ◽  
Guan Yan ◽  
Kai Yu ◽  
Wenqiang Guo
Keyword(s):  
Complex Networks ◽  
Clustering Algorithm ◽  
High Efficiency ◽  
Graph Clustering ◽  
Signed Graph ◽  
Signed Graphs ◽  
Negative Part ◽  
Signed Networks ◽  
Modularity Maximization ◽  
Negative Links

The unsigned graphs containing positive links only, have been analyzed fruitfully. However, the physical relations behind complex networks are dissimilar. We often encounter the signed networks that have both positive and negative links as well. It is very important to study the characteristics of complex networks and predict individual attitudes by analyzing the attitudes of individuals and their neighbors, which can divide individuals into different clusters or communities. To detect the clusters in signed networks, first, a modularity function for signed networks is proposed on the basis of the combination of positive and negative part. Then, a new graph clustering algorithm for signed graphs has also been proposed based on CNM algorithm, which has high efficiency. Finally, the algorithm has been applied on both artificial and the real networks. The results show that the proposed method has been able to achieve near-perfect solution, which is suitable for multiple types real networks.

scholarly journals Modelling community structure and temporal spreading on complex networks

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Vesa Kuikka
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Network Structure ◽  
Network Connectivity ◽  
Network Models ◽  
Building Blocks ◽  
Detection Algorithm ◽  
Research Area ◽  
Detection Methods

AbstractWe present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different sub-communities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.

Determination of tonal signal parameters based on zero crossing detection

2021 ◽  
Vol 263 (5) ◽  
pp. 1794-1803
Author(s):  
Michal Luczynski ◽  
Stefan Brachmanski ◽  
Andrzej Dobrucki
Keyword(s):  
High Efficiency ◽  
Detection Algorithm ◽  
Sine Wave ◽  
Time Interval ◽  
Zero Crossing ◽  
Tonal Signal ◽  
Zero Crossings ◽  
Tonal Component ◽  
Signal Parameters ◽  
The Moment

This paper presents a method for identifying tonal signal parameters using zero crossing detection. The signal parameters: frequency, amplitude and phase can change slowly in time. The described method allows to obtain accurate detection using possibly small number of signal samples. The detection algorithm consists of the following steps: frequency filtering, zero crossing detection and parameter reading. Filtering of the input signal is aimed at obtaining a signal consisting of a single tonal component. Zero crossing detection allows the elimination of multiple random zero crossings, which do not occur in a pure sine wave signal. The frequency is based on the frequency of transitions through zero, the amplitude is the largest value of the signal in the analysed time interval, and the initial phase is derived from the moment at which the transition through zero occurs. The obtained parameters were used to synthesise a compensation signal in an active tonal component reduction algorithm. The results of the algorithm confirmed the high efficiency of the method.

The necessary and sufficient condition for clustering of nodes based on the signs of connections in generalized signed networks

2019 ◽  
Vol 33 (10) ◽  
pp. 1950086
Author(s):  
Qi Wang ◽  
Yinhe Wang ◽  
Zilin Gao ◽  
Lili Zhang ◽  
Wenli Wang
Keyword(s):  
Clustering Algorithms ◽  
Necessary Condition ◽  
Sufficient Condition ◽  
Necessary And Sufficient Condition ◽  
Signed Networks ◽  
Clustering Problem ◽  
Structural Hole ◽  
Signed Network ◽  
Necessary And Sufficient ◽  
Sufficient And Necessary Condition

This paper investigates the clustering problem for the generalized signed networks. By rigorous derivations, a sufficient and necessary condition for clustering of the nodes in generalized signed networks is proposed in this paper. In order to obtain this condition, the concept of friends group is first introduced for the nodes based on their links’ sign. Then, the unprivileged network is also defined in this paper by employing the concepts of structural hole and broker. Compared with the existing clustering algorithms, the outstanding advantage in this paper is that only the positive or negative (especially, or zero) sign of the links is required regardless of their density or sparsity. We have proved mathematically that a generalized signed network is classifiable if and only if it is an unprivileged network. Finally, two examples associated with numerical simulations are proposed to generate the unprivileged networks.

scholarly journals SignRank: A Novel Random Walking Based Ranking Algorithm in Signed Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Cong Wan ◽  
Yanhui Fang ◽  
Cong Wang ◽  
Yanxia Lv ◽  
Zejie Tian ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Positive Emotion ◽  
Ranking Algorithm ◽  
Random Walking ◽  
Signed Networks ◽  
Ranking Algorithms ◽  
Negative Score ◽  
Positive Score ◽  
Signed Network

Social networks have become an indispensable part of modern life. Signed networks, a class of social network with positive and negative edges, are becoming increasingly important. Many social networks have adopted the use of signed networks to model like (trust) or dislike (distrust) relationships. Consequently, how to rank nodes from positive and negative views has become an open issue of social network data mining. Traditional ranking algorithms usually separate the signed network into positive and negative graphs so as to rank positive and negative scores separately. However, much global information of signed network gets lost during the use of such methods, e.g., the influence of a friend’s enemy. In this paper, we propose a novel ranking algorithm that computes a positive score and a negative score for each node in a signed network. We introduce a random walking model for signed network which considers the walker has a negative or positive emotion. The steady state probability of the walker visiting a node with negative or positive emotion represents the positive score or negative score. In order to evaluate our algorithm, we use it to solve sign prediction problem, and the result shows that our algorithm has a higher prediction accuracy compared with some well-known ranking algorithms.

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