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Thong Pham - One of the best experts on this subject based on the ideXlab platform.
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The evolutions of the rich get richer and the fit get richer phenomena in scholarly networks: the case of the strategic management journal
Scientometrics, 2018Co-Authors: Guillermo Armando Ronda-pupo, Thong PhamAbstract:Understanding how a scientist develops new scientific collaborations or how their papers receive new citations is a major challenge in scientometrics. The approach being proposed simultaneously examines the growth processes of the co-authorship and citation networks by analyzing the evolutions of the rich get richer and the fit get richer phenomena. In particular, the preferential Attachment Function and author fitnesses, which govern the two phenomena, are estimated non-parametrically in each network. The approach is applied to the co-authorship and citation networks of the flagship journal of the strategic management scientific community, namely the Strategic Management Journal. The results suggest that the abovementioned phenomena have been consistently governing both temporal networks. The average of the Attachment exponents in the co-authorship network is 0.30 while it is 0.29 in the citation network. This suggests that the rich get richer phenomenon has been weak in both networks. The right tails of the distributions of author fitness in both networks are heavy, which imply that the intrinsic scientific quality of each author has been playing a crucial role in getting new citations and new co-authorships. Since the total competitiveness in each temporal network is founded to be rising with time, it is getting harder to receive a new citation or to develop a new collaboration. Analyzing the average competency, it was found that on average, while the veterans tend to be more competent at developing new collaborations, the newcomers are likely better at acquiring new citations. Furthermore, the author fitness in both networks has been consistent with the history of the strategic management scientific community. This suggests that coupling node fitnesses throughout different networks might be a promising new direction in analyzing simultaneously multiple networks.
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The Rich get Richer and the Fit get Richer Phenomena in Temporal Complex Networks in the Strategic Management Scientific Community.
2017Co-Authors: Guillermo Armando Ronda-pupo, Thong PhamAbstract:The aim of this paper is to determine the general preferential Attachment Function and author fitness, which describe the rich get richer and fit get richer phenomena, in the co-authorship and citation networks of the strategic management scientific community. This has been done by means of the PAFit method using the community's flagship journal, namely Strategic Management Journal. The results suggest the co-authorship and citation temporal networks are governed by both the fit get richer and the rich get richer processes. The average of the Attachment exponents in the co-author network is 0.3 while it is 0.29 in the citation network, which suggests the rich get richer phenomenon is similarly weak in both networks. On the other hand, the distributions of author fitness in both networks have long right tail, which implies that the intrinsic scientific quality of each author plays a crucial role in getting new citations and new co-authorships. Furthermore, author fitness in both co-authorship and citation networks are found to be consistent with the history of the strategic management scientific community.
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PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
Journal of Statistical Software, 2017Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:Many real-world systems are profitably described as complex networks that grow over time. Preferential Attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are statistical packages for estimating various parametric forms of the preferential Attachment Function, there is no such package implementing non-parametric estimation procedures. The non-parametric approach to the estimation of the preferential Attachment Function allows for comparatively finer-grained investigations of the "rich-get-richer" phenomenon that could lead to novel insights in the search to explain certain nonstandard structural properties observed in real-world networks. This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential Attachment Function and node fitnesses in a growing network, as well as a number of Functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure scalability to large-scale networks. In this paper, we first introduce the main Functionalities of PAFit through simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks. The results indicate the joint presence of "richget-richer" and "fit-get-richer" phenomena in the collaboration network. The estimated Attachment Function is observed to be near-linear, which we interpret as meaning that the chance an author gets a new collaborator is proportional to their current number of collaborators. Furthermore, the estimated author fitnesses reveal a host of familiar faces from the complex networks community among the field's topmost fittest network scientists.
