Clustering and preferential attachment in growing networks.

By Mark Newman. Physical review. E, Statistical, nonlinear, and soft matter physics, 2001

2,071 citations recorded by the source corpus.

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 a pair 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 power-law degree distributions in networks.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e5806a4e9ba5f8a99bb661d05e695d641b62afdf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

In Statistical and Nonlinear Physics

Citations in the corpus

Modern temporal network theory: a colloquium (2015, background): “[197] is one exception in the temporal network literature, but note that early static network papers like [206] also touch upon some temporal aspects (even though the main question is why the static network of accumulated contacts look like it does).”
Social network analysis for routing in disconnected delay-tolerant MANETs (2007, background): “Newman analysed the time evolution of scientific collaborations and observed that the use of examining neighbours, in this case coauthors of authors, could help predict future collaborations [30].”
Link prediction in social networks: the state-of-the-art (2014, background): “Common Neighbors (CN): The CN metric is one of the most widespread measurements used in link prediction problem mainly due to its simplicity [96].”
Embedding Networks with Edge Attributes (2018, methodology): “One way to obtain such a set of vectors is to use metrics such as Katz Index [16], Adamic Adar [1] and Common Neighbors [21].”
The link prediction problem for social networks (2003, background): “Barabasi et al. [2] and Newman [10] have further proposed, on the basis of empirical evidence, that the probability of co-authorship of x and y is correlated with the product of the number of collaborators of x and y , corresponding to the measure score ( x, y )”
Robust Matrix Completion via Joint Schatten p-Norm and lp-Norm Minimization (2012, methodology): “Besides comparing the matrix completion methods as in previous subsections, we also compare our method to two link prediction methods which are widely used in the studies of social networks, including common neighbors (CN) method [24] and SVD method [25].”
Link Prediction in Complex Networks: A Survey (2010, background): “Newman [40] used this quantity in the study of collaboration networks, showing a positive correlation between the number of common neighbors and the probability that two scientists will collaborate in the future.”
Evolution of networks (2001, background): “Recent empirical data [15, 101, 103] (see sections 5.”
Statistical mechanics of complex networks (2001, methodology): “To uncover the topology of this complex graph, Newman (2001a, 2001b, 2001c) studied four databases spanning physics, biomedical research, high-energy physics, and computer science over a five-year window (1995–1999).”
Growth of the flickr social network (2008, background): “Newman [16] examined the properties of two scientific collaboration networks and found evidence of preferential attachment in both.”
From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks (2013, background): “The common neighbours (CN) index 11 is the progenitor of these methods and follows the natural intuition that the likelihood that two nodes x and y interact increases if their sets of first-node-neighbours C x ð Þ and C y ð Þ overlap substantially: The other indices are often a variation or…”