Pub Date : 2023-04-20DOI: 10.1088/2632-072X/accef0
C. Ellinas, D. Avraam, C. Nicolaides
Completing large-scale projects on time is a daunting challenge, partly due to the intricate network of dependencies between a project’s activities. To support this challenge, existing theory focuses on predicting whether a delay in completing a single activity is likely to spread and impact downstream activities. Using fine-grained information from 68 546 activities and 84 934 pairs, associated with the delivery of a $1.86Bn infrastructure project, we show that the core mechanism that underpins existing theory underestimates delay propagation. To elucidate the mechanisms that drive delay, we generated null models that destroyed the structural and temporal correlations of the original project activity network. By doing so, we argue that this underestimation is the result of neglecting endogenous structural features within the project’s activity network. Formulating a new mechanism that utilizes both temporal and structural features may help improve our capacity to predict how delays spread within projects.
{"title":"Neglecting complex network structures underestimates delays in a large-capital project","authors":"C. Ellinas, D. Avraam, C. Nicolaides","doi":"10.1088/2632-072X/accef0","DOIUrl":"https://doi.org/10.1088/2632-072X/accef0","url":null,"abstract":"Completing large-scale projects on time is a daunting challenge, partly due to the intricate network of dependencies between a project’s activities. To support this challenge, existing theory focuses on predicting whether a delay in completing a single activity is likely to spread and impact downstream activities. Using fine-grained information from 68 546 activities and 84 934 pairs, associated with the delivery of a $1.86Bn infrastructure project, we show that the core mechanism that underpins existing theory underestimates delay propagation. To elucidate the mechanisms that drive delay, we generated null models that destroyed the structural and temporal correlations of the original project activity network. By doing so, we argue that this underestimation is the result of neglecting endogenous structural features within the project’s activity network. Formulating a new mechanism that utilizes both temporal and structural features may help improve our capacity to predict how delays spread within projects.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43545322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1088/2632-072X/acf430
Tanu Raghav, S. Boccaletti, S. Jalan
Most real-world networks are endowed with the small-world property, by means of which the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. The evidence sparkled a wealth of studies trying to reveal possible mechanisms through which the pairwise interactions amongst the units of a network are structured in a way to determine such observed regularity. Here we show that smallworldness occurs also when interactions are of higher order. Namely, by considering Q-uniform hypergraphs and a process through which connections can be randomly rewired with given probability p, we find that such systems may exhibit prominent clustering properties in connection with small average path lengths for a wide range of p values, in analogy to the case of dyadic interactions. The nature of small-world transition remains the same at different orders Q ( =2,3,4,5, and 6) of the interactions, however, the increase in the hyperedge order reduces the range of rewiring probability for which smallworldness emerge.
{"title":"Smallworldness in hypergraphs","authors":"Tanu Raghav, S. Boccaletti, S. Jalan","doi":"10.1088/2632-072X/acf430","DOIUrl":"https://doi.org/10.1088/2632-072X/acf430","url":null,"abstract":"Most real-world networks are endowed with the small-world property, by means of which the maximal distance between any two of their nodes scales logarithmically rather than linearly with their size. The evidence sparkled a wealth of studies trying to reveal possible mechanisms through which the pairwise interactions amongst the units of a network are structured in a way to determine such observed regularity. Here we show that smallworldness occurs also when interactions are of higher order. Namely, by considering Q-uniform hypergraphs and a process through which connections can be randomly rewired with given probability p, we find that such systems may exhibit prominent clustering properties in connection with small average path lengths for a wide range of p values, in analogy to the case of dyadic interactions. The nature of small-world transition remains the same at different orders Q ( =2,3,4,5, and 6) of the interactions, however, the increase in the hyperedge order reduces the range of rewiring probability for which smallworldness emerge.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46785542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-12DOI: 10.1088/2632-072X/accc69
K. Goswami, R. Metzler
We propose an extension of the existing model describing a biomolecular reaction such as protein folding or ligand binding which is usually visualised as the barrier crossing of a diffusing particle in a double-well potential. In addition to the thermal noise, an active noise modelled in terms of an Ornstein–Uhlenbeck process is introduced to the dynamics. Within this framework, we investigate the transition-path properties of an underdamped particle surmounting an energy barrier, and we show explicitly how these properties are affected by the activity and persistence of the particle. Our theoretical study suggests that an active particle can cross the barrier at comparatively shorter timescales by lowering the (effective) barrier height. In particular, we study how the persistence time of the active force alters the transition-path time (TPT) at different friction limits. Interestingly, in one of our models we find a nonmonotonic behaviour of the TPT which is absent in the overdamped limit. The framework presented here can be useful in designing a reaction in a non-equilibrium environment, particularly inside a living biological cell in which active fluctuations keep the system out of equilibrium.
