Topic Editors

Dr. Jie Meng
Institute for Digital Technologies, Loughborough University, Loughborough E20 3BS, UK
Department of Computer Science, University of Liverpool, Liverpool L69 3BX, UK
Prof. Dr. Minghui Qian
School of Information Resource Management, Renmin University of China, Beijing 100872, China
Dr. Zhixuan Xu
School of Business Administration, Capital University of Economics and Business, Beijing, China

Complex Networks and Social Networks

Abstract submission deadline
closed (31 May 2024)
Manuscript submission deadline
closed (31 July 2024)
Viewed by
16481

Topic Information

Dear Colleagues,

Complex networks and social networks represent a topic of significant interest in the mathematical prediction and calculation to solve various social network problems via the perspective and approach of complex adaptive systems. Even since the boom of this school of methods in the 2000s, complex networks have attracted extensive research endeavors. We have seen fruitful results in several aspects of its prevailing functionalities, for example, prediction, dynamic modeling and experimentation in engineering, information sciences, social sciences, and economics/business studies. In particular, this stream of research is highlighted for its capability to rule out the occurrence of some unexpected outcomes which are usually unable to derive directly from linear modeling methods (e.g., statistical regression), while in many applications, the emergence of the expected pattern is under the spotlight of investigation, with either promising or concerning outcomes. Examples include information cascade on social media, tipping points of public opinions in media hype, coordinated deficiency in a complex system causing tremendous disaster in security or humanity, and so forth. We are strongly interested in receiving multidisciplinary research manuscripts which focus on the application of emerging networks inclusive of information networks, human–machine networks, social media networks, net-zero energy networks, and other automated connected networks for security.

Dr. Jie Meng
Prof. Dr. Xiaowei Huang
Prof. Dr. Minghui Qian
Dr. Zhixuan Xu
Topic Editors

Keywords

  • multilevel analysis
  • multiagents
  • calculative simulation
  • complex adaptive system
  • emerging patterns
  • tipping points
  • structural changes

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Algorithms
algorithms
1.8 4.1 2008 15 Days CHF 1600
Computation
computation
1.9 3.5 2013 19.7 Days CHF 1800
Information
information
2.4 6.9 2010 14.9 Days CHF 1600
Mathematics
mathematics
2.3 4.0 2013 17.1 Days CHF 2600

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Published Papers (11 papers)

