Small worlds: the dynamics of networks between order and randomness
By Jie Wu. 2000
3,244 citations recorded by the source corpus.
Subfields: Sociology and Political Science, Statistical and Nonlinear Physics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Sociology and Political Science
- Mark Newman
- Matjaz Perc
- Alessandro Vespignani
- Jon M. Kleinberg
- Marc Barthelemy
- Yamir Moreno
- Garry Robins
- Duncan J. Watts
In Statistical and Nonlinear Physics
- Mark Newman
- Santo Fortunato
- Alessandro Vespignani
- Albert-László Barabási
- Marc Barthelemy
- Olaf Sporns
- Matjaz Perc
- E. Bullmore
Citations in the corpus
- Transductive Multi-View Zero-Shot Learning (2015, background): “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 clustering”
- Community detection in graphs (2009, background): “A (seemingly) different benchmark is represented by the class of relaxed caveman graphs, which were originally introduced to explain the clustering properties of social networks (Watts, 2003).”
- The Corporate Elite Community Structure of Global Capitalism (2016, background): “An important breakthrough in the study of complex networks with direct relevance for our work came with the introduction of what has become known as small world statistics (Watts 1999, Kogut and Walker 2001, Uzzi et al .”
- Economics: An Emerging Small World (2004, background): “This definition extends the one given by Watts (1999) by addingh(G)/n requirement 2.”
- Emergence of a small world from local interactions: modeling acquaintance networks. (2001, background): “In its original definition [4,5] it served as an elegant toy model, demonstrating the consequences of high clustering and short path length.”
- Networks in Political Science: Back to the Future (2011, background): “…2011 61 Some of this network research caught popular attention, serving as part of the motivation for Malcolm Gladwell’s Tipping Point (2002) and other popular books from the principals of the field, including Watts (2004; 2003), Strogatz (2003), Barabasi (2003), and Christakis and Fowler (2009).”
- Clusters for life or life cycles of clusters: in search of the critical factors of clusters' resilience (2014, background): “Complex system approaches provide convincing complementary results about strong macro-regularities of network structures (Watts 1999).”
- Global Synchronization of Linearly Hybrid Coupled Networks with Time-Varying Delay (2008, background): “Over the last few years, complex networks have received increasing attention from all fields of sciences and humanities [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40].”
- Beyond Bowling Together: SocioTechnical Capital (2001, background): “For example, the diameter of the graph is an indicator of how quickly information might spread through the network (Watts 1999).”
- Social network analysis: developments, advances, and prospects (2010, background): “Taking up these suggestions and prior work on random networks, theorists such as Barabási (2002) and Watts (1999, 2003) proposed what they regard as new areas of application to the social world.”
- Risk Management for Hedge Funds: Introduction and Overview (2001, positive): “The “small world” networks considered by Watts and Strogatz (1998) and Watts (1999) seem to be particularly promising starting points.”
- Statistical analysis of financial networks (2005, neutral): “…also mention the Call graph arising in the telecommunications traac data (Abello et al., 1999; Aiello et al., 2001; Hayes, 2000); metabolic networks arising in biology (Jeong et al., 2000), and social networks where real people are the vertices (Hayes, 2000; Watts, 1999; Watts and Strogatz, 1998).”
- Recent advances in percolation theory and its applications (2015, background): “rld complex networks such as the Internet [159], social networks [160], disease modeling [161] etc., share similar features e.g., they are large, sparse, scale-free, small worlds and highly clustered [162]. Consider a graph G n, with nnumber of vertices. Denote the proportion of vertices with degree”
- Is Oprah Contagious? Identifying Demand Spillovers in Online Networks (2012, neutral): “Local Clustering A measure of how close a node and its neighbors are to being a clique (Watts 2003; Watts and Strogatz 1998).”