scholarly journals Growth model for collaboration networks

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.

scholarly journals MODELLING COLLABORATION NETWORKS BASED ON NONLINEAR PREFERENTIAL ATTACHMENT

2007 ◽  
Vol 18 (02) ◽  
pp. 297-314 ◽  
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.

SYNCHRONIZATION IN THE FAMILY OF CHAOTIC SMALL-WORLD NETWORKS

2008 ◽  
Vol 19 (01) ◽  
pp. 111-123 ◽  
Author(s):  
LIANGMING HE ◽  
DUANWEN SHI
Keyword(s):  
Chaotic System ◽  
Average Distance ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Networks ◽  
Characteristic Path Length ◽  
Unified Chaotic System ◽  
The Family ◽  
Regular Networks ◽  
High Degree

In this paper we investigate by computer simulation the synchronizability of the family of small-world networks, which consists of identical chaotic units, such as the Lorenz chaotic system, the Chen chaotic system, Lü chaotic system, and the unified chaotic system (unit). It is shown that for weak coupling, synchronization clusters emerge in the networks whose disorder probabilities p are large but do not emerge in the networks whose disorder probabilities p are small; while for strong coupling under which the regular networks do not exhibit synchronization, all dynamical nodes, behaving as in the random networks, mutually synchronize in the networks which own very small disorder probability p and have both high degree of clustering and small average distance. Based on the concepts of clustering coefficient C(p), characteristic path length L(p) and global efficiency E(G), these phenomena are discussed briefly.

Research on the Structure of Peer-to-Peer Overlay Based on the Complex Network Theory

2012 ◽  
Vol 263-266 ◽  
pp. 1096-1099
Author(s):  
Zhi Yong Jiang
Keyword(s):  
Complex Network ◽  
Network Theory ◽  
Degree Distribution ◽  
Path Length ◽  
Small World ◽  
Peer To Peer ◽  
Clustering Coefficient ◽  
Power Law Distribution ◽  
Complex Network Theory ◽  
The Mean

Relationship between nodes in peer-to-peer overlay, currently becomes a hot topic in the field of complex network. In this paper a model of peer-to-peer overlay was purposed. And then the paper focused on figuring out the mean-shortest path length (MSPL), clustering coefficient (CC) and the degree of every node which allowed us to discover the degree distribution. The results show that the degree distribution function follows approximately power law distribution and the network possesses notable clustering and small-world properties.

scholarly journals Exploring the topology and dynamic growth properties of co-invention networks and technology fields

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256956
Author(s):  
Pablo E. Pinto ◽  
Guillermo Honores ◽  
Andrés Vallone
Keyword(s):  
Preferential Attachment ◽  
Small World ◽  
Individual Characteristics ◽  
Collaboration Networks ◽  
Technological Diversification ◽  
Sudden Rise ◽  
Growth Properties ◽  
Dynamic Growth ◽  
The Individual ◽  
Inventor Networks

This study investigates the topology and dynamics of collaboration networks that exist between inventors and their patent co-authors for patents granted by the USPTO from 2007–2019 (2,241,201 patents and 1,879,037 inventors). We study changes in the configurations of different technology fields via the power-law, small-world, preferential attachment, shrinking diameter, densification law, and gelling point hypotheses. Similar to the existing literature, we obtain mixed results. Based on network statistics, we argue that the sudden rise of large networks in six technology sectors can be understood as a phase transition in which small, isolated networks form one giant component. In two other technology sectors, such a transition occurred much later and much less dramatically. The examination of inventor networks over time reveals the increased complexity of all technology sectors, regardless of the individual characteristics of the network. Therefore, we introduce ideas associated with the technological diversification of inventors to complement our analysis, and we find evidence that inventors tend to diversify into new fields that are less mature. This behavior appears to be correlated with the compliance of some of the expected network rules and has implications for the emerging patterns among the different collaboration networks under consideration here.

scholarly journals ANALYSIS AND MODELING OF SCIENCE COLLABORATION NETWORKS

2003 ◽  
Vol 06 (04) ◽  
pp. 477-485 ◽  
Author(s):  
FELIX PÜTSCH
Keyword(s):  
Empirical Data ◽  
Small World ◽  
Collaboration Network ◽  
Collaboration Networks ◽  
Small World Networks ◽  
Science Collaboration

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.

scholarly journals An Advanced Algorithm for Higher Network Navigation in Social Internet of Things Using Small-World Networks

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2007 ◽  
Author(s):  
Farhan Amin ◽  
Rashid Abbasi ◽  
Abdul Rehman ◽  
Gyu Sang Choi
Keyword(s):  
Internet Of Things ◽  
Social Networking ◽  
Social Networking Sites ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Networks ◽  
Single Node ◽  
The Social ◽  
Social Internet Of Things ◽  
The Right

The Internet of Things (IoT) is a recent evolutionary technology that has been the primary focus of researchers for the last two decades. In the IoT, an enormous number of objects are connected together using diverse communications protocols. As a result of this massive object connectivity, a search for the exact service from an object is difficult, and hence the issue of scalability arises. In order to resolve this issue, the idea of integrating the social networking concept into the IoT, generally referred as the Social Internet of Things (SIoT) was introduced. The SIoT is gaining popularity and attracting the attention of the research community due to its flexible and spacious nature. In the SIoT, objects have the ability to find a desired service in a distributed manner by using their neighbors. Although the SIoT technique has been proven to be efficient, heterogeneous devices are growing so exponentially that problems can exist in the search for the right object or service from a huge number of devices. In order to better analyze the performance of services in an SIoT domain, there is a need to impose a certain set of rules on these objects. Our novel contribution in this study is to address the link selection problem in the SIoT by proposing an algorithm that follows the key properties of navigability in small-world networks, such as clustering coefficients, path lengths, and giant components. Our algorithm empowers object navigability in the SIoT by restricting the number of connections for objects, eliminating old links or having fewer connections. We performed an extensive series of experiments by using real network data sets from social networking sites like Brightkite and Facebook. The expected results demonstrate that our algorithm is efficient, especially in terms of reducing path length and increasing the average clustering coefficient. Finally, it reflects overall results in terms of achieving easier network navigation. Our algorithm can easily be applied to a single node or even an entire network.

