Network Science

Applied Network Science

Ronen et al. (2014): "Links that speak: The global language network and its association with global fame" link
summary
This paper presents an interesting methodology for evaluating the importance of a language in the network of world languages. The paper uses tweets that are published in multiple languages, wikipedia articles that are translated into multiple languages, and book that are translated into multiple languages to create three co-occurrence networks of languages. The authors then produce visualizations of these networks, evaluate the centrality of each language in each network, and compare the centrality of each language to the prominence of the individuals who are associated with that language. The results are intuitive, and the visualizations are nice.

Asynchronous Network Models

Du et al. (2013): "Scalable Influence Estimation in Continuous-Time Diffusion Networks" pdf
Gomez-Rodriguez and Scholkopf (2012): "Influence Maximization in Continuous Time Diffusion Networks" pdf

Cascades

Leskovec et al. (2007): "Patterns of Cascading Behavior in Large Blog Graphs" link

Citation Networks

Newman (2013): "Prediction of highly cited papers" pdf

Community Detection

Leskovec et al. (2009): "Large-scale community structure in social and information networks" pdf

Complex Contagion

Hodas and Lerman (2013): "The Simple Rules of Social Contagion" link

Data Collection

Sekara and Lehmann (2014): "Application of network properties and signal strength to identify face-to-face links in an electronic dataset" link

Dynamical Systems

Porter and Gleeson (2014): "Dynamical Systems on Networks: A Tutorial" link

Experiments

Aral and Walker (2014): "Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment" link

Friendship Paradox

Eom and Jo (2014): "Generalized friendship paradox in complex networks: The case of scientific collaboration" link
Grund (2014): "Why Your Friends Are More Important and Special Than You Think" pdf
Kooti et al. (2014): "Network Weirdness: Exploring the Origins of Network Paradoxes" link

Methodological Issues

Aral et al. (2009): "Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks" link
Chatterjee and Diaconis (2011): "Estimating and understanding exponential random graph models" link
Howison et al. (2011): "Validity Issues in the Use of Social Network Analysis with Digital Trace Data" link
Shalizi and Rinaldo (2011): "CONSISTENCY UNDER SAMPLING OF EXPONENTIAL RANDOM GRAPH MODELS" pdf
Shalizi and Thomas (2010): "Homophily and Contagion Are Generically Confounded in Observational Social Network Studies" pdf
Ver Steeg and Galstyan (2013): "Statistical Tests for Contagion in Observational Social Network Studies" pdf

Network Dynamics

Barzel and Barabasi (2013): "Universality in network dynamics" link

Rumors

Adamic et al. (2014): "Information Evolution in Social Networks" pdf

Statistical Network Models

Atwood (2014): "Learning to Generate Networks" pdf
summary
This paper suggests using RBMs instead of ERGMs as statistical network models. RBMs are like ERGMs but with inferred features instead of a priori assumed features.

Lee et al. (2015): "Preferential Attachment in Graphs with Affinities" link
summary
The authors present a random graph model that displays scale-free degree distributions, creates small world networks, and also incorporates homophily between nodes. The authors also give an inference algorithm for learning the similarity function that is drivingn homophily.

Snijders (1996): "Stochastic actor-oriented models for network change" pdf
Peter M Krafft Last modified: Mon Dec 29 11:31:52 EST 2014