The DOI matches its Crossref record; 294 of the 294 references it cites were found in a registry or an index.
Dynamics of information diffusion and its applications on complex networks
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Granovetter, M. S. (1973) · American Journal of Sociology · doi:10.1086/225469 · Cited by 40,491
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · An Integrated Approach to Communication Theory and Research · doi:10.4324/9780203710753-35 · Cited by 37,322
Donoho, D. L. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2006.871582 · Cited by 23,994
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
Donoho, D. L. (2004) · Cited by 16,703
Candes, E. J., Romberg, J., & Tao, T. (2006) · IEEE Transactions on Information Theory · doi:10.1109/tit.2005.862083 · Cited by 15,639
Valente, T. W. (2003) · Genetics in Medicine · doi:10.1097/01.gim.0000061743.67794.c4 · Cited by 13,723
A contribution to the mathematical theory of epidemics
Kermack, W. O., & McKendrick, A. G. (1927) · Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character · doi:10.1098/rspa.1927.0118 · Cited by 11,701
Complex networks: structure and dynamics
BOCCALETTI, S., LATORA, V, MORENO, Y., CHAVEZ, M., & HWANG, D. (2006) · Physics Reports · doi:10.1016/j.physrep.2005.10.009 · Cited by 11,010
An Introduction To Compressive Sampling
Candes, E. J., & Wakin, M. B. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2007.914731 · Cited by 10,017
Infectious Diseases of Humans: Dynamics and Control
(1992) · Annals of Internal Medicine · doi:10.7326/0003-4819-117-2-174_4 · Cited by 7,746
Error and attack tolerance of complex networks
Albert, R., Jeong, H., & Barabási, A.-L. (2000) · Nature · doi:10.1038/35019019 · Cited by 7,534
Maximizing the spread of influence through a social network
Kempe, D., Kleinberg, J., & Tardos, É. (2003) · Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/956750.956769 · Cited by 7,326
Stable signal recovery from incomplete and inaccurate measurements
Candès, E. J., Romberg, J. K., & Tao, T. (2006) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.20124 · Cited by 7,036
The Mathematics of Infectious Diseases
Hethcote, H. W. (2000) · SIAM Review · doi:10.1137/s0036144500371907 · Cited by 6,687
What is Twitter, a social network or a news media?
Kwak, H., Lee, C., Park, H., & Moon, S. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772751 · Cited by 6,652
Error and attack tolerance of complex networksPreprint
Albert, R., Jeong, H., & Barabási, A.-Ĺ. (2000) · Cited by 6,467
Threshold models of collective behavior
Granovetter, M. (1978) · American Journal of Sociology · doi:10.1086/226707 · Cited by 6,072
Epidemic Spreading in Scale-Free Networks
Pastor-Satorras, R., & Vespignani, A. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.3200 · Cited by 5,731
Giving content to investor sentiment: The role of media in the stock market
TETLOCK, P. C. (2007) · The Journal of Finance · doi:10.1111/j.1540-6261.2007.01232.x · Cited by 4,827
Catastrophic cascade of failures in interdependent networks
Buldyrev, S. V., Parshani, R., Paul, G., Stanley, H. E., & Havlin, S. (2010) · Nature · doi:10.1038/nature08932 · Cited by 4,513
Epidemic processes in complex networks
Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015) · Reviews of Modern Physics · doi:10.1103/revmodphys.87.925 · Cited by 3,697
Experimental evidence of massive-scale emotional contagion through social networks
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320040111 · Cited by 3,687
Random graphs with arbitrary degree distributions and their applications
Newman, M. E. J., Strogatz, S. H., & Watts, D. J. (2001) · Physical Review E · doi:10.1103/physreve.64.026118 · Cited by 3,648