{"title":"Effects of active noise on transition-path dynamics","authors":"K. Goswami, R. Metzler","doi":"10.1088/2632-072X/accc69","DOIUrl":"https://doi.org/10.1088/2632-072X/accc69","url":null,"abstract":"We propose an extension of the existing model describing a biomolecular reaction such as protein folding or ligand binding which is usually visualised as the barrier crossing of a diffusing particle in a double-well potential. In addition to the thermal noise, an active noise modelled in terms of an Ornstein–Uhlenbeck process is introduced to the dynamics. Within this framework, we investigate the transition-path properties of an underdamped particle surmounting an energy barrier, and we show explicitly how these properties are affected by the activity and persistence of the particle. Our theoretical study suggests that an active particle can cross the barrier at comparatively shorter timescales by lowering the (effective) barrier height. In particular, we study how the persistence time of the active force alters the transition-path time (TPT) at different friction limits. Interestingly, in one of our models we find a nonmonotonic behaviour of the TPT which is absent in the overdamped limit. The framework presented here can be useful in designing a reaction in a non-equilibrium environment, particularly inside a living biological cell in which active fluctuations keep the system out of equilibrium.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42356466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-23DOI: 10.1088/2632-072X/acc71a
G. M. Cicuta, M. Pernici
We study ensembles of sparse block-structured random matrices generated from the adjacency matrix of a Erdös–Renyi random graph with N vertices of average degree Z, inserting a real symmetric d × d random block at each non-vanishing entry. We consider several ensembles of random block matrices with rank r < d and with maximal rank, r = d. The spectral moments of the sparse block-structured random matrix are evaluated for N→∞ , d finite or infinite, and several probability distributions for the blocks (e.g. fixed trace, bounded trace and Gaussian). Because of the concentration of the probability measure in the d→∞ limit, the spectral moments are independent of the probability measure of the blocks (with mild assumptions of isotropy, smoothness and sub-Gaussian tails). The effective medium approximation is the limiting spectral density of the sparse block-structured random ensembles with finite rank. Analogous classes of universality hold for the Laplacian sparse block-structured ensemble. The same limiting distributions are obtained using random regular graphs instead of Erdös–Renyi graphs.
{"title":"Sparse block-structured random matrices: universality","authors":"G. M. Cicuta, M. Pernici","doi":"10.1088/2632-072X/acc71a","DOIUrl":"https://doi.org/10.1088/2632-072X/acc71a","url":null,"abstract":"We study ensembles of sparse block-structured random matrices generated from the adjacency matrix of a Erdös–Renyi random graph with N vertices of average degree Z, inserting a real symmetric d × d random block at each non-vanishing entry. We consider several ensembles of random block matrices with rank r < d and with maximal rank, r = d. The spectral moments of the sparse block-structured random matrix are evaluated for N→∞ , d finite or infinite, and several probability distributions for the blocks (e.g. fixed trace, bounded trace and Gaussian). Because of the concentration of the probability measure in the d→∞ limit, the spectral moments are independent of the probability measure of the blocks (with mild assumptions of isotropy, smoothness and sub-Gaussian tails). The effective medium approximation is the limiting spectral density of the sparse block-structured random ensembles with finite rank. Analogous classes of universality hold for the Laplacian sparse block-structured ensemble. The same limiting distributions are obtained using random regular graphs instead of Erdös–Renyi graphs.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44886008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-21DOI: 10.1088/2632-072X/acc62c
Alexander T. J. Barron, Marijn ten Thij, J. Bollen
Expressing identity socially involves a balance between conformity and innovation. One can adopt existing labels to express belonging to a certain community or introduce new labels to express an individual sense of identity. In such a process of co-creation, the existing identity labels of a community shape one’s sense of identity, while individual expression changes that of a community. Social media has introduced new opportunities to study the expression of collective identity. Here we study the group behavior of individuals defining their identities with hashtag self-labels in their Twitter profiles from mid-2017 through 2019. These timelines of personal self-labeling show behavior incorporating innovation, conservation, and social conformity when defining self. We show that the collective co-labeling of popular concepts in the context of identity, such as #resist and #maga, follow the dynamics of a modified Yule–Simon model balancing novelty and conformity. The dynamics of identity expression resemble the collective tagging processes of folksonomies, indicating a similarity between the collective tagging of external objects and the collective labeling of ourselves. Our work underpins a better understanding of how online environments mediate the evolution of collective identity which plays an increasingly important role in the establishment of community values and identity politics.
