1
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Panditrao G, Bhowmick R, Meena C, Sarkar RR. Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects. J Biosci 2022. [PMID: 36210749 PMCID: PMC9018971 DOI: 10.1007/s12038-022-00253-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.
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Affiliation(s)
- Gauri Panditrao
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Rupa Bhowmick
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
| | - Chandrakala Meena
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
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2
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Yan W, Hu G, Liang Z, Zhou J, Yang Y, Chen J, Shen B. Node-Weighted Amino Acid Network Strategy for Characterization and Identification of Protein Functional Residues. J Chem Inf Model 2018; 58:2024-2032. [PMID: 30107728 DOI: 10.1021/acs.jcim.8b00146] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The study of functional residues (FRs) is essential for understanding protein functions and biological processes. The amino acid network (AAN) has become an emerging paradigm for studying FRs during the past decade. Current AAN models ignore the heterogeneity of nodes and treat amino acids in the AAN as the same. However, the properties of each amino acid node are of fundamental importance. We here proposed a node-weighted AAN strategy termed the node-weighted amino acid contact energy network (NACEN) to characterize and predict three types of FRs, namely, hot spots, catalytic residues, and allosteric residues. We first constructed NACENs with their nodes weighted based on structural, sequence, physicochemical, and dynamical properties of the amino acids and then characterized the FRs with the NACEN parameters. We finally built machine learning predictors to identify each type of FR. The results revealed that residues characterized with NACEN parameters are more distinguishable between FRs and non-FRs than those with unweighted network ones. With few features for classification, NACEN yields comparable performance for FR identification and provides residue level prediction for allosteric regulation. The proposed strategy can be easily implemented to other functional residue identification. An R package is also provided for NACEN construction and analysis at http://sysbio.suda.edu.cn/NACEN/index.html .
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Affiliation(s)
- Wenying Yan
- Center for systems biology , Soochow University , Suzhou 215006 , China
| | - Guang Hu
- Center for systems biology , Soochow University , Suzhou 215006 , China
| | - Zhongjie Liang
- Center for systems biology , Soochow University , Suzhou 215006 , China
| | - Jianhong Zhou
- Center for systems biology , Soochow University , Suzhou 215006 , China
| | - Yang Yang
- School of computer science and technology , Soochow University , Suzhou 215006 , China
| | - Jiajia Chen
- School of Chemistry, Biology and Material Engineering , Suzhou University of Science and Technology , Suzhou 215011 , China
| | - Bairong Shen
- Center for systems biology , Soochow University , Suzhou 215006 , China
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3
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Jalili M, Salehzadeh-Yazdi A, Gupta S, Wolkenhauer O, Yaghmaie M, Resendis-Antonio O, Alimoghaddam K. Evolution of Centrality Measurements for the Detection of Essential Proteins in Biological Networks. Front Physiol 2016; 7:375. [PMID: 27616995 PMCID: PMC4999434 DOI: 10.3389/fphys.2016.00375] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Accepted: 08/12/2016] [Indexed: 02/02/2023] Open
Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences Tehran, Iran
| | - Ali Salehzadeh-Yazdi
- Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical SciencesTehran, Iran; Department of Systems Biology and Bioinformatics, University of RostockRostock, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of RostockRostock, Germany; CSIR-Indian Institute of Toxicology ResearchLucknow, India
| | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock Rostock, Germany
| | - Marjan Yaghmaie
- Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences Tehran, Iran
| | | | - Kamran Alimoghaddam
- Hematology, Oncology and Stem Cell Transplantation Research Center, Tehran University of Medical Sciences Tehran, Iran
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Liu JG, Lin JH, Guo Q, Zhou T. Locating influential nodes via dynamics-sensitive centrality. Sci Rep 2016; 6:21380. [PMID: 26905891 PMCID: PMC4764903 DOI: 10.1038/srep21380] [Citation(s) in RCA: 88] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2015] [Accepted: 01/22/2016] [Indexed: 12/03/2022] Open
Abstract
With great theoretical and practical significance, locating influential nodes of complex networks is a promising issue. In this paper, we present a dynamics-sensitive (DS) centrality by integrating topological features and dynamical properties. The DS centrality can be directly applied in locating influential spreaders. According to the empirical results on four real networks for both susceptible-infected-recovered (SIR) and susceptible-infected (SI) spreading models, the DS centrality is more accurate than degree, k-shell index and eigenvector centrality.
