scholarly journals Community Detection of Dynamic Complex Networks in Stock Markets Using Hybrid Methods (RMT‐CN‐LPAm+ and RMT‐BDM‐SA)

2021 ◽  
Vol 8 ◽  
Author(s):  
Acep Purqon ◽  
Jamaludin
Keyword(s):  
Stock Market ◽  
Complex Networks ◽  
Community Detection ◽  
Matrix Theory ◽  
Hybrid Methods ◽  
Label Propagation ◽  
Block Diagonal Matrix ◽  
Formula Group ◽  
Dynamical Complex Networks ◽  
Two Hybrid

A stock market represents a large number of interacting elements, leading to complex hidden interactions. It is very challenging to find a useful method to detect the detailed dynamical complex networks involved in the interactions. For this reason, we propose two hybrid methods called RMT-CN-LPAm+ and RMT-BDM-SA (RMT, random matrix theory; CN, complex network; LPAm+, advanced label propagation algorithm; BDM, block diagonal matrix; SA, simulated annealing). In this study, we investigated group mapping in the S&P 500 stock market using these two hybrid methods. Our results showed the good performance of the proposed methods, with both the methods demonstrating their own benefits and strong points. For example, RMT-CN-LPAm+ successfully identified six groups comprising 485 involved nodes and 17 isolated nodes, with a maximum modularity of 0.62 (identified more groups and displayed more maximum modularity). Meanwhile, RMT-BDM-SA provided useful detailed information through the decomposition of matrix C into Cm (market-wide), Cg (group), and Cr (noise). Both hybrid methods successfully performed very detailed community detection of dynamic complex networks in the stock market.

scholarly journals Community Detection Based on a Preferential Decision Model

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Jinfang Sheng ◽  
Ben Lu ◽  
Bin Wang ◽  
Jie Hu ◽  
Kai Wang ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Networks ◽  
Community Detection ◽  
Decision Model ◽  
Structural Characteristics ◽  
Real Life ◽  
Stable State ◽  
Ground Truth ◽  
Label Propagation ◽  
Comparison Algorithms

The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

Label propagation algorithm based on edge clustering coefficient for community detection in complex networks

2014 ◽  
Vol 28 (30) ◽  
pp. 1450216 ◽  
Author(s):  
Xian-Kun Zhang ◽  
Xue Tian ◽  
Ya-Nan Li ◽  
Chen Song
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Learning Algorithm ◽  
Low Complexity ◽  
Label Propagation ◽  
Clustering Coefficient ◽  
Propagation Algorithm ◽  
The Stability ◽  
Stability And Accuracy ◽  
Edge Clustering Coefficient

The label propagation algorithm (LPA) is a graph-based semi-supervised learning algorithm, which can predict the information of unlabeled nodes by a few of labeled nodes. It is a community detection method in the field of complex networks. This algorithm is easy to implement with low complexity and the effect is remarkable. It is widely applied in various fields. However, the randomness of the label propagation leads to the poor robustness of the algorithm, and the classification result is unstable. This paper proposes a LPA based on edge clustering coefficient. The node in the network selects a neighbor node whose edge clustering coefficient is the highest to update the label of node rather than a random neighbor node, so that we can effectively restrain the random spread of the label. The experimental results show that the LPA based on edge clustering coefficient has made improvement in the stability and accuracy of the algorithm.

Distributed learning automata-based algorithm for community detection in complex networks

2016 ◽  
Vol 30 (08) ◽  
pp. 1650042 ◽  
Author(s):  
Mohammad Mehdi Daliri Khomami ◽  
Alireza Rezvanian ◽  
Mohammad Reza Meybodi
Keyword(s):  
Complex Networks ◽  
Community Detection ◽  
Learning Automata ◽  
Distributed Learning ◽  
Structure And Function ◽  
Label Propagation ◽  
Information Networks ◽  
Detection Algorithms ◽  
Distributed Learning Automata ◽  
And Function

Community structure is an important and universal topological property of many complex networks such as social and information networks. The detection of communities of a network is a significant technique for understanding the structure and function of networks. In this paper, we propose an algorithm based on distributed learning automata for community detection (DLACD) in complex networks. In the proposed algorithm, each vertex of network is equipped with a learning automation. According to the cooperation among network of learning automata and updating action probabilities of each automaton, the algorithm interactively tries to identify high-density local communities. The performance of the proposed algorithm is investigated through a number of simulations on popular synthetic and real networks. Experimental results in comparison with popular community detection algorithms such as walk trap, Danon greedy optimization, Fuzzy community detection, Multi-resolution community detection and label propagation demonstrated the superiority of DLACD in terms of modularity, NMI, performance, min-max-cut and coverage.

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