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Studies by [[Nicholas A. Christakis|Christakis]] and [[James H. Fowler|Fowler]] suggested that a variety of attributes—like obesity,<ref name="CF Obesity">{{cite journal|title=The Spread of Obesity in a Large Social Network over 32 Years |journal=The New England Journal of Medicine |last1=Christakis |first1=Nicholas A. |last2=Fowler |first2=James H. |volume=357 |pages=370–379 |doi=10.1056/NEJMsa066082 |year=2007 |pmid=17652652 |issue=4}}</ref> smoking cessation,<ref name="CF Smoking">{{cite journal|title=The Collective Dynamics of Smoking in a Large Social Network |journal=The New England Journal of Medicine |last1=Christakis |first1=Nicholas A. |last2=Fowler |first2=James H. |volume=358 |year=2008 |doi=10.1056/NEJMsa0706154 |pmid=18499567 |pmc=2822344}}</ref> and happiness<ref>{{cite journal|title=Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study |journal=British Medical Journal |last1=Christakis |first1=Nicholas A. |last2=Fowler |first2=James H. |issue=337 |doi=10.1136/bmj.a2338 |pmid=19056788 |pmc=2600606 |volume=337 |year=2008 |pages=a2338}}</ref>—rather than being individualistic, are casually correlated by contagion mechanisms that transmit these behaviors over long distances within social networks.<ref>{{cite book|title=Connected:The Surprising Power of Our Social Networks and How They Shape Our Lives |last1=Christakis |first1=Nicholas A. |last2=Fowler |first2=James H. |year=2009 |publisher=Little, Brown and Co. |isbn=978-0316036146}}</ref> While certain subsequent analyses suggested limitations to these analyses (subject to different statistical assumptions);<ref>{{cite journal|title=Detecting implausible social network effects in acne, height, and headaches: longitudinal analysis |last1=Cohen-Cole |first1=Ethan |last2=Fletcher |first2=Jason M. |journal=British Medical Journal |year=2008 |doi=10.1136/bmj.a2533}}</ref> or expressed concern that the Christakis-Fowler analyses did not fully control for other environmental factors;<ref>{{cite journal|title=Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic |last1=Cohen-Cole |first1=Ethan |last2=Fletcher |first2=Jason M. |journal=Journal of Health Economics |volume=27 |year=2008 |pages=1382–1387 |doi=10.1016/j.jhealeco.2008.04.005}}</ref> or mis-interpreted statistical estimates;<ref>{{cite journal|last1=Lyons |first1=Russell |year=2011 |title=The Spread of Evidence-Poor Medicine via Flawed Social Network Analysis |journal=Statistics, Politics, and Policy |volume=2 |issue=1 |doi=10.2202/2151-7509.1024}}</ref> or did not fully account for homophily processes in the creation and retention of relationships over time;<ref>{{cite journal|last1=Noel |first1=Hans |last2=Nyhan |first2=Brendan |title=The 'unfriending problem': The consequences of homophily in friendship retention for causal estimates of social influence |journal=Social Networks |volume=33 |issue=3 |pages=211–218 |year=2011 |doi=10.1016/j.socnet.2011.05.003}}</ref><ref name="ReferenceA">{{cite journal |title=Homphily and Contagion Are Generically Confounded in Observational Social Network Studies |last1=Shalizi |first1=Cosma R. |last2=Thomas |first2=Andrew C. |year=2011 |volume=40 |issue=2 |journal=Sociological Methods & Research |pages=211–239 |doi=10.1177/0049124111404820}}</ref> other scholarship using [[sensitivity analysis]] has found that the core findings regarding the transmissibility of obesity and smoking cessation are robust,<ref>{{cite journal|title=Sensitivity Analysis for Contagion Effects in Social Networks |last1=VanderWeele |first1=Tyler J. |journal=Sociological Methods & Research |volume=40 |issue=2 |pages=240–255 |doi=10.1177/0049124111404821}}</ref><ref name="ReferenceB">Christakis NA and Fowler JH, "Social Contagion Theory: ExaminingDynamic Social Networks and Human Behavior," Statistics in Medicine 2013; 32: 556-577</ref> or has otherwise replicated or supported the findings.