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Affiliation(s)
- Jian-Guo Liu
- Data Science and Cloud Service Centre, Shanghai University of Finance and Economics, Shanghai 200433, PR China
- Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China
| | - Jian-Hong Lin
- Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China
| | - Qiang Guo
- Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, PR China
| | - Tao Zhou
- CompleX Lab, Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 611731, PR China
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5
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Collective punishment is more effective than collective reward for promoting cooperation. Sci Rep 2015; 5:17752. [PMID: 26634907 PMCID: PMC4669458 DOI: 10.1038/srep17752] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2015] [Accepted: 10/16/2015] [Indexed: 11/17/2022] Open
Abstract
Collective punishment and reward are usually regarded as two potential mechanisms to explain the evolution of cooperation. Both scenarios, however, seem problematic to understand cooperative behavior, because they can raise the second-order free-rider problem and many organisms are not able to discriminate less cooperating individuals. Even though they have been proved to increase cooperation, there has been a debate about which one being more effective. To address this issue, we resort to the N-player evolutionary snowdrift game (NESG), where a collective punishment/reward mechanism is added by allowing some players to display punishment/reward towards all remaining players. By means of numerous simulations and analyses, we find that collective punishment is more effective in promoting cooperation for a relatively high initial frequency of cooperation or for a relatively small group. When the intensity of punishment exceeds a certain threshold, a stable state of full cooperation emerges for both small and large groups. In contrast, such state does not appear for large groups playing a NESG with reward mechanism. In the case of mutualistic interactions, finally, our results show the new payoff with collective punishment/reward can lead to the coexistence of cooperators and defectors when discrimination between these two is not possible.
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Jalili M, Salehzadeh-Yazdi A, Asgari Y, Arab SS, Yaghmaie M, Ghavamzadeh A, Alimoghaddam K. CentiServer: A Comprehensive Resource, Web-Based Application and R Package for Centrality Analysis. PLoS One 2015; 10:e0143111. [PMID: 26571275 PMCID: PMC4646361 DOI: 10.1371/journal.pone.0143111] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2015] [Accepted: 11/01/2015] [Indexed: 12/15/2022] Open
Abstract
Various disciplines are trying to solve one of the most noteworthy queries and broadly used concepts in biology, essentiality. Centrality is a primary index and a promising method for identifying essential nodes, particularly in biological networks. The newly created CentiServer is a comprehensive online resource that provides over 110 definitions of different centrality indices, their computational methods, and algorithms in the form of an encyclopedia. In addition, CentiServer allows users to calculate 55 centralities with the help of an interactive web-based application tool and provides a numerical result as a comma separated value (csv) file format or a mapped graphical format as a graph modeling language (GML) file. The standalone version of this application has been developed in the form of an R package. The web-based application (CentiServer) and R package (centiserve) are freely available at http://www.centiserver.org/
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Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Salehzadeh-Yazdi
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Yazdan Asgari
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Seyed Shahriar Arab
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Marjan Yaghmaie
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Ardeshir Ghavamzadeh
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Kamran Alimoghaddam
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
- * E-mail:
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7
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Universal scaling for the dilemma strength in evolutionary games. Phys Life Rev 2015; 14:1-30. [PMID: 25979121 DOI: 10.1016/j.plrev.2015.04.033] [Citation(s) in RCA: 345] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Revised: 04/20/2015] [Accepted: 04/20/2015] [Indexed: 11/24/2022]
Abstract
Why would natural selection favor the prevalence of cooperation within the groups of selfish individuals? A fruitful framework to address this question is evolutionary game theory, the essence of which is captured in the so-called social dilemmas. Such dilemmas have sparked the development of a variety of mathematical approaches to assess the conditions under which cooperation evolves. Furthermore, borrowing from statistical physics and network science, the research of the evolutionary game dynamics has been enriched with phenomena such as pattern formation, equilibrium selection, and self-organization. Numerous advances in understanding the evolution of cooperative behavior over the last few decades have recently been distilled into five reciprocity mechanisms: direct reciprocity, indirect reciprocity, kin selection, group selection, and network reciprocity. However, when social viscosity is introduced into a population via any of the reciprocity mechanisms, the existing scaling parameters for the dilemma strength do not yield a unique answer as to how the evolutionary dynamics should unfold. Motivated by this problem, we review the developments that led to the present state of affairs, highlight the accompanying pitfalls, and propose new universal scaling parameters for the dilemma strength. We prove universality by showing that the conditions for an ESS and the expressions for the internal equilibriums in an infinite, well-mixed population subjected to any of the five reciprocity mechanisms depend only on the new scaling parameters. A similar result is shown to hold for the fixation probability of the different strategies in a finite, well-mixed population. Furthermore, by means of numerical simulations, the same scaling parameters are shown to be effective even if the evolution of cooperation is considered on the spatial networks (with the exception of highly heterogeneous setups). We close the discussion by suggesting promising directions for future research including (i) how to handle the dilemma strength in the context of co-evolution and (ii) where to seek opportunities for applying the game theoretical approach with meaningful impact.