(2016) · Foundations of Physically Based Modeling and Animation · doi:10.1201/9781315373140-16 · Cited by 3,267
Exposure to ideologically diverse news and opinion on Facebook
Bakshy, E., Messing, S., & Adamic, L. A. (2015) · Science · doi:10.1126/science.aaa1160 · Cited by 3,245
Spread of epidemic disease on networks
Newman, M. E. J. (2002) · Physical Review E · doi:10.1103/physreve.66.016128 · Cited by 3,218
Identification of influential spreaders in complex networks
Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010) · Nature Physics · doi:10.1038/nphys1746 · Cited by 3,212
Private traits and attributes are predictable from digital records of human behavior
Kosinski, M., Stillwell, D., & Graepel, T. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218772110 · Cited by 2,829
Broder, A., Kumar, R., Maghoul, F., Raghavan, P., Rajagopalan, S., Stata, R., Tomkins, A., & Wiener, J. (2000) · Computer Networks · doi:10.1016/s1389-1286(00)00083-9 · Cited by 2,810
Holme, P., & Saramäki, J. (2012) · Physics Reports · doi:10.1016/j.physrep.2012.03.001 · Cited by 2,736 · cited 1×
“Even more complicated, the edges are not continuously active in social system, which is also referred to as temporal network [117], and the temporal structure of edges can significantly affect the interaction dynamics through the network.”
BackgroundDynamical Processes on Complex Networks
Barrat, A., Barthélemy, M., & Vespignani, A. (2008) · doi:10.1017/cbo9780511791383 · Cited by 2,704 · cited 2×
“The threshold value of this approach is also different, which is R0 = ⟨k⟩β/γ (R0 is the basic reproductive number) [219].”
BackgroundThe spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692
Crowdfunding: Tapping the right crowd
Belleflamme, P., Lambert, T., & Schwienbacher, A. (2014) · Journal of Business Venturing · doi:10.1016/j.jbusvent.2013.07.003 · Cited by 2,690
A simple model of global cascades on random networks
Watts, D. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.082090499 · Cited by 2,673
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment
Tumasjan, A., Sprenger, T., Sandner, P., & Welpe, I. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14009 · Cited by 2,649
The spreading of misinformation online
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517441113 · Cited by 2,407
Social Contagion and Innovation: Cohesion versus Structural Equivalence
Burt, R. S. (1987) · American Journal of Sociology · doi:10.1086/228667 · Cited by 2,401
Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market
Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006) · Science · doi:10.1126/science.1121066 · Cited by 2,231
Critical phenomena in complex networks
Dorogovtsev, S. N., Goltsev, A. V., & Mendes, J. F. F. (2008) · Reviews of Modern Physics · doi:10.1103/revmodphys.80.1275 · Cited by 2,220
The origin of bursts and heavy tails in human dynamics
Barabási, A.-L. (2005) · Nature · doi:10.1038/nature03459 · Cited by 2,210
Hierarchical structure and the prediction of missing links in networks
Clauset, A., Moore, C., & Newman, M. E. J. (2008) · Nature · doi:10.1038/nature06830 · Cited by 2,171
The dynamics of viral marketing
Leskovec, J., Adamic, L. A., & Huberman, B. A. (2007) · ACM Transactions on the Web · doi:10.1145/1232722.1232727 · Cited by 2,032
Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth
Goldenberg, J., Libai, B., & Muller, E. (2001) · Marketing Letters · doi:10.1023/a:1011122126881 · Cited by 1,995
Structure and tie strengths in mobile communication networks
Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610245104 · Cited by 1,926
From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series