{"title":"Online identity as a collective labeling process","authors":"Alexander T. J. Barron, Marijn ten Thij, J. Bollen","doi":"10.1088/2632-072X/acc62c","DOIUrl":"https://doi.org/10.1088/2632-072X/acc62c","url":null,"abstract":"Expressing identity socially involves a balance between conformity and innovation. One can adopt existing labels to express belonging to a certain community or introduce new labels to express an individual sense of identity. In such a process of co-creation, the existing identity labels of a community shape one’s sense of identity, while individual expression changes that of a community. Social media has introduced new opportunities to study the expression of collective identity. Here we study the group behavior of individuals defining their identities with hashtag self-labels in their Twitter profiles from mid-2017 through 2019. These timelines of personal self-labeling show behavior incorporating innovation, conservation, and social conformity when defining self. We show that the collective co-labeling of popular concepts in the context of identity, such as #resist and #maga, follow the dynamics of a modified Yule–Simon model balancing novelty and conformity. The dynamics of identity expression resemble the collective tagging processes of folksonomies, indicating a similarity between the collective tagging of external objects and the collective labeling of ourselves. Our work underpins a better understanding of how online environments mediate the evolution of collective identity which plays an increasingly important role in the establishment of community values and identity politics.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44749280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-07DOI: 10.1088/2632-072X/acc22b
A. Viol, G. Viswanathan, Oleksandra Soldatkina, Fernanda Palhano-Fontes, H. Onias, D. D. de Araujo, Philipp Hoevel
The physical basis of consciousness is one of the most intriguing open questions that contemporary science aims to solve. By approaching the brain as an interactive information system, complex network theory has greatly contributed to understand brain process in different states of mind. We study a non-ordinary state of mind by comparing resting-state functional brain networks of individuals in two different conditions: before and after the ingestion of the psychedelic brew Ayahuasca. In order to quantify the functional, statistical symmetries between brain region connectivity, we calculate the pairwise information parity of the functional brain networks. Unlike the usual approach to quantitative network analysis that considers only local or global scales, information parity instead quantifies pairwise statistical similarities over the entire network structure. We find an increase in the average information parity on brain networks of individuals under psychedelic influences. Notably, the information parity between regions from the limbic system and frontal cortex is consistently higher for all the individuals while under the psychedelic influence. These findings suggest that the resemblance of statistical influences between pair of brain regions activities tends to increase under Ayahuasca effects. This could be interpreted as a mechanism to maintain the network functional resilience.