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22 pages, 2570 KiB  
Article
Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure
by Tongyu Wu, Minjie Li and Shuangjiao Lin
Algorithms 2024, 17(9), 414; https://doi.org/10.3390/a17090414 - 19 Sep 2024
Viewed by 765
Abstract
One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare [...] Read more.
One of the most challenging issues in contemporary complex network research is to understand the structure and vulnerability of multilayer networks, even though cascading failures in single networks have been widely studied in recent years. The goal of this work is to compare the similarities and differences between four single layers and understand the implications of interdependencies among cities on the overall vulnerability of a multilayer global logistics network. In this paper, a global logistics network model set as a multilayer network considering cascading failures is proposed in different disruption scenarios. Two types of attack strategies—a highest load attack and a lowest load attack—are used to evaluate the vulnerability of the global logistics network and to further analyze the changes in the topology properties. For a multilayer network, the vulnerability of single layers is compared as well. The results suggest that compared with the results of a single global logistics network, a multilayer network has a higher vulnerability. In addition, the heterogeneity of networks plays an important role in the vulnerability of a multilayer network against targeted attacks. Protecting the most important nodes is critical to safeguard the potential “vulnerability” in the development of the global logistics network. The three-step response strategy of “Prewarning–Response–Postrepair” is the main pathway to improving the adjustment ability and adaptability of logistics hub cities in response to external shocks. These findings supplement and extend the previous attack results on nodes and can thus help us better explain the vulnerability of different networks and provide insight into more tolerant, real, complex system designs. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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14 pages, 371 KiB  
Article
Complex Network Model of Global Financial Time Series Based on Different Distance Functions
by Zhen Wang, Jicai Ning and Meng Gao
Mathematics 2024, 12(14), 2210; https://doi.org/10.3390/math12142210 - 15 Jul 2024
Cited by 1 | Viewed by 726
Abstract
By constructing a complex network model grounded in time series analysis, this study delves into the intricate relationships between the stock markets of 18 countries worldwide. Utilizing 31 distinct time series distance functions to formulate the network, we employ Hamming distance to quantify [...] Read more.
By constructing a complex network model grounded in time series analysis, this study delves into the intricate relationships between the stock markets of 18 countries worldwide. Utilizing 31 distinct time series distance functions to formulate the network, we employ Hamming distance to quantify the resemblance between networks derived from different distance functions. By modulating the network density through distance percentiles (p=0.1, 0.3, 0.5), we demonstrate the similarity of various distance functions across multiple density levels. Our findings reveal that certain distance functions exhibit high degrees of similarity across varying network densities, suggesting their potential for mutual substitution in network construction. Furthermore, the centroid network identified via hierarchical cluster analysis highlights the similarities between the stock markets of different nations, mirroring the intricate interconnections within the global financial landscape. The insights gained from this study offer crucial perspectives for comprehending the intricate network structure of global financial time series data, paving the way for further analysis and prediction of global financial market dynamics. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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13 pages, 1010 KiB  
Article
Static Bipartite Consensus Problems of Heterogeneous Signed Networks
by Yu Ma, Yi Zhang, Jinchao Li, Mingjun Du and Peng Ji
Mathematics 2024, 12(10), 1523; https://doi.org/10.3390/math12101523 - 14 May 2024
Viewed by 766
Abstract
This paper aims to study the distributed control problems of heterogeneous signed networks whose communication topologies are undirected. A distributed control protocol is designed based on neighboring state information. With this protocol be employed, the convergence results of the heterogeneous signed network are [...] Read more.
This paper aims to study the distributed control problems of heterogeneous signed networks whose communication topologies are undirected. A distributed control protocol is designed based on neighboring state information. With this protocol be employed, the convergence results of the heterogeneous signed network are provided. It is shown that the heterogeneous signed network can achieve the static bipartite consensus (respectively, state stability) if and only if the signed graph is structurally balanced (respectively, unbalanced). The associated convergence analyses can be developed by constructing a suitable Lyapunov function. In addition, two simulation examples are presented to validate the correctness of the obtained results. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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15 pages, 499 KiB  
Article
Revolutionary Strategy for Depicting Knowledge Graphs with Temporal Attributes
by Sihan Li and Qi Li
Mathematics 2024, 12(9), 1324; https://doi.org/10.3390/math12091324 - 26 Apr 2024
Viewed by 863
Abstract
In practical applications, the temporal completeness of knowledge graphs is of great importance. However, previous studies have mostly focused on static knowledge graphs, generally neglecting the dynamic evolutionary properties of facts. Moreover, the unpredictable and limited availability of temporal knowledge graphs, together with [...] Read more.