<ref>MM Ali, A Amialchuk, S Gao, and F Heiland, "Adolescent Weight Gain and Social Networks: Is There a Contagion Effect?," Applied Economics 2012; 44: 2969-2983</ref><ref name="ReferenceC">G ver Steeg, A. Galstyan, "Statistical Tests for Contagion in Observational Social Network Studies," Journal of Machine Learning Research 2012; 563-571</ref> Christakis and Fowler reviewed critical and supportive findings in 2013.<ref name="ReferenceB"/> Moreover, a 2012 paper by physicists ver Steeg and Galstyan suggests it may be possible to bound estimates of peer effects <ref name="ReferenceC"/> even if parametric assumptions are otherwise required to identify such effects using observational data (if indeed substantial unobserved homophily is thought to be present).<ref name="ReferenceA"/> Additional support for the modeling approach used by Christakis and Fowler provided by other authors has continued to appear,<ref>A. Gonzalez-Pardo, R. Cajias, D. Camacho, "An Agent Based Simulation of Christakis-Fowler Social Model," Recent Developments in Computational Collective Intelligence, 2014; 513: 69-77</ref> including of the three-degrees-of-influence property.<ref>http://www.cbma.bio.uminho.pt/files/Pacheco-Manuscript.pdf</ref>
Studies by [[Nicholas A. Christakis|Christakis]] and [[James H. Fowler|Fowler]] suggested that a variety of attributes—like obesity,<ref name="CF Obesity">{{cite journal|title=The Spread of Obesity in a Large Social Network over 32 Years |journal=The New England Journal of Medicine |last1=Christakis |first1=Nicholas A. |last2=Fowler |first2=James H. |volume=357 |pages=370–379 |doi=10.1056/NEJMsa066082 |year=2007 |pmid=17652652 |issue=4}}</ref> smoking cessation,<ref name="CF Smoking">{{cite journal|title=The Collective Dynamics of Smoking in a Large Social Network |journal=The New England Journal of Medicine |last1=Christakis |first1=Nicholas A. |last2=Fowler |first2=James H. |volume=358 |year=2008 |doi=10.1056/NEJMsa0706154 |pmid=18499567 |pmc=2822344}}</ref> and happiness<ref>{{cite journal|title=Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study |journal=British Medical Journal |last1=Christakis |first1=Nicholas A. |last2=Fowler |first2=James H. |issue=337 |doi=10.1136/bmj.a2338 |pmid=19056788 |pmc=2600606 |volume=337 |year=2008 |pages=a2338}}</ref>—rather than being individualistic, are casually correlated by contagion mechanisms that transmit these behaviors over long distances within social networks.