O'Connor, B., Balasubramanyan, R., Routledge, B., & Smith, N. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14031 · Cited by 1,923
Imagined Communities: Awareness, Information Sharing, and Privacy on the Facebook
Acquisti, A., & Gross, R. (2006) · Lecture Notes in Computer Science · doi:10.1007/11957454_3 · Cited by 1,895
Attack vulnerability of complex networks
Holme, P., Kim, B. J., Yoon, C. N., & Han, S. K. (2002) · Physical Review E · doi:10.1103/physreve.65.056109 · Cited by 1,891
Influentials, Networks, and Public Opinion Formation
Watts, D. J., & Dodds, P. S. (2007) · Journal of Consumer Research · doi:10.1086/518527 · Cited by 1,875
Twitter power: Tweets as electronic word of mouth
Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21149 · Cited by 1,642
Mining knowledge-sharing sites for viral marketing
Richardson, M., & Domingos, P. (2002) · Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/775047.775057 · Cited by 1,612
Meme-tracking and the dynamics of the news cycle
Leskovec, J., Backstrom, L., & Kleinberg, J. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557077 · Cited by 1,530
Breakdown of the Internet under Intentional Attack
Cohen, R., Erez, K., ben-Avraham, D., & Havlin, S. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.3682 · Cited by 1,455
Enterprise 2.0: the dawn of emergent collaboration
McAfee, A. P. (2006) · IEEE Engineering Management Review · doi:10.1109/emr.2006.261380 · Cited by 1,407
Modelling the influence of human behaviour on the spread of infectious diseases: a review
Funk, S., Salathé, M., & Jansen, V. A. A. (2010) · Journal of The Royal Society Interface · doi:10.1098/rsif.2010.0142 · Cited by 1,314
Influence maximization in complex networks through optimal percolation
Morone, F., & Makse, H. A. (2015) · Nature · doi:10.1038/nature14604 · Cited by 1,230
Perspectives on the basic reproductive ratio
Heffernan, J. M., Smith, R. J., & Wahl, L. M. (2005) · Journal of The Royal Society Interface · doi:10.1098/rsif.2005.0042 · Cited by 1,228
Identifying influential nodes in complex networks
Chen, D., Lü, L., Shang, M.-S., Zhang, Y.-C., & Zhou, T. (2012) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2011.09.017 · Cited by 1,210
Self-citationDiurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures
Golder, S. A., & Macy, M. W. (2011) · Science · doi:10.1126/science.1202775 · Cited by 1,155
The spread of awareness and its impact on epidemic outbreaks
Funk, S., Gilad, E., Watkins, C., & Jansen, V. A. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810762106 · Cited by 1,128
Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling
Gardner, T. S., di Bernardo, D., Lorenz, D., & Collins, J. J. (2003) · Science · doi:10.1126/science.1081900 · Cited by 1,117
Romero, D. M., Meeder, B., & Kleinberg, J. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963503 · Cited by 1,082
Identifying influential and susceptible members of social networks
Aral, S., & Walker, D. (2012) · Science · doi:10.1126/science.1215842 · Cited by 1,047
Van Mieghem, P., Omic, J., & Kooij, R. (2009) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2008.925623 · Cited by 1,040
Perpetual Contact: Mobile Communication, Private Talk, Public Performance
Katz, J. E., & Aakhus, M. A. (2002) · Cited by 1,034
Lü, L., Medo, M., Yeung, C. H., Zhang, Y.-C., Zhang, Z.-K., & Zhou, T. (2012) · Physics Reports · doi:10.1016/j.physrep.2012.02.006 · Cited by 995
Self-citationPatterns of temporal variation in online media
Yang, J., & Leskovec, J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935863 · Cited by 986
Who says what to whom on twitter
Wu, S., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963504 · Cited by 957
Influential Nodes in a Diffusion Model for Social Networks