In practical applications, the temporal completeness of knowledge graphs is of great importance. However, previous studies have mostly focused on static knowledge graphs, generally neglecting the dynamic evolutionary properties of facts. Moreover, the unpredictable and limited availability of temporal knowledge graphs, together with the complex temporal dependency patterns, make current models inadequate for effectively describing facts that experience temporal transitions. To better represent the evolution of things over time, we provide a learning technique that uses quaternion rotation to describe temporal knowledge graphs. This technique describes the evolution of entities as a temporal rotation change in quaternion space. Compared to the Ermitian inner product in complex number space, the Hamiltonian product in quaternion space is better at showing how things might be connected. This leads to a learning process that is both more effective and more articulate. Experimental results demonstrate that our learning method significantly outperforms existing methods in capturing the dynamic evolution of temporal knowledge graphs, with improved accuracy and robustness across a range of benchmark datasets. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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16 pages, 460 KiB  
Article
Bipartite Consensus Problems for Directed Signed Networks with External Disturbances
by Baoyu Huo, Jian Ma and Mingjun Du
Mathematics 2023, 11(23), 4828; https://doi.org/10.3390/math11234828 - 30 Nov 2023
Viewed by 1120
Abstract
The intention of this paper is to explore the distributed control issues for directed signed networks in the face of external disturbances under strongly connected topologies. A new class of nonsingular transformations is provided by introducing an output variable, with which the consensus [...] Read more.
The intention of this paper is to explore the distributed control issues for directed signed networks in the face of external disturbances under strongly connected topologies. A new class of nonsingular transformations is provided by introducing an output variable, with which the consensus can be equivalently transformed into the output stability regardless of whether the associated signed digraphs are structurally balanced or not. By taking advantage of the standard robust H control theory, the bipartite consensus and state stability results can be built for signed networks under structurally balanced and unbalanced conditions, respectively, in which the desired disturbance rejection performances can also be satisfied. Furthermore, the mathematical expression can be given for the terminal states of signed networks under the influence of external disturbances. In addition, two simulations are presented to verify the correctness of our developed results. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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22 pages, 1036 KiB  
Article
Logical–Mathematical Foundations of a Graph Query Framework for Relational Learning
by Pedro Almagro-Blanco, Fernando Sancho-Caparrini and Joaquín Borrego-Díaz
Mathematics 2023, 11(22), 4672; https://doi.org/10.3390/math11224672 - 16 Nov 2023
Viewed by 1088
Abstract
Relational learning has attracted much attention from the machine learning community in recent years, and many real-world applications have been successfully formulated as relational learning problems. In recent years, several relational learning algorithms have been introduced that follow a pattern-based approach. However, this [...] Read more.
Relational learning has attracted much attention from the machine learning community in recent years, and many real-world applications have been successfully formulated as relational learning problems. In recent years, several relational learning algorithms have been introduced that follow a pattern-based approach. However, this type of learning model suffers from two fundamental problems: the computational complexity arising from relational queries and the lack of a robust and general framework to serve as the basis for relational learning methods. In this paper, we propose an efficient graph query framework that allows for cyclic queries in polynomial time and is ready to be used in pattern-based learning methods. This solution uses logical predicates instead of graph isomorphisms for query evaluation, reducing complexity and allowing for query refinement through atomic operations. The main differences between our method and other previous pattern-based graph query approaches are the ability to evaluate arbitrary subgraphs instead of nodes or complete graphs, the fact that it is based on mathematical formalization that allows the study of refinements and their complementarity, and the ability to detect cyclic patterns in polynomial time. Application examples show that the proposed framework allows learning relational classifiers to be efficient in generating data with high expressiveness capacities. Specifically, relational decision trees are learned from sets of tagged subnetworks that provide both classifiers and characteristic patterns for the identified classes. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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19 pages, 1222 KiB  
Article
Discrete versus Continuous Algorithms in Dynamics of Affective Decision Making
by Vyacheslav I. Yukalov and Elizaveta P. Yukalova
Algorithms 2023, 16(9), 416; https://doi.org/10.3390/a16090416 - 29 Aug 2023
Viewed by 1635
Abstract
The dynamics of affective decision making is considered for an intelligent network composed of agents with different types of memory: long-term and short-term memory. The consideration is based on probabilistic affective decision theory, which takes into account the rational utility of alternatives as [...] Read more.
The dynamics of affective decision making is considered for an intelligent network composed of agents with different types of memory: long-term and short-term memory. The consideration is based on probabilistic affective decision theory, which takes into account the rational utility of alternatives as well as the emotional alternative attractiveness. The objective of this paper is the comparison of two multistep operational algorithms of the intelligent network: one based on discrete dynamics and the other on continuous dynamics. By means of numerical analysis, it is shown that, depending on the network parameters, the characteristic probabilities for continuous and discrete operations can exhibit either close or drastically different behavior. Thus, depending on which algorithm is employed, either discrete or continuous, theoretical predictions can be rather different, which does not allow for a uniquely defined description of practical problems. This finding is important for understanding which of the algorithms is more appropriate for the correct analysis of decision-making tasks. A discussion is given, revealing that the discrete operation seems to be more realistic for describing intelligent networks as well as affective artificial intelligence. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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12 pages, 410 KiB  
Article
Subdomination in Graphs with Upper-Bounded Vertex Degree