{"title":"Information parity increases on functional brain networks under influence of a psychedelic substance","authors":"A. Viol, G. Viswanathan, Oleksandra Soldatkina, Fernanda Palhano-Fontes, H. Onias, D. D. de Araujo, Philipp Hoevel","doi":"10.1088/2632-072X/acc22b","DOIUrl":"https://doi.org/10.1088/2632-072X/acc22b","url":null,"abstract":"The physical basis of consciousness is one of the most intriguing open questions that contemporary science aims to solve. By approaching the brain as an interactive information system, complex network theory has greatly contributed to understand brain process in different states of mind. We study a non-ordinary state of mind by comparing resting-state functional brain networks of individuals in two different conditions: before and after the ingestion of the psychedelic brew Ayahuasca. In order to quantify the functional, statistical symmetries between brain region connectivity, we calculate the pairwise information parity of the functional brain networks. Unlike the usual approach to quantitative network analysis that considers only local or global scales, information parity instead quantifies pairwise statistical similarities over the entire network structure. We find an increase in the average information parity on brain networks of individuals under psychedelic influences. Notably, the information parity between regions from the limbic system and frontal cortex is consistently higher for all the individuals while under the psychedelic influence. These findings suggest that the resemblance of statistical influences between pair of brain regions activities tends to increase under Ayahuasca effects. This could be interpreted as a mechanism to maintain the network functional resilience.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42305600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-02DOI: 10.1088/2632-072X/acc0c1
Giordano De Marzo, F. Attili, L. Pietronero
A central problem in economics and statistics is the assessment of income or wealth inequality starting from empirical data. Here we focus on the behavior of Gini index, one of the most used inequality measures, in presence of Zipf’s law, a situation which occurs in many complex financial and economical systems. First, we show that the application of asymptotic formulas to finite size systems always leads to an overestimation of inequality. We thus compute finite size corrections and we show that depending on Zipf’s exponent two distinct regimes can be observed: low inequality, where Gini index is less than one and maximal inequality, where Gini index asymptotically tends to its maximal value one. In both cases, the inequality of an expanding system slowly increases just as effect of growth, with a scaling never faster than the inverse of the size. We test our computations on two real systems, US cities and the cryptocurrency market, observing in both cases an increase of inequality that is completely explained by Zipf’s law and the systems expanding. This shows that in growing complex systems finite size effects must be considered in order to properly assess if inequality is increasing due to natural growth processes or if it is produced by a change in the economical structure of the systems. Finally we discuss how such effects must be carefully considered when analyzing survey data.
{"title":"Growing inequality in systems showing Zipf’s law","authors":"Giordano De Marzo, F. Attili, L. Pietronero","doi":"10.1088/2632-072X/acc0c1","DOIUrl":"https://doi.org/10.1088/2632-072X/acc0c1","url":null,"abstract":"A central problem in economics and statistics is the assessment of income or wealth inequality starting from empirical data. Here we focus on the behavior of Gini index, one of the most used inequality measures, in presence of Zipf’s law, a situation which occurs in many complex financial and economical systems. First, we show that the application of asymptotic formulas to finite size systems always leads to an overestimation of inequality. We thus compute finite size corrections and we show that depending on Zipf’s exponent two distinct regimes can be observed: low inequality, where Gini index is less than one and maximal inequality, where Gini index asymptotically tends to its maximal value one. In both cases, the inequality of an expanding system slowly increases just as effect of growth, with a scaling never faster than the inverse of the size. We test our computations on two real systems, US cities and the cryptocurrency market, observing in both cases an increase of inequality that is completely explained by Zipf’s law and the systems expanding. This shows that in growing complex systems finite size effects must be considered in order to properly assess if inequality is increasing due to natural growth processes or if it is produced by a change in the economical structure of the systems. Finally we discuss how such effects must be carefully considered when analyzing survey data.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43345277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1088/2632-072X/acb8a1
L. Cugliandolo
This article summarises the outstanding scientific career of Giorgio Parisi, who was awarded the 2021 Nobel Prize in Physics, with special emphasis on his contributions to the description of the equilibrium properties of disordered systems.