Kempe, D., Kleinberg, J., & Tardos, É. (2005) · Lecture Notes in Computer Science · doi:10.1007/11523468_91 · Cited by 948
Dynamics of rumor spreading in complex networks
Moreno, Y., Nekovee, M., & Pacheco, A. F. (2004) · Physical Review E · doi:10.1103/physreve.69.066130 · Cited by 936
Social influence analysis in large-scale networks
Tang, J., Sun, J., Wang, C., & Yang, Z. (2009) · Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1557019.1557108 · Cited by 927
Information diffusion through blogspace
Gruhl, D., Liben-Nowell, D., Guha, R., & Tomkins, A. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1046456.1046462 · Cited by 927
DALEY, D. J., & KENDALL, D. G. (1964) · Nature · doi:10.1038/2041118a0 · Cited by 872
Leaders in Social Networks, the Delicious Case
Lü, L., Zhang, Y.-C., Yeung, C. H., & Zhou, T. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021202 · Cited by 871
Self-citationProminent Features of Rumor Propagation in Online Social Media
Kwon, S., Cha, M., Jung, K., Chen, W., & Wang, Y. (2013) · 2013 IEEE 13th International Conference on Data Mining · doi:10.1109/icdm.2013.61 · Cited by 859
Adaptive coevolutionary networks: a review
Gross, T., & Blasius, B. (2008) · Journal of The Royal Society Interface · doi:10.1098/rsif.2007.1229 · Cited by 849
Epidemic outbreaks in complex heterogeneous networksPreprint
Moreno, Y., Pastor‐Satorras, R., & Vespignani, A. (2002) · Cited by 823
Information contagion: an empirical study of the spread of news on digg and twitter social networks
Lerman, K., & Ghosh, R. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14021 · Cited by 813
Limiting the spread of misinformation in social networks
Budak, C., Agrawal, D., & El Abbadi, A. (2011) · Proceedings of the 20th international conference on World wide web · doi:10.1145/1963405.1963499 · Cited by 807
Location‐Based Services: Fundamentals and Operation
Küpper, A. (2005) · Wiley eBooks · doi:10.1002/0470092335 · Cited by 801
The effects of local spatial structure on epidemiological invasions
Keeling, M. J. (1999) · Proceedings of the Royal Society of London. Series B: Biological Sciences · doi:10.1098/rspb.1999.0716 · Cited by 798
Automated reverse engineering of nonlinear dynamical systems
Bongard, J., & Lipson, H. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0609476104 · Cited by 792
Epidemic thresholds in real networks
Chakrabarti, D., Wang, Y., Wang, C., Leskovec, J., & Faloutsos, C. (2008) · ACM Transactions on Information and System Security · doi:10.1145/1284680.1284681 · Cited by 787
Cheng, J., Adamic, L. A., Dow, P. A., Kleinberg, J. M., & Leskovec, J. (2014) · Cited by 787
Robust dynamic classes revealed by measuring the response function of a social system
Crane, R., & Sornette, D. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803685105 · Cited by 786
Proceedings 2001 IEEE International Conference on Data Mining
(2001) · Proceedings 2001 IEEE International Conference on Data Mining · doi:10.1109/icdm.2001.989492 · Cited by 768
Epidemic outbreaks in complex heterogeneous networks
Moreno, Y., Pastor-Satorras, R., & Vespignani, A. (2002) · The European Physical Journal B · doi:10.1140/epjb/e20020122 · Cited by 755
Motivations for social networking at work
DiMicco, J., Millen, D. R., Geyer, W., Dugan, C., Brownholtz, B., & Muller, M. (2008) · Proceedings of the 2008 ACM conference on Computer supported cooperative work · doi:10.1145/1460563.1460674 · Cited by 754
Accelerating the Diffusion of Innovations using Opinion Leaders
VALENTE, T. W., & DAVIS, R. L. (1999) · The ANNALS of the American Academy of Political and Social Science · doi:10.1177/0002716299566001005 · Cited by 705
Structural diversity in social contagion
Ugander, J., Backstrom, L., Marlow, C., & Kleinberg, J. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116502109 · Cited by 695
