Spreading Dynamics in Social Systems

A Springer Nature book edited by Sune Lehmann and Yong-Yeol Ahn


This page contains information for authors of the forthcoming Spreading Dynamics in Social Systems. Below, you can find the current Table of Contents, timeline, and the LaTeX template.

Table of Contents

Part 1: Introduction to spreading in social systems

Part 2: Models and Theories

Part 3: Observational studies

Part 4: Controlled studies


We will be using Springer’s LaTeX package for typesetting manuscripts. You can find full info on this site


including the style guide, etc. We’re using the LaTeX package for contributed books and if you’re lazy, you can download the package directly via this link.


Part 1: Introduction to spreading in social systems

Doug Guilbeault, Joshua Becker and Damon Centola: Complex contagions: A decade in review

Peter Sheridan Dodds: Understanding the global spreading condition for simple models of spreading

This chapter lays out how the the condition for global spreading from a single seed can be derived in a physically clear way in core random network models. Should create a framework for how researchers approach more complex kinds of spreading on real-world networks. The line of thinking will apply for generalized random networks, bipartite networks, temporal networks, and multilayered networks.

Part 2: Models and Theories

James Gleeson and Mason Porter: Message-passing methods for complex contagions

Message-passing methods can be applied to calculate the expected size of cascades both on random networks (e.g. drawn from the configuration model ensemble) and on specific finite networks (e.g. Shrestha and Moore PRE 2014, Lokhov et al. PRE 2015). We review the message-passing approach and show how it can be easily derived for configuration model networks using the methods of (Gleeson PRE 2008, Dhar J. Phys A 1997). Using this approach, we explain how the analytical “cascade condition” is determined for such cases. We extend this approach to the message-passing methods for specific finite networks, and derive the generalized cascade condition that determines whether a global cascade will occur on a given network. To provide context, we’ll also indicate briefly other types of insights that message-passing methods can give and discuss limitations of the approach.

Peter Sheridan Dodds: Generalized contagion: Unifying simple models of social and biological spreading

Present and motivate the generalized contagion model originally introduced by Dodds and Watts in 2004. Give a cleaner explanation of the global spreading condition that was originally put forward.

Azadeh Nematzadeh, Nathaniel Rodriguez, Alessandro Flammini, and Yong-Yeol Ahn: Optimal modularity in complex contagion

In this chapter, we apply the theoretical framework introduced in the previous chapter to study the impact of modularity in the spreading of complex contagion. In particular, we focus on the phenomenon of optimal modularity, where global cascade occurs when the network exhibit just the right amount of modularity. Here we further generalize the original finding by investigating the case where there are multiple communities and the seeds are randomly distributed across the network. Then we also provide insights into the temporal aspects of the optimal modularity phenomenon.

Petter Holme: Probing empirical contact networks by simulation of spreading dynamics

In this chapter, we will review the methods and techniques to understand the role of temporal and topological structures on spreading phenomena. We survey randomization techniques and the subtle issues when adapting compartmental or threshold models to temporal networks. We furthermore discuss how to best represent contact data as static networks. Finally, we go through some of the general results of such studies.

Sen Pei, Flaviano Morone, and Hernan Makse: Influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platform, scientific publication, brain network and socioeconomic system.

Part 3: Observational studies

Gerardo Iñiguez, Kimmo Kaski, János Kertész and Márton Karsai: Service adoption spreading in online social networks

Adoption of innovations, products or online services is commonly interpreted as a spreading process that is arguably driven by social influence, external effects of media, and conditioned by the needs and capacities of individuals. Observations of such processes date back to the seminal studies of Rogers and Bass, and their modelling has taken two directions: One paradigm, called simple contagion, identifies adoption spreading similar to epidemic processes, while the other, named complex contagion, is concerned with behavioural thresholds, and successfully explains global adoption cascades commonly observed in real Data. The observation of real service adoption processes has become easier lately due to the availability of large digital social network and behavioural datasets. This has allowed the simultaneous study of network structure and ongoing service adoption dynamics, which has shed light on various mechanisms and external effects influencing the dynamics of global adoption. These advancements have induced the development of more realistic models of social spreading phenomena, which in turn have provided remarkably good predictions of real adoption cases. In this chapter we first review recent data-driven studies addressing real cases of service adoption processes. Then we study the modelling of such phenomena with formal methods and data-driven simulations. Our objective is to understand the effects of identified social mechanisms on the final outcome of service adoption spreading, and to provide potential new directions and open questions for future research.

Walter Quattrociocchi: (Mis)information spreading on Facebook

The diffusion of false claims on social media as well as the pivotal role of confirmation bias in informational cascades are among the most important topics addressed by computational social science. In this chapter, by focusing on both Italian and US Facebook users interacting with two different and contrasting narratives (involving scientific and conspiracy-like information), we provide quantitative evidence of the existence and emergence of echo chambers on online social media. Confirmation bias dominates spreading processes and define group of interests where users crystalize their beliefs. Our findings, indeed, show the users’ tendency to select information that is coherent to their system of beliefs and to form segregated groups of like-minded people where they reinforce and polarize their pre-existing beliefs. We offer empirical evidence that confirmatory information gets accepted even when containing deliberately false claims, while dissenting information is mainly ignored. The interaction within like-minded people seems to negatively influences users’ emotions and to increase group polarization. Finally, we provide evidence that misinformation cascades might be linked to highly polarizing topics fomenting hated debates online.