{"title":"A scientific portrait of Giorgio Parisi: complex systems and much more","authors":"L. Cugliandolo","doi":"10.1088/2632-072X/acb8a1","DOIUrl":"https://doi.org/10.1088/2632-072X/acb8a1","url":null,"abstract":"This article summarises the outstanding scientific career of Giorgio Parisi, who was awarded the 2021 Nobel Prize in Physics, with special emphasis on his contributions to the description of the equilibrium properties of disordered systems.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46234527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-28DOI: 10.1088/2632-072X/acef9d
Dolores Bernenko, Sang Hoon Lee, L. Lizana
Researchers have developed chromosome capture methods such as Hi-C to better understand DNA’s 3D folding in nuclei. The Hi-C method captures contact frequencies between DNA segment pairs across the genome. When analyzing Hi-C data sets, it is common to group these pairs using standard bioinformatics methods (e.g. PCA). Other approaches handle Hi-C data as weighted networks, where connected node pairs represent DNA segments in 3D proximity. In this representation, one can leverage community detection techniques developed in complex network theory to group nodes into mesoscale communities containing nodes with similar connection patterns. While there are several successful attempts to analyze Hi-C data in this way, it is common to report and study the most typical community structure. But in reality, there are often several valid candidates. Therefore, depending on algorithm design, different community detection methods focusing on slightly different connectivity features may have differing views on the ideal node groupings. In fact, even the same community detection method may yield different results if using a stochastic algorithm. This ambiguity is fundamental to community detection and shared by most complex networks whenever interactions span all scales in the network. This is known as community inconsistency. This paper explores this inconsistency of 3D communities in Hi-C data for all human chromosomes. We base our analysis on two inconsistency metrics, one local and one global, and quantify the network scales where the community separation is most variable. For example, we find that TADs are less reliable than A/B compartments and that nodes with highly variable node-community memberships are associated with open chromatin. Overall, our study provides a helpful framework for data-driven researchers and increases awareness of some inherent challenges when clustering Hi-C data into 3D communities.
{"title":"Exploring 3D community inconsistency in human chromosome contact networks","authors":"Dolores Bernenko, Sang Hoon Lee, L. Lizana","doi":"10.1088/2632-072X/acef9d","DOIUrl":"https://doi.org/10.1088/2632-072X/acef9d","url":null,"abstract":"Researchers have developed chromosome capture methods such as Hi-C to better understand DNA’s 3D folding in nuclei. The Hi-C method captures contact frequencies between DNA segment pairs across the genome. When analyzing Hi-C data sets, it is common to group these pairs using standard bioinformatics methods (e.g. PCA). Other approaches handle Hi-C data as weighted networks, where connected node pairs represent DNA segments in 3D proximity. In this representation, one can leverage community detection techniques developed in complex network theory to group nodes into mesoscale communities containing nodes with similar connection patterns. While there are several successful attempts to analyze Hi-C data in this way, it is common to report and study the most typical community structure. But in reality, there are often several valid candidates. Therefore, depending on algorithm design, different community detection methods focusing on slightly different connectivity features may have differing views on the ideal node groupings. In fact, even the same community detection method may yield different results if using a stochastic algorithm. This ambiguity is fundamental to community detection and shared by most complex networks whenever interactions span all scales in the network. This is known as community inconsistency. This paper explores this inconsistency of 3D communities in Hi-C data for all human chromosomes. We base our analysis on two inconsistency metrics, one local and one global, and quantify the network scales where the community separation is most variable. For example, we find that TADs are less reliable than A/B compartments and that nodes with highly variable node-community memberships are associated with open chromatin. Overall, our study provides a helpful framework for data-driven researchers and increases awareness of some inherent challenges when clustering Hi-C data into 3D communities.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44751293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-20DOI: 10.1088/2632-072X/acbd7c
Aurélien Hazan
In this article, production process databases originating from environmental sciences, more specifically from life cycle inventory (LCI), are considered as bipartite directed random networks. To model the observed directed hierarchical connection patterns, we turn to recent development concerning trophic coherence. Extending the scope to include bipartite networks, we compare several LCI networks to networks from other fields, and show empirically that they have high coherence and belong to the loopless regime, or close to its boundary.
{"title":"Production process networks: a trophic analysis","authors":"Aurélien Hazan","doi":"10.1088/2632-072X/acbd7c","DOIUrl":"https://doi.org/10.1088/2632-072X/acbd7c","url":null,"abstract":"In this article, production process databases originating from environmental sciences, more specifically from life cycle inventory (LCI), are considered as bipartite directed random networks. To model the observed directed hierarchical connection patterns, we turn to recent development concerning trophic coherence. Extending the scope to include bipartite networks, we compare several LCI networks to networks from other fields, and show empirically that they have high coherence and belong to the loopless regime, or close to its boundary.","PeriodicalId":53211,"journal":{"name":"Journal of Physics Complexity","volume":" ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46433549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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