<ref>{{cite book|title=Connected:The Surprising Power of Our Social Networks and How They Shape Our Lives |last1=Christakis |first1=Nicholas A. |last2=Fowler |first2=James H. |year=2009 |publisher=Little, Brown and Co. |isbn=978-0316036146}}</ref> While certain subsequent analyses suggested limitations to these analyses (subject to different statistical assumptions);<ref>{{cite journal|title=Detecting implausible social network effects in acne, height, and headaches: longitudinal analysis |last1=Cohen-Cole |first1=Ethan |last2=Fletcher |first2=Jason M. |journal=British Medical Journal |year=2008 |doi=10.1136/bmj.a2533}}</ref> or expressed concern that the Christakis-Fowler analyses did not fully control for other environmental factors;<ref>{{cite journal|title=Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic |last1=Cohen-Cole |first1=Ethan |last2=Fletcher |first2=Jason M. |journal=Journal of Health Economics |volume=27 |year=2008 |pages=1382–1387 |doi=10.1016/j.jhealeco.2008.04.005}}</ref> or mis-interpreted statistical estimates;<ref>{{cite journal|last1=Lyons |first1=Russell |year=2011 |title=The Spread of Evidence-Poor Medicine via Flawed Social Network Analysis |journal=Statistics, Politics, and Policy |volume=2 |issue=1 |doi=10.2202/2151-7509.1024}}</ref> or did not fully account for homophily processes in the creation and retention of relationships over time;<ref>{{cite journal|last1=Noel |first1=Hans |last2=Nyhan |first2=Brendan |title=The 'unfriending problem': The consequences of homophily in friendship retention for causal estimates of social influence |journal=Social Networks |volume=33 |issue=3 |pages=211–218 |year=2011 |doi=10.1016/j.socnet.2011.05.003}}</ref><ref name="ReferenceA">{{cite journal |title=Homphily and Contagion Are Generically Confounded in Observational Social Network Studies |last1=Shalizi |first1=Cosma R. |last2=Thomas |first2=Andrew C. |year=2011 |volume=40 |issue=2 |journal=Sociological Methods & Research |pages=211–239 |doi=10.1177/0049124111404820}}</ref> other scholarship using [[sensitivity analysis]] has found that the core findings regarding the transmissibility of obesity and smoking cessation are robust,<ref>{{cite journal|title=Sensitivity Analysis for Contagion Effects in Social Networks |last1=VanderWeele |first1=Tyler J. |journal=Sociological Methods & Research |volume=40 |issue=2 |pages=240–255 |doi=10.1177/0049124111404821}}</ref><ref name="ReferenceB">Christakis NA and Fowler JH, "Social Contagion Theory: ExaminingDynamic Social Networks and Human Behavior," Statistics in Medicine 2013; 32: 556-577</ref> or has otherwise replicated or supported the findings.<ref>MM Ali, A Amialchuk, S Gao, and F Heiland, "Adolescent Weight Gain and Social Networks: Is There a Contagion Effect?," Applied Economics 2012; 44: 2969-2983</ref><ref name="ReferenceC">G ver Steeg, A. Galstyan, "Statistical Tests for Contagion in Observational Social Network Studies," Journal of Machine Learning Research 2012; 563-571</ref> Christakis and Fowler reviewed critical and supportive findings in 2013.<ref name="ReferenceB"/> Moreover, a 2012 paper by physicists ver Steeg and Galstyan suggests it may be possible to bound estimates of peer effects <ref name="ReferenceC"/> even if parametric assumptions are otherwise required to identify such effects using observational data (if indeed substantial unobserved homophily is thought to be present).<ref name="ReferenceA"/> Additional support for the modeling approach used by Christakis and Fowler provided by other authors has continued to appear,<ref>A. Gonzalez-Pardo, R. Cajias, D. Camacho, "An Agent Based Simulation of Christakis-Fowler Social Model," Recent Developments in Computational Collective Intelligence, 2014; 513: 69-77</ref> including of the three-degrees-of-influence property.<ref>http://www.cbma.bio.uminho.pt/files/Pacheco-Manuscript.pdf</ref>


