How people interact in evolving online affiliation networks

  title={How people interact in evolving online affiliation networks},
  author={Lazaros K. Gallos and Diego Rybski and Fredrik Liljeros and Shlomo Havlin and Hern{\'a}n A. Makse},
The study of human interactions is of central importance for understanding the behavior of individuals, groups and societies. Here, we observe the formation and evolution of networks by monitoring the addition of all new links and we analyze quantitatively the tendencies used to create ties in these evolving online affiliation networks. We first show that an accurate estimation of these probabilistic tendencies can only be achieved by following the time evolution of the network. For example… 

Figures and Tables from this paper

The role of information diffusion in the evolution of social networks
An analysis of longitudinal micro-blogging data is presented, revealing a more nuanced view of the strategies employed by users when expanding their social circles and characterize users with a set of parameters associated with different link creation strategies, estimated by a Maximum-Likelihood approach.
Co-evolutionary dynamics in social networks: a case study of Twitter
Complex networks often exhibit co-evolutionary dynamics, meaning that the network topology and the state of nodes or links are coupled, affecting each other in overlapping time scales. We focus on
Propinquity drives the emergence of network structure and density
The work shows that sociologically meaningful mechanisms are influencing network evolution and provides indications of the importance of measuring the distance between successive connections.
Conditions for viral influence spreading through multiplex correlated social networks
This work shows that a condition for sustaining a viral spreading process is the existence of a multiplex correlated graph with hidden "influence links", and predicts the conditions for viral cascading in a large class of multiplex networks ranging from social to financial systems and markets.
Modeling spatial social complex networks for dynamical processes
A spatial social complex network (SSCN) model is developed that captures not only essential connectivity features of real-life social networks, including a heavy-tailed degree distribution and high clustering, but also the spatial location of individuals, reproducing Zipf's law for the distribution of city populations as well as other observed hallmarks.
Finding Influential Spreaders from Human Activity beyond Network Location
It is found that a reliable immunization scheme can be achieved by asking people how they interact with each other, and the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms.
Evolution through bursts: Network structure develops through localized bursts in time and space
Coincidence of the localized network change with the increase in homophily suggests a strong coupling between the selection and influence processes that lead to simultaneous elevation of assortativity and clustering.
Strategies of Success for Social Networks: Mermaids and Temporal Evolution
This study describes an effective technique that deals with the issue of attracting users’ attention by introducing the notion of mermaids, special attractors that alter the normal evolutive behavior of a social system.
How Do Online Social Networks Grow?
It is demonstrated that seemingly long-range temporal correlations in the growth of online social networks, such as in Gowalla, can be explained by a decomposition into temporally and spatially independent growth processes with a large variety of entry rates.
Social contagions on interdependent lattice networks
A novel non-Markovian social contagion model on interdependent spatial networks composed of two identical two-dimensional lattices is presented and it is found that the density of final recovered nodes increases as the number of dependency links is increased.


Microscopic evolution of social networks
A complete model of network evolution, where nodes arrive at a prespecified rate and select their lifetimes, and the combination of the gap distribution with the node lifetime leads to a power law out-degree distribution that accurately reflects the true network in all four cases is presented.
Empirical Analysis of an Evolving Social Network
This work analyzed a dynamic social network comprising 43,553 students, faculty, and staff at a large university, in which interactions between individuals are inferred from time-stamped e-mail headers recorded over one academic year and are matched with affiliations and attributes.
Group formation in large social networks: membership, growth, and evolution
It is found that the propensity of individuals to join communities, and of communities to grow rapidly, depends in subtle ways on the underlying network structure, and decision-tree techniques are used to identify the most significant structural determinants of these properties.
Analyzing Affiliation Networks
In social network analysis, the term “affiliations” usually refers to membership or participation data, such as when we have data on which actors have participated in which events. Often, the
Detecting rich-club ordering in complex networks
The presented analysis enables the measurement of the rich-club ordering and its relation with the function and dynamics of networks in examples drawn from the biological, social and technological domains.
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters
This paper employs approximation algorithms for the graph-partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities, and defines the network community profile plot, which characterizes the "best" possible community—according to the conductance measure—over a wide range of size scales.
Social Network Sensors for Early Detection of Contagious Outbreaks
This paper proposes an alternative strategy that does not require ascertainment of global network structure, namely, simply monitoring the friends of randomly selected individuals, which could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks.
Preferential attachment in sexual networks
The PA model is modified to account for individual heterogeneity in the inclination to find new partners and fitted to Norwegian survey data on heterosexual men and women, showing evidence of nonrandom, sublinear PA when comparing the growth in 3- to 5-year periods.