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PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
arXiv: Data Analysis Statistics and Probability, 2017Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:Many real-world systems are profitably described as complex networks that grow over time. Preferential Attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are statistical packages for estimating various parametric forms of the preferential Attachment Function, there is no such package implementing non-parametric estimation procedures. The non-parametric approach to the estimation of the preferential Attachment Function allows for comparatively finer-grained investigations of the `rich-get-richer' phenomenon that could lead to novel insights in the search to explain certain nonstandard structural properties observed in real-world networks. This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential Attachment Function and node fitnesses in a growing network, as well as a number of Functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure scalability to large-scale networks. We first introduce the main Functionalities of PAFit through simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks. The results indicate the joint presence of `rich-get-richer' and `fit-get-richer' phenomena in the collaboration network. The estimated Attachment Function is observed to be near-linear, which we interpret as meaning that the chance an author gets a new collaborator is proportional to their current number of collaborators. Furthermore, the estimated author fitnesses reveal a host of familiar faces from the complex networks community among the field's topmost fittest network scientists.
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Joint estimation of preferential Attachment and node fitness in growing complex networks.
Scientific reports, 2016Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:Complex network growth across diverse fields of science is hypothesized to be driven in the main by a combination of preferential Attachment and node fitness processes. For measuring the respective influences of these processes, previous approaches make strong and untested assumptions on the Functional forms of either the preferential Attachment Function or fitness Function or both. We introduce a Bayesian statistical method called PAFit to estimate preferential Attachment and node fitness without imposing such Functional constraints that works by maximizing a log-likelihood Function with suitably added regularization terms. We use PAFit to investigate the interplay between preferential Attachment and node fitness processes in a Facebook wall-post network. While we uncover evidence for both preferential Attachment and node fitness, thus validating the hypothesis that these processes together drive complex network evolution, we also find that node fitness plays the bigger role in determining the degree of a node. This is the first validation of its kind on real-world network data. But surprisingly the rate of preferential Attachment is found to deviate from the conventional log-linear form when node fitness is taken into account. The proposed method is implemented in the R package PAFit.
Hidetoshi Shimodaira - One of the best experts on this subject based on the ideXlab platform.
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PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
Journal of Statistical Software, 2017Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:Many real-world systems are profitably described as complex networks that grow over time. Preferential Attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are statistical packages for estimating various parametric forms of the preferential Attachment Function, there is no such package implementing non-parametric estimation procedures. The non-parametric approach to the estimation of the preferential Attachment Function allows for comparatively finer-grained investigations of the "rich-get-richer" phenomenon that could lead to novel insights in the search to explain certain nonstandard structural properties observed in real-world networks. This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential Attachment Function and node fitnesses in a growing network, as well as a number of Functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure scalability to large-scale networks. In this paper, we first introduce the main Functionalities of PAFit through simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks. The results indicate the joint presence of "richget-richer" and "fit-get-richer" phenomena in the collaboration network. The estimated Attachment Function is observed to be near-linear, which we interpret as meaning that the chance an author gets a new collaborator is proportional to their current number of collaborators. Furthermore, the estimated author fitnesses reveal a host of familiar faces from the complex networks community among the field's topmost fittest network scientists.
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PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
arXiv: Data Analysis Statistics and Probability, 2017Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:Many real-world systems are profitably described as complex networks that grow over time. Preferential Attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are statistical packages for estimating various parametric forms of the preferential Attachment Function, there is no such package implementing non-parametric estimation procedures. The non-parametric approach to the estimation of the preferential Attachment Function allows for comparatively finer-grained investigations of the `rich-get-richer' phenomenon that could lead to novel insights in the search to explain certain nonstandard structural properties observed in real-world networks. This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential Attachment Function and node fitnesses in a growing network, as well as a number of Functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure scalability to large-scale networks. We first introduce the main Functionalities of PAFit through simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks. The results indicate the joint presence of `rich-get-richer' and `fit-get-richer' phenomena in the collaboration network. The estimated Attachment Function is observed to be near-linear, which we interpret as meaning that the chance an author gets a new collaborator is proportional to their current number of collaborators. Furthermore, the estimated author fitnesses reveal a host of familiar faces from the complex networks community among the field's topmost fittest network scientists.
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Joint estimation of preferential Attachment and node fitness in growing complex networks.