In addition, subsequent studies (by many research groups, including Christakis and Fowler) have found strong causal evidence of behavioral contagion processes (including those that spread beyond dyads, out to two, three, or four degrees) using [[Randomized controlled trial|randomized controlled experiments]],<ref>{{cite journal|last1=Centola |first1=Damon |title=The Spread of Behavior in an Online Social Network Experiment |journal=Science |volume=329 |issue=5995 |pages=1194–1197 |doi=10.1126/science.1185231 |year=2010}}</ref><ref>{{cite journal|title=An experimental study of homophily in the adoption of health behavior |journal=Science |last1=Centola |first1=Damon |year=2011 |volume=334 |issue=6060 |pages=1269–1272 |doi=10.1126/science.1207055}}</ref><ref>{{cite journal|last2=Christakis |first2=Nicholas A. |last1=Fowler |first1=James H. |title=Cooperative behavior cascades in human social networks |journal=Proceedings of the National Academy of Sciences |volume=107 |issue=12 |pages=5334–5338 |doi=10.1073/pnas.0913149107 |year=2010 |pmid=20212120 |pmc=2851803}}</ref><ref>{{cite journal |last1=Aral |first1=Sinan |last2=Walker |first2=Dylan |title=Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks |journal=Management Science |year=2011 |volume=57 |issue=9 |pages=1623–1639 |doi=10.1287/mnsc.1110.1421}}</ref><ref>Rand D, Arbesman S, and Christakis NA,"Dynamic Social Networks Promote Cooperation in Experiments with Humans," PNAS:Proceedings of the National Academy of Sciences 2011; 108: 19193-19198</ref> including one experiment involving 61,000,000 people that showed spread of voting behavior out to two degrees of separation,<ref>{{cite journal | last1 = Bond | first1 = RM | last2 = Fariss | first2 = CJ | last3 = Jones | first3 = JJ | last4 = Kramer | first4 = ADI | last5 = Marlow | first5 = C | last6 = Settle | first6 = JE | last7 = Fowler | first7 = JH | year = 2012 | title = A 61-million-person experiment in social influence and political mobilization | url = | journal = Nature | volume = 489 | issue = | pages = 295–298 }}</ref> [[Propensity score matching|matched sample estimation]],<ref>{{cite journal|title=Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks |last1=Aral |first1=Sinan |last2=Muchnik |first2=Lev |last3=Sunararajan |first3=Arun |journal=Proceedings of the National Academy of Sciences |volume=106 |issue=51 |pages=21544–21549 |doi=10.1073/pnas.0908800106 |year=2009}}</ref> and [[Resampling (statistics)|reshuffling]] techniques.<ref>{{cite journal|title=Influence and Correlation in Social Networks |last1=Anagnostopoulos |first1=Aris |last2=Kumar |first2=Ravi |last3=Mahdian |first3=Mohammad |journal=Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |pages=7–15 |year=2008 |doi=10.1145/1401890.1401897}}</ref> A 2014 paper also confirmed the spread of emotions online, using a massive experiment.<ref>{{cite journal | last1 = Kramer | first1 = ADI | last2 = Guillory | first2 = JE | last3 = Hancock | first3 = JT | year = 2014 | title = Experimental evidence of massive-scale emotional contagion through social networks | url = http://m.pnas.org/content/early/2014/05/29/1320040111.full.pdf | format = PDF | journal = Proceedings of the National Academy of Sciences | volume = | issue = | page = }}</ref>
In addition, subsequent studies (by many research groups, including Christakis and Fowler) have found strong causal evidence of behavioral contagion processes (including those that spread beyond dyads, out to two, three, or four degrees) using [[Randomized controlled trial|randomized controlled experiments]],<ref>{{cite journal|last1=Centola |first1=Damon |title=The Spread of Behavior in an Online Social Network Experiment |journal=Science |volume=329 |issue=5995 |pages=1194–1197 |doi=10.1126/science.1185231 |year=2010}}</ref><ref>{{cite journal|title=An experimental study of homophily in the adoption of health behavior |journal=Science |last1=Centola |first1=Damon |year=2011 |volume=334 |issue=6060 |pages=1269–1272 |doi=10.1126/science.1207055}}</ref><ref>{{cite journal|last2=Christakis |first2=Nicholas A. |last1=Fowler |first1=James H. |title=Cooperative behavior cascades in human social networks |journal=Proceedings of the National Academy of Sciences |volume=107 |issue=12 |pages=5334–5338 |doi=10.1073/pnas.0913149107 |year=2010 |pmid=20212120 |pmc=2851803}}</ref><ref>{{cite journal |last1=Aral |first1=Sinan |last2=Walker |first2=Dylan |title=Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks |journal=Management Science |year=2011 |volume=57 |issue=9 |pages=1623–1639 |doi=10.1287/mnsc.1110.1421}}</ref><ref>Rand D, Arbesman S, and Christakis NA,"Dynamic Social Networks Promote Cooperation in Experiments with Humans," PNAS:Proceedings of the National Academy of Sciences 2011; 108: 19193-19198</ref> including one experiment involving 61,000,000 people that showed spread of voting behavior out to two degrees of separation,<ref>{{cite journal | last1 = Bond | first1 = RM | last2 = Fariss | first2 = CJ | last3 = Jones | first3 = JJ | last4 = Kramer | first4 = ADI | last5 = Marlow | first5 = C | last6 = Settle | first6 = JE | last7 = Fowler | first7 = JH | year = 2012 | title = A 61-million-person experiment in social influence and political mobilization | url = | journal = Nature | volume = 489 | issue = | pages = 295–298 | doi=10.1038/nature11421}}</ref> [[Propensity score matching|matched sample estimation]],<ref>{{cite journal|title=Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks |last1=Aral |first1=Sinan |last2=Muchnik |first2=Lev |last3=Sunararajan |first3=Arun |journal=Proceedings of the National Academy of Sciences |volume=106 |issue=51 |pages=21544–21549 |doi=10.1073/pnas.0908800106 |year=2009}}</ref> and [[Resampling (statistics)|reshuffling]] techniques.<ref>{{cite journal|title=Influence and Correlation in Social Networks |last1=Anagnostopoulos |first1=Aris |last2=Kumar |first2=Ravi |last3=Mahdian |first3=Mohammad |journal=Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining |pages=7–15 |year=2008 |doi=10.1145/1401890.1401897}}</ref> A 2014 paper also confirmed the spread of emotions online, using a massive experiment.<ref>{{cite journal | last1 = Kramer | first1 = ADI | last2 = Guillory | first2 = JE | last3 = Hancock | first3 = JT | year = 2014 | title = Experimental evidence of massive-scale emotional contagion through social networks | url = http://m.pnas.org/content/early/2014/05/29/1320040111.full.pdf | format = PDF | journal = Proceedings of the National Academy of Sciences | volume = | issue = | page = }}</ref>


