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Structural Diversity In Social Contagion

J. Ugander, L. Backstrom, C. Marlow, J. Kleinberg
Published 2012 · Psychology, Computer Science, Medicine

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The concept of contagion has steadily expanded from its original grounding in epidemic disease to describe a vast array of processes that spread across networks, notably social phenomena such as fads, political opinions, the adoption of new technologies, and financial decisions. Traditional models of social contagion have been based on physical analogies with biological contagion, in which the probability that an individual is affected by the contagion grows monotonically with the size of his or her “contact neighborhood”—the number of affected individuals with whom he or she is in contact. Whereas this contact neighborhood hypothesis has formed the underpinning of essentially all current models, it has been challenging to evaluate it due to the difficulty in obtaining detailed data on individual network neighborhoods during the course of a large-scale contagion process. Here we study this question by analyzing the growth of Facebook, a rare example of a social process with genuinely global adoption. We find that the probability of contagion is tightly controlled by the number of connected components in an individual's contact neighborhood, rather than by the actual size of the neighborhood. Surprisingly, once this “structural diversity” is controlled for, the size of the contact neighborhood is in fact generally a negative predictor of contagion. More broadly, our analysis shows how data at the size and resolution of the Facebook network make possible the identification of subtle structural signals that go undetected at smaller scales yet hold pivotal predictive roles for the outcomes of social processes.
This paper references
Structural Holes: The SocialStructure of Competition (Harvard
R Burt (1992)
10.1086/226707
Threshold Models of Collective Behavior
Mark S. Granovetter (1978)
10.1142/9781848168770_0005
Random Graphs
B. Bollobás (1985)
10.1038/nature05670
Quantifying social group evolution
G. Palla (2007)
10.1016/0012-365X(91)90162-U
Size and connectivity of the k-core of a random graph
T. Łuczak (1991)
10.1086/521848
Complex Contagions and the Weakness of Long Ties1
Damon Centola (2007)
Large scale networks fingerprinting and visualization using the k-core decomposition
José Ignacio Alvarez-Hamelin (2005)
10.1145/1250790.1250811
On the submodularity of influence in social networks
Elchanan Mossel (2007)
Gesundheit! Modeling Contagion through Facebook News Feed
E. Sun (2009)
Random Graphs (Cambridge
B Bollobás (2001)
10.1002/(SICI)1097-4571(1998)49:12%3C1101::AID-ASI6%3E3.0.CO;2-Z
Work, Friendship, and Media Use for Information Exchange in a Networked Organization
C. Haythornthwaite (1998)
Trusses: Cohesive subgraphs for social network analysis
JD Cohen (2008)
10.1145/1109557.1109663
Random graphs
A. Frieze (2006)
10.1073/pnas.0908800106
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
S. Aral (2009)
10.1016/j.physa.2006.06.018
Cascade Dynamics of Complex Propagation
Damon Centola (2007)
10.1073/pnas.1006155107
Inferring social ties from geographic coincidences
David J. Crandall (2010)
10.1103/PhysRevLett.92.218701
Universal behavior in a generalized model of contagion.
P. S. Dodds (2004)
Association for the Advancement of Artificial Intelligence
M. Crosby (2014)
Interacting Particle Systems (Springer, Berlin)
T Liggett (1985)
10.2307/587152
Conflict and the Web of Group Affiliations
D. Macrae (1955)
10.1103/PhysRevLett.86.3200
Epidemic spreading in scale-free networks.
R. Pastor-Satorras (2001)
Ten Lectures on Particle Systems (Springer, Berlin)
R Durrett (1995)
10.1017/9781108528986.011
Interacting particle systems
Stefan Grosskinsky Warwick (2009)
10.1073/PNAS.012582999
Random graph models of social networks
M. Newman (2002)
10.1080/0022250X.1971.9989794
Dynamic models of segregation
T. Schelling (1971)
10.1056/NEJMSA066082
The spread of obesity in a large social network over 32 years.
N. Christakis (2007)
10.1007/BFB0095747
Ten lectures on particle systems
R. Durrett (1995)
10.1086/228667
Social Contagion and Innovation: Cohesion versus Structural Equivalence
R. Burt (1987)
10.1073/pnas.0913149107
Cooperative behavior cascades in human social networks
J. Fowler (2010)
10.1086/518527
Influentials, Networks, and Public Opinion Formation
D. Watts (2007)
Interacting Particle Systems
T. Te (2013)
10.1002/rsa.20147
A simple solution to the k-core problem
S. Janson (2007)
10.1002/(SICI)1097-4571(1998)49:12<1101::AID-ASI6>3.3.CO;2-S
Work, friendship, and media use for information exchange in a networked organization
C. Haythornthwaite (1998)
Structural Holes: The SocialStructure of Competition
R Burt (1992)
Size and connectivity of the k-core of a random graph
ŁuczakTomasz (1991)
Threshold models of collective action
M Granovetter (1978)
10.1016/0012-365X(91)90162-U
Size and connectivity of the k-core of a random graph
T. Luczak (1991)
10.1145/1150402.1150412
Group formation in large social networks: membership, growth, and evolution
L. Backstrom (2006)
10.1086/225469
The Strength of Weak Ties
Mark S. Granovetter (1973)
10.1126/SCIENCE.1127207
An Experimental Study of the Coloring Problem on Human Subject Networks
M. Kearns (2006)
10.1126/SCIENCE.1116869
Empirical Analysis of an Evolving Social Network
Gueorgi Kossinets (2006)
10.1073/pnas.0701175104
A model of Internet topology using k-shell decomposition
S. Carmi (2007)



