This text is about spreading of information and influence in complex networks. Although previously considered similar and modeled in parallel approaches, there is now experimental evidence that epidemic and social spreading work in subtly different ways. While previously explored through modeling, there is currently an explosion of work on revealing the mechanisms underlying complex contagion based on big data and data-driven approaches.
This volume consists of four parts. Part 1 is an Introduction, providing an accessible summary of the state of the art. Part 2 provides an overview of the central theoretical developments in the field. Part 3 describes the empirical work on observing spreading processes in real-world networks. Finally, Part 4 goes into detail with recent and exciting new developments: dedicated studies designed to measure specific aspects of the spreading processes, often using randomized control trials to isolate the network effect from confounders, such as homophily.
Each contribution is authored by leading experts in the field. This volume, though based on technical selections of the most important results on complex spreading, remains quite accessible to the newly interested. The main benefit to the reader is that the topics are carefully structured to take the novice to the level of expert on the topic of social spreading processes. This book will be of great importance to a wide field: from researchers in physics, computer science, and sociology to professionals in public policy and public health.
Part 1: Introduction to spreading in social systems.- Complex
contagions: A decade in review.- A simple persons approach to understanding
the contagion condition for spreading processes on generalized random
networks.- Challenges to estimating contagion effects from observational
data.- Part 2: Models and Theories.- Slightly generalized Generalized
Contagion: Unifying simple models of biological and social spreading.-
Message-passing methods for complex contagions.- Optimal modularity in
complex contagion.- Probing empirical contact networks by simulation of
spreading dynamics.- Theories for influencer identification in complex
networks.- Part 3: Observational studies.- Service adoption spreading in
online social networks.- Misinformation spreading on Facebook.- Scalable
detection of viral memes from diffusion patterns.- Attention on weak ties in
social and communication networks.- Measuring social spam and the effect of
bots on information diffusion in social media.- Network happiness: how online
social interactions relate to our well being.- Information spreading during
emergencies and anomalous events.- Part 4: Controlled studies.- Randomized
Experiments to detect and estimate social influence.- The rippling effect of
social influence via phone communication network.- Network experiments
through academic-industry collaboration.-Spreading in Social Systems:
Reflections.
Sune Lehmann is an associate professor at the Technical University of Denmark, an adjunct (full) professor at the University of Copenhagens Department of Sociology, and and adjunct associate professor at the Niels Bohr Institute for Theoretical Physics. Hes also associate director of the interdisciplinary "Center for Social Data Science" at the University of Copenhagen. In addition to publishing in top interdisciplinary journals, Prof Lehmanns work on spreading processes including spreading in both biological and social domains has received world-wide press coverage. Yong-Yeol (YY) Ahn is an assistant professor at Indiana University School of Informatics, Computing, and Engineering. He worked as a postdoctoral research associate at the Center for Complex Network Research at Northeastern University and as a visiting researcher at the Center for Cancer Systems Biology at Dana-Farber Cancer Institute after earning his PhD in Statistical Physics from KAIST in 2008. He has made contributions in a variety of areas including the study of network community structure, information diffusion, and culture. He is a recipient of several awards, including the Microsoft Research Faculty Fellowship and the LinkedIn Economic Graph Challenge.