==Moral implications==
==Moral implications==

Revision as of 10:31, 10 November 2014

Three Degrees of Influence is a theory in the realm of Social Networks,[1] proposed by Nicholas A. Christakis and James H. Fowler. Christakis and Fowler found that social networks have great influence on individuals' behavior. But social influence does not end with the people to whom a person is directly tied. We influence our friends who in their turn influence their friends, meaning that our actions can influence people we have never met. They posit that "everything we do or say tends to ripple through our network, having an impact on our friends (one degree), our friends’ friends (two degrees), and even our friends’ friends’ friends (three degrees). Our influence gradually dissipates and ceases to have a noticeable effect on people beyond the social frontier that lies at three degrees of separation". This argument is basically that peer effects need not stop at one degree, and that, if we can affect our friends, then we can (in many cases) affect our friends' friends, and so on. However, across a broad set of empirical settings, the effect seems to no longer be meaningful at a social horizon of three degrees. Christakis and Fowler have examined phenomena from various domains, such as gaining weight, happiness, and politics.

Mechanism

The influence of actions ripples through networks three degrees (to and from your friends’ friends’ friends). Influence dissipates after three degrees for three reasons, Christakis and Fowler propose:[2]

  1. Intrinsic decay - corruption of information (like the game telephone)
  2. Network instability - social ties become unstable (or are not constant across time) at more than three degrees of separation
  3. Evolutionary purpose - we evolved in small groups where everyone was connected by three degrees or fewer