by Darya Lemtyuzhnikova, Pavel Chebotarev, Mikhail Goubko, Ilja Kudinov and Nikita Shushko
Mathematics 2023, 11(12), 2722; https://doi.org/10.3390/math11122722 - 15 Jun 2023
Cited by 1 | Viewed by 1089
Abstract
We find a lower bound for the k-subdomination number on the set of graphs with a given upper bound for vertex degrees. We study the cases where the proposed lower bound is sharp, construct the optimal graphs and indicate the corresponding k [...] Read more.
We find a lower bound for the k-subdomination number on the set of graphs with a given upper bound for vertex degrees. We study the cases where the proposed lower bound is sharp, construct the optimal graphs and indicate the corresponding k-subdominating functions. The results are interpreted in terms of social structures. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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16 pages, 725 KiB  
Article
Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure
by Yinghui Wang, Wenjun Wang, Minglai Shao and Yueheng Sun
Algorithms 2023, 16(5), 234; https://doi.org/10.3390/a16050234 - 28 Apr 2023
Viewed by 1759
Abstract
Network alignment (NA) offers a comprehensive way to build associations between different networks by identifying shared nodes. While the majority of current NA methods rely on the topological consistency assumption, which posits that shared nodes across different networks typically have similar local structures [...] Read more.
Network alignment (NA) offers a comprehensive way to build associations between different networks by identifying shared nodes. While the majority of current NA methods rely on the topological consistency assumption, which posits that shared nodes across different networks typically have similar local structures or neighbors, we argue that anchor nodes, which play a pivotal role in NA, face a more challenging scenario that is often overlooked. In this paper, we conduct extensive statistical analysis across networks to investigate the connection status of labeled anchor node pairs and categorize them into four situations. Based on our analysis, we propose an end-to-end network alignment framework that uses node representations as a distribution rather than a point vector to better handle the structural diversity of networks. To mitigate the influence of specific nodes, we introduce a mask mechanism during the representation learning process. In addition, we utilize meta-learning to generalize the learned information on labeled anchor node pairs to other node pairs. Finally, we perform comprehensive experiments on both real-world and synthetic datasets to confirm the efficacy of our proposed method. The experimental results demonstrate that the proposed model outperforms the state-of-the-art methods significantly. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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29 pages, 2155 KiB  
Article
Stability, Hopf Bifurcation and Optimal Control of Multilingual Rumor-Spreading Model with Isolation Mechanism
by Shuzhen Yu, Zhiyong Yu and Haijun Jiang
Mathematics 2022, 10(23), 4556; https://doi.org/10.3390/math10234556 - 1 Dec 2022
Cited by 2 | Viewed by 1563
Abstract
The propagation of rumors on online social networks (OSNs) brings an awful lot of trouble to people’s life and society. Aiming at combating rumors spreading on OSNs, two novel rumor-propagation models without and with time delays are proposed, which combine with the influence [...] Read more.
The propagation of rumors on online social networks (OSNs) brings an awful lot of trouble to people’s life and society. Aiming at combating rumors spreading on OSNs, two novel rumor-propagation models without and with time delays are proposed, which combine with the influence of the immune mechanism, isolation mechanism and network structure. Firstly, we analyze the existence of rumor equilibria and obtain some existence conditions of backward bifurcation. Secondly, the local stabilities of rumor-free and rumor equilibria are proved by using the Jacobian matrix method, and some critical conditions for the existence of Hopf bifurcation are acquired by selecting critical parameters and delays as bifurcation parameters. Furthermore, an optimal control method is proposed, which can prevent the spread of rumors within an expected time period and minimize the cost of control. Finally, some numerical simulations are provided to verify the effectiveness of the proposed theoretical results. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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14 pages, 412 KiB  
Article
Monitoring Sparse and Attributed Network Streams with MultiLevel and Dynamic Structures
by Mostafa Mostafapour, Farzad Movahedi Sobhani and Abbas Saghaei
Mathematics 2022, 10(23), 4483; https://doi.org/10.3390/math10234483 - 28 Nov 2022
Cited by 1 | Viewed by 1440
Abstract
In this study, we create a new monitoring system for change detection in sparse attributed network streams with multilevel or nested dynamic structures. To achieve this, we hypothesize that the contingency of establishing an edge between two network nodes at time t depends [...] Read more.
In this study, we create a new monitoring system for change detection in sparse attributed network streams with multilevel or nested dynamic structures. To achieve this, we hypothesize that the contingency of establishing an edge between two network nodes at time t depends on the properties of the network edges, network nodes, groups, or categories. Then, we estimate the model parameters using the expressed logit model. The model parameters are developed using the state-space model to achieve a dynamic state in the system. The extended Kalman filter (EKF) updates state-space parameters and predicts upcoming networks. Predicted residuals are tracked using statistical process control charts to identify changes in the underlying mechanism of edge generation. This research makes a methodological contribution by combining zero-inflated generalized linear mixed models (ZI-GLMMs) with the state-space model to monitor changes in the sequences of sparse, attributed, and weighted multilevel networks by applying control charts. The proposed model is compared to previous models to evaluate performance by implementing three scenarios. The results show that the model is faster at detecting the first change. Finally, using real e-MID data, we measured the model’s performance in detecting real data changes. The findings suggest that the proposed model could predict a crisis in advance of significant European Central Bank statements and events. Full article
(This article belongs to the Topic Complex Networks and Social Networks)
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