Pikmai Hui, Lilian Weng, Alireza Sahami, YY Ahn, Filippo Menczer: Predicting viral memes

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We show that community concentration is a predictor of virality. Our methods scale up to very large networks and our findings generalize to multiple platforms (Twitter and Tumblr).

Lilian Weng, Márton Karsai, Nicola Perra, Filippo Menczer, Alessandro Flammini: Attention on Weak Ties in Social and Communication Networks

We present empirical support for both of Granovetter’s weak tie hypotheses: (1) strong social ties carry the large majority of interaction events; (2) weak social ties are often relevant for the exchange of especially important information. We show that attention is high on weak ties but also on very strong ties. Data from online social media and mobile communication reveal network-dependent mixtures of these two effects on the basis of a platform’s typical usage. See also: https://arxiv.org/abs/1505.02399

Emilio Ferrara: Measuring the effect of social bots on information diffusion in social media

Social bots have been playing a crucial role in online platform ecosystems, as efficient and automatic tools to generate content and diffuse information to the social media human population. In this chapter, I will discuss the role of social bots in content spreading dynamics in social media. I will investigate, in particular, the difference between diffusion dynamics of content generated by bots, as opposed to humans, in the context of political and other types of social media campaigns.

Johan Bollen and Bruno Goncalves: Network Happiness: How Social Interactions Impact our Well Being

As social animals, we rely on our communities to support and sustain us. As a result, our social interactons play a fundamental role in how we live our lives and in shaping our emotional states and general well being. The recent developments in Online Social Networks have allowed us to, for the first time, have an extensive detailed record of a well defined subset of our social structures and behaviors. In this chapter we review several recent results on the structure of the social networks of which we are all a part of. In particular we will analyze how simple mechanisms of network formation such as Preferential Attachement result in the broad tailed degree distributions and assortativity that are characteristic of this kind of networks. A result of positive degree assortativity is the fact that hubs are tendentially connected to by lower degree nodes, a fact that has become known as the Friendship Paradox. Finally, we consider as well a mesure of Subjective Well Being through an analysis of the content written by each user over an extended period of time. We find that Happiness is correlated with popularity within the network resulting also in a Happiness Paradox

James P Bagrow: Examining human dynamics by contrasting normal and anomalous activities during normal and anomalous events

Essentially, the goal is to describe work where we can use emergency events compared with normal events to better understand what people are doing, how better understanding normal activity helps us understand anomalous activity and vice versa, and how contrasting the two is helpful for this. I will discuss the emergency paper I did, present some other data mining for emergencies work I have left, perhaps review some other work people have done on Twitter during events, etc. I have a result about the friendship paradox that may be able to go in as well.

Part 4: Controlled studies

Sean Taylor: Measuring Social Influence with Randomized Experiments

Measurements of the effects of social influence can be substantially biased in observational studies due to assortative mixing in social networks and network correlation in exposure to exogenous events. Randomized experiments, where the researcher intervenes in the social system and uses randomization to determine how to do so, provide a methodology to measure unbiased estimates of causal effects of social behaviors. These estimates form the basis for effective public policy and advertising, as well as help answer deep questions about social science. In this chapter we motivate a taxonomy of experiments to measure social influence effects through various combinations of interventions and randomizations. We define an experiment as combination of 1) a target population of individuals connected by an observed interaction network, 2) a set of treatments whereby the researcher will intervene in the social system, 3) a randomization strategy which maps sets of individuals or edges to treatments, and 4) a measurement of an outcome of interest after treatment has been assigned. We review both recent and older research which demonstrate potential experimental designs and evaluate their advantages and drawbacks for answering different types of causal questions about social influence.

Yan Leng, Xiaowen Dong, and Alex Pentland: Mapping Behavior Influence using Matched and Balanced Samples

Causal inference is a difficult problem that is best understood within a Kolmogorov complexity framework. Taking this perspective illuminates the shortcomings of techniques like Randomized Controlled Trials, and suggests techniques such as matched or balanced sampling may often be more powerful and are often easier to use for large population studies. We illustrate this work in two cellphone based studies where we track the behavioral effects of exposure within a social network.

Robert Bond, Chris Fariss, Jason Jones, Jaime Settle: Network Experiments through Academic-Industry Collaboration

Our main goal in this chapter is to summarize and describe our work on get-out-the-vote experiments run on the Facebook social media platform. We ran randomized experiments and observed both direct effects – a message on Election Day made Facebook users more likely to vote – and cascading effects in the social network – the friends of treated users became more likely to vote. Collaborating with Facebook vastly increased the scope of our research project from what we originally planned. We will also discuss why academic collaboration with industry is not only important in general, but particularly important for understanding complex social systems. We will conclude with a discussion of some of the opportunities we see for scientific advancement in this area.