This paper is referenced by
Network based interpersonal influences on online casual game choices
S. Y. Lee (2014)
Influence of media on collective debates
Walter Quattrociocchi (2013)
10.1088/1742-6596/1345/3/032054
User Departure Behavior Prediction in Social Group
Jiayi Liu (2019)
10.1109/CoDIT.2016.7593641
On models of ‘having friends’ and SN friends distribution: Information propagation on social networks and disaster modeling
Horia-Nicolai Teodorescu (2016)
10.1007/978-3-319-03578-9_12
Influence Diffusion in Social Networks under Time Window Constraints
L. Gargano (2013)
The Lifecycle and Cascade of Social Messaging Groups
Jiezhong Qiu (2015)
10.1109/ICDE48307.2020.00023
Exploring Finer Granularity within the Cores: Efficient (k,p)-Core Computation
Chen Zhang (2020)
10.5441/002/edbt.2019.29
Efficient Computation of Probabilistic Core Decomposition at Web-Scale
Fatemeh Esfahani (2019)
10.1016/j.artint.2014.06.004
On influence, stable behavior, and the most influential individuals in networks: A game-theoretic approach
M. Irfan (2014)
Identifying significant behaviour in complex bipartite networks
J. Liebig (2016)
End-to-end Learning for Mining Text and Network Data
Cheng Li (2017)
10.1007/978-3-319-92312-3_11
Network Based Targeting
Y. Ouyang (2018)
Social Influence Locality for Modeling Retweeting Behaviors
J. Zhang (2013)
10.1145/3316809
Polarization and Fake News: Early Warning of Potential Misinformation Targets
Michela Del Vicario (2019)
10.1371/journal.pone.0163914
An Examination of Not-For-Profit Stakeholder Networks for Relationship Management: A Small-Scale Analysis on Social Media
J. Wyllie (2016)
10.1016/J.PHYSA.2015.09.028
A modified weighted TOPSIS to identify influential nodes in complex networks
Jiantao Hu (2016)
10.11610/isij.4327
Diffusion of Information in an Online Social Network with Limited Attention
Diego F. M. Oliveira (2019)
10.1109/WI-IAT.2013.61
Incorporating Structural Diversity of Neighbors in a Diffusion Model for Social Networks
Qing Bao (2013)
Inferring Motif-Based Diffusion Models for Social Networks
Qing Bao (2016)
Coupled Graph Neural Networks for Predicting the Popularity of Online Content
Qi Cao (2019)
10.1145/3328526.3329612
Displaying Things in Common to Encourage Friendship Formation: A Large Randomized Field Experiment
Tianshu Sun (2019)
10.1007/978-3-319-72150-7_30
Toward a Better Understanding of Emotional Dynamics on Facebook
F. Zollo (2017)
10.2139/ssrn.2542032
Predicting Social Influence Based on Dynamic Network Structures
Mandy Hu (2014)
10.1007/978-3-642-54484-2_17
Guerrilla Media: Interactive Social Media
A. Duh (2016)
10.1209/0295-5075/110/68006
Vulnerability assessment in social networks under cascade-based node departures
Fragkiskos D. Malliaros (2015)
10.1109/CCNC.2019.8651770
PUBLISH: A Distributed Service Advertising Scheme for Vehicular Cloud Networks
B. Brik (2019)
10.1145/2470654.2466449
Understanding motivations for facebook use: usage metrics, network structure, and privacy
Tasos Spiliotopoulos (2013)
10.1080/07421222.2019.1628878
Social Influence and Monetization of Freemium Social Games
Bin Fang (2019)
Generalizations of Threshold Graph Dynamical Systems
D. Carlson (2013)
10.1016/j.tcs.2015.02.015
Influence diffusion in social networks under time window constraints
L. Gargano (2015)
10.1109/ASONAM.2016.7752243
Learning cascaded influence under partial monitoring
J. Zhang (2016)
10.1088/1757-899X/537/4/042003
Genetic algorithm based sentence packaging in natural language text generation
D. Devyatkin (2019)
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