Results 1  10
of
80,571
Preferential Attachment and Individual Heterogeneity
, 2008
"... Preliminary draft Many empirical social networks have been found to display a degree distribution that either follows a power law or exhibit considerably fatter tails than would be predicted by random link formation The most commonly accepted explanation for this empirical regularity is preferential ..."
Abstract
 Add to MetaCart
is preferential attachment. Using data on coauthorship in economics, we estimate a model of preferential attachment with unobserved heterogeneity. Our main finding is that the evidence for preferential attachment evaporates once we control for individual heterogeneity in the propensity to form new links.
Competitioninduced preferential attachment
 Proceedings of the 31st International Colloquium on Automata, Languages and Programming (ICALP), 208221, Lecture Notes in Computer Science 3142
, 2004
"... Abstract. Models based on preferential attachment have had much success in reproducing the power law degree distributions which seem ubiquitous in both natural and engineered systems. Here, rather than assuming preferential attachment, we give an explanation of how it can arise from a more basic un ..."
Abstract

Cited by 13 (0 self)
 Add to MetaCart
Abstract. Models based on preferential attachment have had much success in reproducing the power law degree distributions which seem ubiquitous in both natural and engineered systems. Here, rather than assuming preferential attachment, we give an explanation of how it can arise from a more basic
Preferential attachment with choice
, 2014
"... We consider the degree distributions of preferential attachment random graph models with choice similar to those considered in recent work by Malyshkin and Paquette and Krapivsky and Redner. In these models a new vertex chooses r vertices according to a preferential rule and connects to the vertex ..."
Abstract
 Add to MetaCart
We consider the degree distributions of preferential attachment random graph models with choice similar to those considered in recent work by Malyshkin and Paquette and Krapivsky and Redner. In these models a new vertex chooses r vertices according to a preferential rule and connects to the vertex
Clustering and preferential attachment in growing networks
 Phys. Rev. E
, 2001
"... We study empirically the time evolution of scientific collaboration networks in physics and biology. In these networks, two scientists are considered connected if they have coauthored one or more papers together. We show that the probability of scientists collaborating increases with the number of o ..."
Abstract

Cited by 191 (5 self)
 Add to MetaCart
We study empirically the time evolution of scientific collaboration networks in physics and biology. In these networks, two scientists are considered connected if they have coauthored one or more papers together. We show that the probability of scientists collaborating increases with the number of other collaborators they have in common, and that the probability of a particular scientist acquiring new collaborators increases with the number of his or her past collaborators. These results provide experimental evidence in favor of previously conjectured mechanisms for clustering and powerlaw degree distributions in networks. 1 I.
13. Preferential attachment
"... Brief description: There are various growth models for random graphs that use the following idea. Consider the number of neighbours of each vertex in a graph Gn with n vertices. Suppose that the graph Gn+1 is constructed from Gn by connecting the (n + 1) st vertex to one or more other vertices depen ..."
Abstract
 Add to MetaCart
depending on their number of neighbours. Such models are called “preferential attachment models”. There are several variants of such models. In this project, we will consider some of the literature on such models, e.g. starting from Athreya et al., which is written in an accessible way for a student who has
Coexistence in preferential attachment networks
, 2014
"... Competition in markets is ubiquitous: cellphone providers, computer manufacturers, and sport gear brands all vie for customers. Though several coexisting competitors are often observed in empirical data, many current theoretical models of competition on smallworld networks predict a single winner ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
. When a new node joins the network, it chooses neighbors according to preferential attachment, and then chooses its type based on the number of initial neighbors of each type. This can model a new cellphone user choosing a cellphone provider, a new student choosing a laptop, or a new athletic team
Diameters in preferential attachment models
, 2009
"... In this paper, we investigate the diameter in preferential attachment (PA) models, thus quantifying the statement that these models are small worlds. The models studied here are such that edges are attached to older vertices proportional to the degree plus a constant, i.e., we consider affine PAmo ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
In this paper, we investigate the diameter in preferential attachment (PA) models, thus quantifying the statement that these models are small worlds. The models studied here are such that edges are attached to older vertices proportional to the degree plus a constant, i.e., we consider affine PA
Diameters in preferential attachment models
, 2009
"... In this paper, we investigate the diameter in preferential attachment (PA) models, thus quantifying the statement that these models are small worlds. There is a substantial amount of literature proving that, in quite generality, PAgraphs possess powerlaw degree sequences with exponent τ> 2. Th ..."
Abstract
 Add to MetaCart
In this paper, we investigate the diameter in preferential attachment (PA) models, thus quantifying the statement that these models are small worlds. There is a substantial amount of literature proving that, in quite generality, PAgraphs possess powerlaw degree sequences with exponent τ> 2
THE POWER OF CHOICE COMBINED WITH PREFERENTIAL ATTACHMENT
"... Abstract. We prove almost sure convergence of the maximum degree in an evolving tree model combining local choice and preferential attachment. At each step in the growth of the graph, a new vertex is introduced. A fixed, finite number of possible neighbors are sampled from the existing vertices with ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
Abstract. We prove almost sure convergence of the maximum degree in an evolving tree model combining local choice and preferential attachment. At each step in the growth of the graph, a new vertex is introduced. A fixed, finite number of possible neighbors are sampled from the existing vertices
Results 1  10
of
80,571