A NEW DYNAMIC COMMUNITY MODEL FOR SOCIAL NETWORKS

2014 ◽  
Vol 25 (02) ◽  
pp. 1350088 ◽  
Author(s):  
ZHE-MING LU ◽  
ZHEN WU ◽  
SHI-ZE GUO ◽  
ZHE CHEN ◽  
GUANG-HUA SONG
Keyword(s):  
Social Networks ◽  
Average Distance ◽  
Small World ◽  
Clustering Coefficient ◽  
Community Model ◽  
Network Diameter ◽  
Proposed Model ◽  
Small World Property ◽  
Two Parameters ◽  
Dynamic Community

In this paper, based on the phenomenon that individuals join into and jump from the organizations in the society, we propose a dynamic community model to construct social networks. Two parameters are adopted in our model, one is the communication rate Pa that denotes the connection strength in the organization and the other is the turnover rate Pb, that stands for the frequency of jumping among the organizations. Based on simulations, we analyze not only the degree distribution, the clustering coefficient, the average distance and the network diameter but also the group distribution which is closely related to their community structure. Moreover, we discover that the networks generated by the proposed model possess the small-world property and can well reproduce the networks of social contacts.

THE SMALL-WORLD PROPERTY IN NETWORKS GROWING BY ACTIVE EDGES

2011 ◽  
Vol 14 (06) ◽  
pp. 853-869 ◽  
Author(s):  
PHILIPPE J. GIABBANELLI
Keyword(s):  
Growth Mechanism ◽  
Average Distance ◽  
Network Models ◽  
Small World ◽  
Clustering Coefficient ◽  
Small World Networks ◽  
Fractal Approach ◽  
Small World Property ◽  
Complex Network Models ◽  
Similar Growth

In the last three years, we have witnessed an increasing number of complex network models based on a 'fractal' approach, in which parts of the network are repeatedly replaced by a given pattern. Our focus is on models that can be defined by repeatedly adding a pattern network to selected edges, called active edges. We prove that when a pattern network has at least two active edges, then the resulting network has an average distance at most logarithmic in the number of nodes. This suggests that real-world networks based on a similar growth mechanism are likely to have small average distance. We provide an estimate of the clustering coefficient and verify its accuracy using simulations. Using numerous examples of simple patterns, our simulations show various ways to generate small-world networks. Finally, we discuss extensions to our framework encompassing probabilistic patterns and active subnetworks.

Topological Analysis of Urban Street Networks

10.1068/b306 ◽  
2004 ◽  
Vol 31 (1) ◽  
pp. 151-162 ◽  
Author(s):  
Bin Jiang ◽  
Christophe Claramunt
Keyword(s):  
Topological Analysis ◽  
Small World ◽  
Clustering Coefficient ◽  
Graph Representation ◽  
Small World Networks ◽  
Street Network ◽  
Scale Free ◽  
Urban Street ◽  
Street Networks ◽  
Functional Graph

The authors propose a topological analysis of large urban street networks based on a computational and functional graph representation. This representation gives a functional view in which vertices represent named streets and edges represent street intersections. A range of graph measures, including street connectivity, average path length, and clustering coefficient, are computed for structural analysis. In order to characterise different clustering degrees of streets in a street network they generalise the clustering coefficient to a k-clustering coefficient that takes into account k neighbours. Based on validations applied to three cities, the authors show that large urban street networks form small-world networks but exhibit no scale-free property.

scholarly journals On the Influence of Topological Characteristics on Robustness of Complex Networks

2013 ◽  
Vol 3 (2) ◽  
pp. 89-100 ◽  
Author(s):  
Dharshana Kasthurirathna ◽  
Mahendra Piraveenan ◽  
Gnanakumar Thedchanamoorthy
Keyword(s):  
Path Length ◽  
Small World ◽  
Average Path Length ◽  
Clustering Coefficient ◽  
Small World Networks ◽  
Scale Free ◽  
Rich Club ◽  
Targeted Attacks ◽  
Topological Characteristics ◽  
Topological Robustness

Abstract In this paper, we explore the relationship between the topological characteristics of a complex network and its robustness to sustained targeted attacks. Using synthesised scale-free, small-world and random networks, we look at a number of network measures, including assortativity, modularity, average path length, clustering coefficient, rich club profiles and scale-free exponent (where applicable) of a network, and how each of these influence the robustness of a network under targeted attacks. We use an established robustness coefficient to measure topological robustness, and consider sustained targeted attacks by order of node degree. With respect to scale-free networks, we show that assortativity, modularity and average path length have a positive correlation with network robustness, whereas clustering coefficient has a negative correlation. We did not find any correlation between scale-free exponent and robustness, or rich-club profiles and robustness. The robustness of small-world networks on the other hand, show substantial positive correlations with assortativity, modularity, clustering coefficient and average path length. In comparison, the robustness of Erdos-Renyi random networks did not have any significant correlation with any of the network properties considered. A significant observation is that high clustering decreases topological robustness in scale-free networks, yet it increases topological robustness in small-world networks. Our results highlight the importance of topological characteristics in influencing network robustness, and illustrate design strategies network designers can use to increase the robustness of scale-free and small-world networks under sustained targeted attacks.

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