ANALYSIS AND MODELING OF SCIENCE COLLABORATION NETWORKS

We analyze a science collaboration network, i.e. a network whose nodes are scientists with edges connecting them for each paper published together. Furthermore, we develop a model for the simulation of discontiguous small-world networks that shows good coherence with empirical data.

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MODELLING COLLABORATION NETWORKS BASED ON NONLINEAR PREFERENTIAL ATTACHMENT

International Journal of Modern Physics C ◽

10.1142/s0129183107010437 ◽

2007 ◽

Vol 18 (02) ◽

pp. 297-314 ◽

Cited By ~ 30

Author(s):

TAO ZHOU ◽

BING-HONG WANG ◽

YING-DI JIN ◽

DA-REN HE ◽

PEI-PEI ZHANG ◽

...

Keyword(s):

Empirical Data ◽

Degree Distribution ◽

Alternative Model ◽

Free Parameter ◽

Preferential Attachment ◽

Average Distance ◽

Small World ◽

Clustering Coefficient ◽

Collaboration Networks ◽

Scale Free

In this paper, we propose an alternative model for collaboration networks based on nonlinear preferential attachment. Depending on a single free parameter "preferential exponent", this model interpolates between networks with a scale-free and an exponential degree distribution. The degree distribution in the present networks can be roughly classified into four patterns, all of which are observed in empirical data. And this model exhibits small-world effect, which means the corresponding networks are of very short average distance and highly large clustering coefficient. More interesting, we find a peak distribution of act-size from empirical data which has not been emphasized before. Our model can produce the peak act-size distribution naturally that agrees with the empirical data well.

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Inter-firm inventor mobility and the role of co-inventor networks in producing high-impact innovation

The Journal of Technology Transfer ◽

10.1007/s10961-019-09758-5 ◽

2019 ◽

Cited By ~ 2

Author(s):

Gergő Tóth ◽

Balázs Lengyel

Keyword(s):

Knowledge Transfer ◽

Small World ◽

High Impact ◽

Collaboration Network ◽

Collaboration Networks ◽

Firm Innovation ◽

Network Characteristics ◽

Inventor Networks ◽

The Impact

Abstract Inter-firm mobility of inventors is a major source of embodied knowledge transfer and receiving firms enjoy additional benefits from the collaboration networks of mobile inventors. However, there is still limited understanding on how the firm can maximize the impact of incoming inventors and what structure of co-inventor networks is the most beneficial for that. To answer this question, we construct a weighted and time-decayed co-inventor network from all IT-related patents in the harmonized OECD PATSTAT 1977–2010 database and analyze events of inter-firm inventor mobility. We look at the future impact of firm innovation and isolate the effect of mobile inventors’ network characteristics from the characteristics of the collaboration network in the receiving firm. Our results imply that high-impact innovations are produced if the firm hires broker inventors who have diverse networks and thus has the potential to channel a wide pool of knowledge into the firm. We find evidence that cohesive networks within the firm, measured by small world characteristics, exaggerate the effect of incoming brokers and high-impact inventors.

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Growth model for collaboration networks

Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées ◽

10.46298/arima.1447 ◽

2017 ◽

Vol Volume 24 - 2017 - Special... ◽

Author(s):

Ghislain Romaric MELEU ◽

Paulin MELATAGIA YONTA

Keyword(s):

Preferential Attachment ◽

Small World ◽

Clustering Coefficient ◽

Collaboration Networks ◽

Small World Networks ◽

Model Match ◽

Proposed Model ◽

Modèle De Croissance ◽

Growing Networks ◽

The Mean

We propose a model of growing networks based on cliques formations. A clique is used to illustrate for example co-authorship in co-publication networks, co-occurence of words or collaboration between actors of the same movie. Our model is iterative and at each step, a clique of λη existing vertices and (1 − λ)η new vertices is created and added in the network; η is the mean number of vertices per clique and λ is the proportion of old vertices per clique. The old vertices are selected according to preferential attachment. We show that the degree distribution of the generated networks follows the Power Law of parameter 1 + 1/ λ and thus they are ultra small-world networks with high clustering coefﬁcient and low density. Moreover, the networks generated by the proposed model match with some real co-publication networks such as CARI, EGC and HepTh. Nous proposons un modèle de croissance de graphe basé sur la formation de clique. Une clique peut par exemple illustrer la collaboration entre auteurs dans un réseau de co-publication, les relations de co-occurrence des mots dans une phrase ou les relations entre acteurs d'un film. C'est un modèle itératif qui à chaque étape crée une clique de λη anciens sommets et (1 − λ)η nouveaux sommets et l'insère dans le graphe. η est le nombre moyen de sommets dans une clique et λ la proportion moyenne d'anciens sommets dans une clique. La distribution des degrés des réseaux générés suit la Loi de Puissance de paramètre 1 + 1/λ et par conséquent ce sont des réseaux petit-mondes qui présentent un coefficient de clustering élevé et une faible densité. En outre, les réseaux générés par le modèle proposé reproduisent la structure des réseaux de terrains à l'instar des réseaux de co-publication du CARI, de EGC et de HepTh.

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Networks

10.1093/oso/9780198821939.003.0004 ◽

2018 ◽

Author(s):

Stefan Thurner ◽

Rudolf Hanel ◽

Peter Klimekl

Keyword(s):

Complex Systems ◽

Complex Networks ◽

Phase Diagrams ◽

Community Detection ◽

Network Science ◽

Small World ◽

Small World Networks ◽

Multilayer Networks ◽

Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

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