Scientific literature

Studies by Christakis and Fowler suggested that a variety of attributes—like obesity,[3] smoking cessation,[4] and happiness[5]—rather than being individualistic, are casually correlated by contagion mechanisms that transmit these behaviors over long distances within social networks.[6] While certain subsequent analyses suggested limitations to these analyses (subject to different statistical assumptions);[7] or expressed concern that the Christakis-Fowler analyses did not fully control for other environmental factors;[8] or mis-interpreted statistical estimates;[9] or did not fully account for homophily processes in the creation and retention of relationships over time;[10][11] other scholarship using sensitivity analysis has found that the core findings regarding the transmissibility of obesity and smoking cessation are robust,[12][13] or has otherwise replicated or supported the findings.[14][15] Christakis and Fowler reviewed critical and supportive findings in 2013.[13] Moreover, a 2012 paper by physicists ver Steeg and Galstyan suggests it may be possible to bound estimates of peer effects [15] even if parametric assumptions are otherwise required to identify such effects using observational data (if indeed substantial unobserved homophily is thought to be present).[11] Additional support for the modeling approach used by Christakis and Fowler provided by other authors has continued to appear,[16] including of the three-degrees-of-influence property.[17]

In addition, subsequent studies (by many research groups, including Christakis and Fowler) have found strong causal evidence of behavioral contagion processes (including those that spread beyond dyads, out to two, three, or four degrees) using randomized controlled experiments,[18][19][20][21][22] including one experiment involving 61,000,000 people that showed spread of voting behavior out to two degrees of separation,[23] matched sample estimation,[24] and reshuffling techniques.[25] A 2014 paper also confirmed the spread of emotions online, using a massive experiment.[26]

Moral implications

The idea of network influence raises the question of free will, because it suggests that we are influenced by factors which we cannot control and which we are not aware of. Christakis and Fowler claim that society should use the knowledge about social networks in order to create a better society with a more efficient public policy. This applies to many aspects of life, from public health to economics. For instance, they note that it might be preferable to immunize individuals located in network's center more than peripheral individuals. Or, it might be much more effective to motivate clusters or people to avoid criminal behavior than to act upon individuals or than to punish each criminal separately.

If people are connected to everyone by six degrees of separation (according to the social psychologist Stanley Milgram) and influence those up to three degrees (Christakis and Fowler), then people can reach halfway to anyone in the world.[27]