Scientific reports, 2016Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:Complex network growth across diverse fields of science is hypothesized to be driven in the main by a combination of preferential Attachment and node fitness processes. For measuring the respective influences of these processes, previous approaches make strong and untested assumptions on the Functional forms of either the preferential Attachment Function or fitness Function or both. We introduce a Bayesian statistical method called PAFit to estimate preferential Attachment and node fitness without imposing such Functional constraints that works by maximizing a log-likelihood Function with suitably added regularization terms. We use PAFit to investigate the interplay between preferential Attachment and node fitness processes in a Facebook wall-post network. While we uncover evidence for both preferential Attachment and node fitness, thus validating the hypothesis that these processes together drive complex network evolution, we also find that node fitness plays the bigger role in determining the degree of a node. This is the first validation of its kind on real-world network data. But surprisingly the rate of preferential Attachment is found to deviate from the conventional log-linear form when node fitness is taken into account. The proposed method is implemented in the R package PAFit.
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ECCS - Nonparametric Estimation of the Preferential Attachment Function in Complex Networks: Evidence of Deviations from Log Linearity
Proceedings of ECCS 2014, 2016Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:We introduce a statistically sound method called PAFit for the joint estimation of preferential Attachment and node fitness in temporal complex networks. Together these mechanisms play a crucial role in shaping network topology by governing the way in which nodes acquire new edges over time. PAFit is an advance over previous methods in so far as it does not make any assumptions on the Functional form of the preferential Attachment Function. We found that the application of PAFit to a publicly available Flickr social network dataset turned up clear evidence for a deviation of the preferential Attachment Function from the popularly assumed log-linear form. What is more, we were surprised to find that hubs are not always the nodes with the highest node fitnesses. PAFit is implemented in an R package of the same name.
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nonparametric estimation of the preferential Attachment Function in complex networks evidence of deviations from log linearity
ECCS, 2016Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:We introduce a statistically sound method called PAFit for the joint estimation of preferential Attachment and node fitness in temporal complex networks. Together these mechanisms play a crucial role in shaping network topology by governing the way in which nodes acquire new edges over time. PAFit is an advance over previous methods in so far as it does not make any assumptions on the Functional form of the preferential Attachment Function. We found that the application of PAFit to a publicly available Flickr social network dataset turned up clear evidence for a deviation of the preferential Attachment Function from the popularly assumed log-linear form. What is more, we were surprised to find that hubs are not always the nodes with the highest node fitnesses. PAFit is implemented in an R package of the same name.
Ronald M. Iorio - One of the best experts on this subject based on the ideXlab platform.
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Structural and Functional Relationship between the Receptor Recognition and Neuraminidase Activities of the Newcastle Disease Virus Hemagglutinin-Neuraminidase Protein: Receptor Recognition Is Dependent on Neuraminidase Activity
Journal of virology, 2001Co-Authors: Ronald M. Iorio, Gisela M. Field, Jennifer M. Sauvron, Anne M. Mirza, Ruitang Deng, Paul J. Mahon, Johannes P. M. LangedijkAbstract:The terminal globular domain of the paramyxovirus hemagglutinin-neuraminidase (HN) glycoprotein spike has a number of conserved residues that are predicted to form its neuraminidase (NA) active site, by analogy to the influenza virus neuraminidase protein. We have performed a site-directed mutational analysis of the role of these residues in the Functional activity of the Newcastle disease virus (NDV) HN protein. Substitutions for several of these residues result in a protein lacking both detectable NA and receptor recognition activity. Contribution of NA activity, either exogenously or by coexpression with another HN protein, partially rescues the receptor recognition activity of these proteins, indicating that the receptor recognition deficiencies of the mutated HN proteins result from their lack of detectable NA activity. In addition to providing support for the homology-based predictions for the structure of HN, these findings argue that (i) the HN residues that mediate its NA activity are not critical to its Attachment Function and (ii) NA activity is required for the protein to mediate binding to receptors.
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A single amino acid substitution in the hemagglutinin-neuraminidase of Newcastle disease virus results in a protein deficient in both Functions.
Virology, 1992Co-Authors: John P. Sheehan, Ronald M. IorioAbstract:Abstract Sequence determinations of the hemagglutinin-neuraminidase (HN) glycoproteins of a temperature-sensitive mutant of Newcastle disease virus and two sequentially selected revenants had previously shown that substitution at a pair of residues, 129 and 175, resulted in a deficiency in neuraminidase (NA) activity, which was partially restored by a third substitution at residue 193. To evaluate the role of the substitution at residue 175 in diminished NA activity, the mutation was introduced into HN and the protein expressed in COS cells. The mutated HN not only had minimal NA activity but also was unable to absorb chicken erythrocytes, even though it was transported to the cell surface in normal amounts, in an apparently antigenic form. Attachment Function was restored to the protein by the introduction of the additional substitution(s) at 129 and/or 193. These results indicate that residue 175 influences not only NA activity but also receptor recognition.