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Bauer CR, Li S, Siegal ML. Essential gene disruptions reveal complex relationships between phenotypic robustness, pleiotropy, and fitness. Mol Syst Biol 2015; 11:773. [PMID: 25609648 PMCID: PMC4332149 DOI: 10.15252/msb.20145264] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
The concept of robustness in biology has gained much attention recently, but a mechanistic understanding of how genetic networks regulate phenotypic variation has remained elusive. One approach to understand the genetic architecture of variability has been to analyze dispensable gene deletions in model organisms; however, the most important genes cannot be deleted. Here, we have utilized two systems in yeast whereby essential genes have been altered to reduce expression. Using high-throughput microscopy and image analysis, we have characterized a large number of morphological phenotypes, and their associated variation, for the majority of essential genes in yeast. Our results indicate that phenotypic robustness is more highly dependent upon the expression of essential genes than on the presence of dispensable genes. Morphological robustness appears to be a general property of a genotype that is closely related to pleiotropy. While the fitness profile across a range of expression levels is idiosyncratic to each gene, the global pattern indicates that there is a window in which phenotypic variation can be released before fitness effects are observable.
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Affiliation(s)
- Christopher R Bauer
- Department of Biology, NYU Center for Genomics and Systems Biology, New York, NY, USA
| | - Shuang Li
- Department of Biology, NYU Center for Genomics and Systems Biology, New York, NY, USA
| | - Mark L Siegal
- Department of Biology, NYU Center for Genomics and Systems Biology, New York, NY, USA
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Chen X, Perc M. Optimal distribution of incentives for public cooperation in heterogeneous interaction environments. Front Behav Neurosci 2014; 8:248. [PMID: 25100959 PMCID: PMC4107675 DOI: 10.3389/fnbeh.2014.00248] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2014] [Accepted: 06/30/2014] [Indexed: 02/05/2023] Open
Abstract
In the framework of evolutionary games with institutional reciprocity, limited incentives are at disposal for rewarding cooperators and punishing defectors. In the simplest case, it can be assumed that, depending on their strategies, all players receive equal incentives from the common pool. The question arises, however, what is the optimal distribution of institutional incentives? How should we best reward and punish individuals for cooperation to thrive? We study this problem for the public goods game on a scale-free network. We show that if the synergetic effects of group interactions are weak, the level of cooperation in the population can be maximized simply by adopting the simplest “equal distribution” scheme. If synergetic effects are strong, however, it is best to reward high-degree nodes more than low-degree nodes. These distribution schemes for institutional rewards are independent of payoff normalization. For institutional punishment, however, the same optimization problem is more complex, and its solution depends on whether absolute or degree-normalized payoffs are used. We find that degree-normalized payoffs require high-degree nodes be punished more lenient than low-degree nodes. Conversely, if absolute payoffs count, then high-degree nodes should be punished stronger than low-degree nodes.
