Structure and time evolution of an Internet dating community

By Petter Holme, Christofer R Edling, Fredrik Liljeros. Soc. Networks, 2002

327 citations recorded by the source corpus.

We present statistics for the structure and time evolution of a network constructed from user activity in an Internet community. The vastness and precise time resolution of an Internet community offers unique possibilities to monitor social network formation and dynamics. Time evolution of well-known quantities, such as clustering, mixing (degree-degree correlations), average geodesic length, degree, and reciprocity is studied. In contrast to earlier analyses of scientific collaboration networks, mixing by degree between vertices is found to be disassortative. Furthermore, both the evolutionary trajectories of the average geodesic length and of the clustering coefficients are found to have minima.

Subfields: Condensed Matter Physics, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/4f29dc71915bb1a4f3e7d5a31f9a7c003cf7ff21

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In Condensed Matter Physics

In Economics and Econometrics

In Statistical and Nonlinear Physics

Citations in the corpus

Quantifying social group evolution (2007, background): “n a social network [3,7,8,9,10]. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution [7,11,12,13,14,15,16]. Our knowledge of the mechanisms governing the underlying community dynamics i”
Measuring the value of electronic word of mouth and its impact in consumer communities (2007, background): “More extensive studies include a study by Ebel et al. (2002) of email communications between 5,000 students at Keil University and an examination by Holme et al. (2004) of an online dating community.”
Main-memory triangle computations for very large (sparse (power-law)) graphs (2008, background): “Recent studies however show that, despite the fact thatm is small compared to n2, it may be in ω(n) [28, 31,27].”
The Spatial Evolution of Innovation Networks: A Proximity Perspective (2010, background): “In the case of inter-firm networks, it is obvious there are limits to the number of partners a firm can maintain (Holme et al., 2004).”
Methods for detrending success metrics to account for inflationary and deflationary factors* (2011, unknown): “Technometrics 11, 683-690 (1969) [34] P.”
Clustering in weighted networks (2009, background): “However, despite the fact that there are a large number of network datasets where the weights of the relationships are recorded (see Section 5, but also Barrat et al., 2004; Ebel et al., 2002; Holme et al., 2004; Kossinets and Watts, 2006; Panzarasa et al., 2009), only a limited number of measures t”
Do the Young Live in a "Smaller World" Than the Old? Age-Specific Degrees of Separation in a Large-Scale Mobile Communication Network (2016, background): “These estimates are larger than those obtained via indirect experimental strategies [11] (which are contaminated by selection on completed chains) or in online social media environments [4, 18, 23].”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “Holme and collaborators [104] observed that during the period considered in their study, both the number of members and the average degree grew with time but with decreasing growth rate.”
Statistical analysis of the social network and discussion threads in slashdot (2008, background): “Interestingly, other related studies of online communities show similar [10, 8] coefficients.”
White matter maturation reshapes structural connectivity in the late developing human brain (2010, background): “Empirical studies have examined evolving social networks from scientific collaborations (4) and the time evolution of Internet dating communities (5).”
Scaling laws of human interaction activity (2009, background): “The target group of the second online community (OC2) is teenagers (10).”
Modern temporal network theory: a colloquium (2015, background): “The immediate boundary problem more or less disappears if one studies complete records of closed Internet communities or social networking services [107,128,304,119,184,138], but even for these cases the dynamics is of course also shaped by events and communication outside of the system.”
Statistical physics of social dynamics (2007, background): “…ongoing social relationships (Holme, 2003), from email exchanges networks (Ebel et al., 2002; Eckmann et al., 2004; Newman et al., 2002) to the dating community network (Holme et al., 2004) and to mobile communication networks (Onnela et al., 2007; Palla et al., 2007), just to quote a few examples.”
Effects of missing data in social networks (2003, background): “This approach has not been feasible for large networks until recently, when data on large-scale social interactions become readily available from the records of email communication or virtual communities (Ebel et al., 2002; Guimera et al., 2002; Holme et al., 2002; Newman et al., 2002).”
Patterns and dynamics of users' behavior and interaction: Network analysis of an online community (2009, background): “However, because, in our case, the system follows a non-linear process of “accelerated” growth, the structure of ties becomes denser over time, and thus average separation between users decreases (Ahn et al., 2007; Barabási et al., 2002; Holme et al., 2004; Leskovec, Kleinberg, & Faloutsos, 2005).”