See also

References

  1. ^ "The hidden influence of social networks Nicholas Christakis on TED.com".
  2. ^ Connected Preface+chapter1
  3. ^ Christakis, Nicholas A.; Fowler, James H. (2007). "The Spread of Obesity in a Large Social Network over 32 Years". The New England Journal of Medicine. 357 (4): 370–379. doi:10.1056/NEJMsa066082. PMID 17652652.
  4. ^ Christakis, Nicholas A.; Fowler, James H. (2008). "The Collective Dynamics of Smoking in a Large Social Network". The New England Journal of Medicine. 358. doi:10.1056/NEJMsa0706154. PMC 2822344. PMID 18499567.
  5. ^ Christakis, Nicholas A.; Fowler, James H. (2008). "Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study". British Medical Journal. 337 (337): a2338. doi:10.1136/bmj.a2338. PMC 2600606. PMID 19056788.
  6. ^ Christakis, Nicholas A.; Fowler, James H. (2009). Connected:The Surprising Power of Our Social Networks and How They Shape Our Lives. Little, Brown and Co. ISBN 978-0316036146.
  7. ^ Cohen-Cole, Ethan; Fletcher, Jason M. (2008). "Detecting implausible social network effects in acne, height, and headaches: longitudinal analysis". British Medical Journal. doi:10.1136/bmj.a2533.
  8. ^ Cohen-Cole, Ethan; Fletcher, Jason M. (2008). "Is obesity contagious? Social networks vs. environmental factors in the obesity epidemic". Journal of Health Economics. 27: 1382–1387. doi:10.1016/j.jhealeco.2008.04.005.
  9. ^ Lyons, Russell (2011). "The Spread of Evidence-Poor Medicine via Flawed Social Network Analysis". Statistics, Politics, and Policy. 2 (1). doi:10.2202/2151-7509.1024.
  10. ^ Noel, Hans; Nyhan, Brendan (2011). "The 'unfriending problem': The consequences of homophily in friendship retention for causal estimates of social influence". Social Networks. 33 (3): 211–218. doi:10.1016/j.socnet.2011.05.003.
  11. ^ a b Shalizi, Cosma R.; Thomas, Andrew C. (2011). "Homphily and Contagion Are Generically Confounded in Observational Social Network Studies". Sociological Methods & Research. 40 (2): 211–239. doi:10.1177/0049124111404820.
  12. ^ VanderWeele, Tyler J. "Sensitivity Analysis for Contagion Effects in Social Networks". Sociological Methods & Research. 40 (2): 240–255. doi:10.1177/0049124111404821.
  13. ^ a b Christakis NA and Fowler JH, "Social Contagion Theory: ExaminingDynamic Social Networks and Human Behavior," Statistics in Medicine 2013; 32: 556-577
  14. ^ MM Ali, A Amialchuk, S Gao, and F Heiland, "Adolescent Weight Gain and Social Networks: Is There a Contagion Effect?," Applied Economics 2012; 44: 2969-2983
  15. ^ a b G ver Steeg, A. Galstyan, "Statistical Tests for Contagion in Observational Social Network Studies," Journal of Machine Learning Research 2012; 563-571
  16. ^ A. Gonzalez-Pardo, R. Cajias, D. Camacho, "An Agent Based Simulation of Christakis-Fowler Social Model," Recent Developments in Computational Collective Intelligence, 2014; 513: 69-77
  17. ^ http://www.cbma.bio.uminho.pt/files/Pacheco-Manuscript.pdf
  18. ^ Centola, Damon (2010). "The Spread of Behavior in an Online Social Network Experiment". Science. 329 (5995): 1194–1197. doi:10.1126/science.1185231.
  19. ^ Centola, Damon (2011). "An experimental study of homophily in the adoption of health behavior". Science. 334 (6060): 1269–1272. doi:10.1126/science.1207055.
  20. ^ Fowler, James H.; Christakis, Nicholas A. (2010). "Cooperative behavior cascades in human social networks". Proceedings of the National Academy of Sciences. 107 (12): 5334–5338. doi:10.1073/pnas.0913149107. PMC 2851803. PMID 20212120.
  21. ^ Aral, Sinan; Walker, Dylan (2011). "Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks". Management Science. 57 (9): 1623–1639. doi:10.1287/mnsc.1110.1421.
  22. ^ Rand D, Arbesman S, and Christakis NA,"Dynamic Social Networks Promote Cooperation in Experiments with Humans," PNAS:Proceedings of the National Academy of Sciences 2011; 108: 19193-19198
  23. ^ Bond, RM; Fariss, CJ; Jones, JJ; Kramer, ADI; Marlow, C; Settle, JE; Fowler, JH (2012). "A 61-million-person experiment in social influence and political mobilization". Nature. 489: 295–298. doi:10.1038/nature11421.
  24. ^ Aral, Sinan; Muchnik, Lev; Sunararajan, Arun (2009). "Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks". Proceedings of the National Academy of Sciences. 106 (51): 21544–21549. doi:10.1073/pnas.0908800106.
  25. ^ Anagnostopoulos, Aris; Kumar, Ravi; Mahdian, Mohammad (2008). "Influence and Correlation in Social Networks". Proceedings of the 14th ACM SIGKDD Conference on Knowledge Discovery and Data Mining: 7–15. doi:10.1145/1401890.1401897.
  26. ^ Kramer, ADI; Guillory, JE; Hancock, JT (2014). "Experimental evidence of massive-scale emotional contagion through social networks" (PDF). Proceedings of the National Academy of Sciences.
  27. ^ connectedthebook.com - Download slides
  • Article concerning three degrees of influence [1]
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