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Neutralization map of the hemagglutinin-neuraminidase glycoprotein of Newcastle disease virus: domains recognized by monoclonal antibodies that prevent receptor recognition.
Journal of virology, 1991Co-Authors: Ronald M. Iorio, John P. Sheehan, Richard J. Syddall, Michael A. Bratt, Rhona L. Glickman, Anne M. RielAbstract:Abstract Monoclonal antibodies (MAbs) to the hemagglutinin-neuraminidase (HN) glycoprotein of Newcastle disease virus delineate seven overlapping antigenic sites which form a continuum on the surface of the molecule. Antibodies to five of these sites neutralize viral infectivity principally by preventing Attachment of the virion to cellular receptors. Through the identification of single amino acid substitutions in variants which escape neutralization by MAbs to these five antigenic sites, a neutralization map of HN was constructed, identifying several residues that contribute to the epitopes recognized by MAbs which block the Attachment Function of the molecule. These epitopes are defined, at least in part, by three domains on HN: residues 193 to 201; 345 to 353 (which include the only linear epitope we have identified in HN); and a C-terminal domain composed of residues 494, 513 to 521, and 569. To identify HN residues directly involved in receptor recognition, each of the variants was tested for its ability to agglutinate periodate-modified chicken erythrocytes. One variant with a single amino acid substitution at residue 193 was 2.5- to 3-fold more resistant to periodate treatment of erythrocytes than the wild-type virus, suggesting that this residue influences the binding of virus to a sialic acid-containing receptor(s) on the cell surface.
Remco Van Der Hofstad - One of the best experts on this subject based on the ideXlab platform.
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Consistent Estimation in General Sublinear Preferential Attachment Trees
arXiv: Statistics Theory, 2017Co-Authors: Fengnan Gao, Aad Van Der Vaart, Rui M. Castro, Remco Van Der HofstadAbstract:We propose an empirical estimator of the preferential Attachment Function $f$ in the setting of general preferential Attachment trees. Using a supercritical continuous-time branching process framework, we prove the almost sure consistency of the proposed estimator. We perform simulations to study the empirical properties of our estimators.
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Consistent estimation in general sublinear preferential Attachment trees
Electronic Journal of Statistics, 2017Co-Authors: Fengnan Gao, Aad Van Der Vaart, Rui M. Castro, Remco Van Der HofstadAbstract:We propose an empirical estimator of the preferential Attachment Function f in the setting of general sublinear preferential Attachment trees. Using a supercritical continuous-time branching process framework, we prove the almost sure consistency of the proposed estimator.We perform simulations to study the empirical properties of our estimators.
Paul Sheridan - One of the best experts on this subject based on the ideXlab platform.
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PAFit: An R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
Journal of Statistical Software, 2017Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:Many real-world systems are profitably described as complex networks that grow over time. Preferential Attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are statistical packages for estimating various parametric forms of the preferential Attachment Function, there is no such package implementing non-parametric estimation procedures. The non-parametric approach to the estimation of the preferential Attachment Function allows for comparatively finer-grained investigations of the "rich-get-richer" phenomenon that could lead to novel insights in the search to explain certain nonstandard structural properties observed in real-world networks. This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential Attachment Function and node fitnesses in a growing network, as well as a number of Functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure scalability to large-scale networks. In this paper, we first introduce the main Functionalities of PAFit through simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks. The results indicate the joint presence of "richget-richer" and "fit-get-richer" phenomena in the collaboration network. The estimated Attachment Function is observed to be near-linear, which we interpret as meaning that the chance an author gets a new collaborator is proportional to their current number of collaborators. Furthermore, the estimated author fitnesses reveal a host of familiar faces from the complex networks community among the field's topmost fittest network scientists.