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Affiliation(s)
- Xiaojie Chen
- School of Mathematical Sciences, University of Electronic Science and Technology of China Chengdu, China
| | - Matjaž Perc
- Department of Physics, Faculty of Natural Sciences and Mathematics, University of Maribor Maribor, Slovenia ; CAMTP - Center for Applied Mathematics and Theoretical Physics, University of Maribor Maribor, Slovenia
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Wang Z, Wang L, Perc M. Degree mixing in multilayer networks impedes the evolution of cooperation. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2014; 89:052813. [PMID: 25353850 DOI: 10.1103/physreve.89.052813] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Indexed: 05/05/2023]
Abstract
Traditionally, the evolution of cooperation has been studied on single, isolated networks. Yet a player, especially in human societies, will typically be a member of many different networks, and those networks will play different roles in the evolutionary process. Multilayer networks are therefore rapidly gaining on popularity as the more apt description of a networked society. With this motivation, we here consider two-layer scale-free networks with all possible combinations of degree mixing, wherein one network layer is used for the accumulation of payoffs and the other is used for strategy updating. We find that breaking the symmetry through assortative mixing in one layer and/or disassortative mixing in the other layer, as well as preserving the symmetry by means of assortative mixing in both layers, impedes the evolution of cooperation. We use degree-dependent distributions of strategies and cluster-size analysis to explain these results, which highlight the importance of hubs and the preservation of symmetry between multilayer networks for the successful resolution of social dilemmas.
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Affiliation(s)
- Zhen Wang
- Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong and Center for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Center for Nonlinear and Complex Systems, Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Lin Wang
- Centre for Chaos and Complex Networks, Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia
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Jiang LL, Perc M. Spreading of cooperative behaviour across interdependent groups. Sci Rep 2013; 3:2483. [PMID: 23963495 PMCID: PMC3748424 DOI: 10.1038/srep02483] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2013] [Accepted: 08/05/2013] [Indexed: 12/02/2022] Open
Abstract
Recent empirical research has shown that links between groups reinforce individuals within groups to adopt cooperative behaviour. Moreover, links between networks may induce cascading failures, competitive percolation, or contribute to efficient transportation. Here we show that there in fact exists an intermediate fraction of links between groups that is optimal for the evolution of cooperation in the prisoner's dilemma game. We consider individual groups with regular, random, and scale-free topology, and study their different combinations to reveal that an intermediate interdependence optimally facilitates the spreading of cooperative behaviour between groups. Excessive between-group links simply unify the two groups and make them act as one, while too rare between-group links preclude a useful information flow between the two groups. Interestingly, we find that between-group links are more likely to connect two cooperators than in-group links, thus supporting the conclusion that they are of paramount importance.
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Affiliation(s)
- Luo-Luo Jiang
- College of Physics and Electronic Information Engineering, Wenzhou University, 325035 Wenzhou, China
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia
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Wang Z, Szolnoki A, Perc M. Optimal interdependence between networks for the evolution of cooperation. Sci Rep 2013; 3:2470. [PMID: 23959086 PMCID: PMC3747507 DOI: 10.1038/srep02470] [Citation(s) in RCA: 221] [Impact Index Per Article: 20.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2013] [Accepted: 08/02/2013] [Indexed: 11/17/2022] Open
Abstract
Recent research has identified interactions between networks as crucial for the outcome of evolutionary games taking place on them. While the consensus is that interdependence does promote cooperation by means of organizational complexity and enhanced reciprocity that is out of reach on isolated networks, we here address the question just how much interdependence there should be. Intuitively, one might assume the more the better. However, we show that in fact only an intermediate density of sufficiently strong interactions between networks warrants an optimal resolution of social dilemmas. This is due to an intricate interplay between the heterogeneity that causes an asymmetric strategy flow because of the additional links between the networks, and the independent formation of cooperative patterns on each individual network. Presented results are robust to variations of the strategy updating rule, the topology of interdependent networks, and the governing social dilemma, thus suggesting a high degree of universality.
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Affiliation(s)
- Zhen Wang
- Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Center for Nonlinear Studies and the Beijing-Hong Kong-Singapore Joint Center for Nonlinear and Complex Systems, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Attila Szolnoki
- Institute of Technical Physics and Materials Science, Research Centre for Natural Sciences, Hungarian Academy of SciencesP.O. Box 49, H-1525 Budapest, Hungary
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia
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