Small Worlds: The Dynamics of Networks between Order and Randomness

By Jie Wu, Duncan J. Watts. 1999

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Citations in the corpus

Transductive Multi-View Zero-Shot Learning (2015, methodology): “peredge, and such independent, we consider the heterogeneous hypergraph a distributed representation. The advantage of having a distributed representation has been studied by Watts and Strogatz [52], [53] which shows that such a representation gives rise to better convergence rates and better cluste”
Social network analysis: developments, advances, and prospects (2010, background): “The most important recent theoretical arguments have been those that have taken up the work of White (1992) on issues of culture, identity, and agency.”
Distances in Random Graphs with Finite Mean and Infinite Variance Degrees (2005, background): “See [4, 29, 36 ] and the references therein for an introduction to complex networks and many examples where the above two properties hold.”
Concepts, Techniques, and Models of Computer Programming (2004, methodology): “With this assumption, the simulation is called a “Small World” simulation [203].”
The Physics of Communicability in Complex Networks (2011, background): “Complex networks are also ubiquitous in many biological, ecological, technological, informational, and infrastructural systems [12–20].”
Complex networks: Structure and dynamics (2006, background): “A measure of the typical separation between two nodes in the graph is given by the average shortest path length, also known as characteristic path length, defined as the mean of geodesic lengths over all couples of nodes [5,28]:”
The simultaneous evolution of author and paper networks (2003, background): “ze the statistical mechanics of topology and dynamics of diverse physical, biological, and social networks. A major goal is to identify elementary mechanisms that lead to the emergence of small world (4, 5) and scale free network structures (6, 7) commonly observed in the real world. Small world net”