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PAFit: an R Package for the Non-Parametric Estimation of Preferential Attachment and Node Fitness in Temporal Complex Networks
arXiv: Data Analysis Statistics and Probability, 2017Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:Many real-world systems are profitably described as complex networks that grow over time. Preferential Attachment and node fitness are two simple growth mechanisms that not only explain certain structural properties commonly observed in real-world systems, but are also tied to a number of applications in modeling and inference. While there are statistical packages for estimating various parametric forms of the preferential Attachment Function, there is no such package implementing non-parametric estimation procedures. The non-parametric approach to the estimation of the preferential Attachment Function allows for comparatively finer-grained investigations of the `rich-get-richer' phenomenon that could lead to novel insights in the search to explain certain nonstandard structural properties observed in real-world networks. This paper introduces the R package PAFit, which implements non-parametric procedures for estimating the preferential Attachment Function and node fitnesses in a growing network, as well as a number of Functions for generating complex networks from these two mechanisms. The main computational part of the package is implemented in C++ with OpenMP to ensure scalability to large-scale networks. We first introduce the main Functionalities of PAFit through simulated examples, and then use the package to analyze a collaboration network between scientists in the field of complex networks. The results indicate the joint presence of `rich-get-richer' and `fit-get-richer' phenomena in the collaboration network. The estimated Attachment Function is observed to be near-linear, which we interpret as meaning that the chance an author gets a new collaborator is proportional to their current number of collaborators. Furthermore, the estimated author fitnesses reveal a host of familiar faces from the complex networks community among the field's topmost fittest network scientists.
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Joint estimation of preferential Attachment and node fitness in growing complex networks.
Scientific reports, 2016Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:Complex network growth across diverse fields of science is hypothesized to be driven in the main by a combination of preferential Attachment and node fitness processes. For measuring the respective influences of these processes, previous approaches make strong and untested assumptions on the Functional forms of either the preferential Attachment Function or fitness Function or both. We introduce a Bayesian statistical method called PAFit to estimate preferential Attachment and node fitness without imposing such Functional constraints that works by maximizing a log-likelihood Function with suitably added regularization terms. We use PAFit to investigate the interplay between preferential Attachment and node fitness processes in a Facebook wall-post network. While we uncover evidence for both preferential Attachment and node fitness, thus validating the hypothesis that these processes together drive complex network evolution, we also find that node fitness plays the bigger role in determining the degree of a node. This is the first validation of its kind on real-world network data. But surprisingly the rate of preferential Attachment is found to deviate from the conventional log-linear form when node fitness is taken into account. The proposed method is implemented in the R package PAFit.
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ECCS - Nonparametric Estimation of the Preferential Attachment Function in Complex Networks: Evidence of Deviations from Log Linearity
Proceedings of ECCS 2014, 2016Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:We introduce a statistically sound method called PAFit for the joint estimation of preferential Attachment and node fitness in temporal complex networks. Together these mechanisms play a crucial role in shaping network topology by governing the way in which nodes acquire new edges over time. PAFit is an advance over previous methods in so far as it does not make any assumptions on the Functional form of the preferential Attachment Function. We found that the application of PAFit to a publicly available Flickr social network dataset turned up clear evidence for a deviation of the preferential Attachment Function from the popularly assumed log-linear form. What is more, we were surprised to find that hubs are not always the nodes with the highest node fitnesses. PAFit is implemented in an R package of the same name.
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nonparametric estimation of the preferential Attachment Function in complex networks evidence of deviations from log linearity
ECCS, 2016Co-Authors: Thong Pham, Paul Sheridan, Hidetoshi ShimodairaAbstract:We introduce a statistically sound method called PAFit for the joint estimation of preferential Attachment and node fitness in temporal complex networks. Together these mechanisms play a crucial role in shaping network topology by governing the way in which nodes acquire new edges over time. PAFit is an advance over previous methods in so far as it does not make any assumptions on the Functional form of the preferential Attachment Function. We found that the application of PAFit to a publicly available Flickr social network dataset turned up clear evidence for a deviation of the preferential Attachment Function from the popularly assumed log-linear form. What is more, we were surprised to find that hubs are not always the nodes with the highest node fitnesses. PAFit is implemented in an R package of the same name.
