Complex Networks and Social Networks

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

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Algorithms algorithms	2.3	3.7	2008	15 Days	CHF 1600	Submit
Computation computation	2.2	3.3	2013	18 Days	CHF 1800	Submit
Information information	3.1	5.8	2010	18 Days	CHF 1600	Submit
Mathematics mathematics	2.4	3.5	2013	16.9 Days	CHF 2600	Submit

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Published Papers (7 papers)

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All Algorithms Computation Information Mathematics

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16 pages, 460 KiB  

Open AccessArticle

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 662

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 $H_{\infty }$ 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  

Open AccessArticle

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 598

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  

Open AccessArticle

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 1201

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  

Open AccessArticle

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 736

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  

Open AccessArticle

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 1182

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  

Open AccessArticle

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 - 01 Dec 2022

Cited by 1 | Viewed by 1089

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  

Open AccessArticle

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 1074

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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