The H-index of a network node and its relation to degree and coreness
Lü, L., Zhou, T., Zhang, Q.-M., & Stanley, H. E. (2016) · Nature Communications · doi:10.1038/ncomms10168 · Cited by 690
Fournier, S., & Avery, J. (2011) · Business Horizons · doi:10.1016/j.bushor.2011.01.001 · Cited by 685
An Experimental Study of Homophily in the Adoption of Health Behavior
Centola, D. (2011) · Science · doi:10.1126/science.1207055 · Cited by 681
Inferring networks of diffusion and influence
Gomez Rodriguez, M., Leskovec, J., & Krause, A. (2010) · Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1835804.1835933 · Cited by 679
Small but slow world: How network topology and burstiness slow down spreading
Karsai, M., Kivelä, M., Pan, R. K., Kaski, K., Kertész, J., Barabási, A.-L., & Saramäki, J. (2011) · Physical Review E · doi:10.1103/physreve.83.025102 · Cited by 668
The Structural Virality of Online Diffusion
Goel, S., Anderson, A., Hofman, J., & Watts, D. J. (2016) · Management Science · doi:10.1287/mnsc.2015.2158 · Cited by 647
Detect Rumors Using Time Series of Social Context Information on Microblogging Websites
Ma, J., Gao, W., Wei, Z., Lu, Y., & Wong, K.-F. (2015) · Proceedings of the 24th ACM International on Conference on Information and Knowledge Management · doi:10.1145/2806416.2806607 · Cited by 640
Morris, S. (2000) · Review of Economic Studies · doi:10.1111/1467-937x.00121 · Cited by 639
Competition among memes in a world with limited attention
Weng, L., Flammini, A., Vespignani, A., & Menczer, F. (2012) · Scientific Reports · doi:10.1038/srep00335 · Cited by 620
Hazard Warning Systems: Review of 20 Years of Progress
Sorensen, J. H. (2000) · Natural Hazards Review · doi:10.1061/(asce)1527-6988(2000)1:2(119 · Cited by 609
Medical Innovation: A Diffusion Study.
Sower, C., Coleman, J. S., Katz, E., & Menzel, H. (1967) · Administrative Science Quarterly · doi:10.2307/2391560 · Cited by 579
Dynamics of rumor propagation on small-world networks
Zanette, D. H. (2002) · Physical Review E · doi:10.1103/physreve.65.041908 · Cited by 576
Universal Behavior in a Generalized Model of Contagion
Dodds, P. S., & Watts, D. J. (2004) · Physical Review Letters · doi:10.1103/physrevlett.92.218701 · Cited by 571
DALEY, D. J., & KENDALL, D. G. (1965) · IMA Journal of Applied Mathematics · doi:10.1093/imamat/1.1.42 · Cited by 556
Suppressing cascades of load in interdependent networks
Brummitt, C. D., D’Souza, R. M., & Leicht, E. A. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1110586109 · Cited by 553
Modeling Information Diffusion in Implicit Networks
Yang, J., & Leskovec, J. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.22 · Cited by 550
Virality Prediction and Community Structure in Social Networks
Weng, L., Menczer, F., & Ahn, Y.-Y. (2013) · Scientific Reports · doi:10.1038/srep02522 · Cited by 512
Proceedings of the 18th international conference on World wide web
(2009) · doi:10.1145/1526709 · Cited by 498
Understanding the Spreading Patterns of Mobile Phone Viruses
Wang, P., González, M. C., Hidalgo, C. A., & Barabási, A.-L. (2009) · Science · doi:10.1126/science.1167053 · Cited by 489
Two hearts in three-quarter time: How to waltz the social media/viral marketing dance
Kaplan, A. M., & Haenlein, M. (2011) · Business Horizons · doi:10.1016/j.bushor.2011.01.006 · Cited by 484
Percolation transition in dynamical traffic network with evolving critical bottlenecks
Li, D., Fu, B., Wang, Y., Lu, G., Berezin, Y., Stanley, H. E., & Havlin, S. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1419185112 · Cited by 475
The Effectiveness of Branded Mobile Phone Apps
Bellman, S., Potter, R. F., Treleaven-Hassard, S., Robinson, J. A., & Varan, D. (2011) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2011.06.001 · Cited by 465
A generalized model of social and biological contagion
Dodds, P. S., & Watts, D. J. (2005) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2004.09.006 · Cited by 448
Prediction of Information Diffusion Probabilities for Independent Cascade Model
Saito, K., Nakano, R., & Kimura, M. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-85567-5_9 · Cited by 436
Proceedings of the 2012 SIAM International Conference on Data Mining
(2012) · doi:10.1137/1.9781611972825 · Cited by 417
Impact of Human Activity Patterns on the Dynamics of Information Diffusion
Iribarren, J. L., & Moro, E. (2009) · Physical Review Letters · doi:10.1103/physrevlett.103.038702 · Cited by 413
Locating the Source of Diffusion in Large-Scale Networks
Pinto, P. C., Thiran, P., & Vetterli, M. (2012) · Physical Review Letters · doi:10.1103/physrevlett.109.068702 · Cited by 409
Using Hashtags to Capture Fine Emotion Categories from Tweets
Mohammad, S. M., & Kiritchenko, S. (2015) · Computational Intelligence · doi:10.1111/coin.12024 · Cited by 408
Predicting the Speed, Scale, and Range of Information Diffusion in Twitter
Yang, J., & Counts, S. (2010) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v4i1.14039 · Cited by 400
Warf, B. (2010) · Encyclopedia of Geography · doi:10.4135/9781412939591.n722 · Cited by 388
The structure of online diffusion networks
Goel, S., Watts, D. J., & Goldstein, D. G. (2012) · Proceedings of the 13th ACM Conference on Electronic Commerce · doi:10.1145/2229012.2229058 · Cited by 385
Measurements and mitigation of peer-to-peer-based botnets: a case study on storm worm
Holz, T., Steiner, M., Dahl, F., Biersack, E. W., & Freiling, F. (2008) · Cited by 383
Location-Based Recommendation System Using Bayesian User’s Preference Model in Mobile Devices
Park, M.-H., Hong, J.-H., & Cho, S.-B. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-73549-6_110 · Cited by 381
Dynamical classes of collective attention in twitter
Lehmann, J., Gonçalves, B., Ramasco, J. J., & Cattuto, C. (2012) · Proceedings of the 21st international conference on World Wide Web · doi:10.1145/2187836.2187871 · Cited by 380
Critical behavior of propagation on small-world networks
Zanette, D. H. (2001) · Physical Review E · doi:10.1103/physreve.64.050901 · Cited by 379
Graham, T., Broersma, M., Hazelhoff, K., & van 't Haar, G. (2013) · Information, Communication & Society · doi:10.1080/1369118x.2013.785581 · Cited by 363
Innovation and robustness in complex regulatory gene networks
Ciliberti, S., Martin, O. C., & Wagner, A. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0705396104 · Cited by 362
Searching for superspreaders of information in real-world social media
Pei, S., Muchnik, L., Andrade, Zheng, Z., & Makse, H. A. (2014) · Scientific Reports · doi:10.1038/srep05547 · Cited by 362
Why rumors spread so quickly in social networks
Doerr, B., Fouz, M., & Friedrich, T. (2012) · Communications of the ACM · doi:10.1145/2184319.2184338 · Cited by 357
Cascade dynamics of complex propagation
Centola, D., Eguíluz, V. M., & Macy, M. W. (2007) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2006.06.018 · Cited by 357
Predicting Catastrophes in Nonlinear Dynamical Systems by Compressive Sensing
Wang, W.-X., Yang, R., Lai, Y.-C., Kovanis, V., & Grebogi, C. (2011) · Physical Review Letters · doi:10.1103/physrevlett.106.154101 · Cited by 352
Libai, B., Muller, E., Recanati, L., Givon, M. M., & Hogan, J. E. (2001) · Cited by 351
Impact of Non-Poissonian Activity Patterns on Spreading Processes
Vazquez, A., Rácz, B., Lukács, A., & Barabási, A.-L. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.158702 · Cited by 338
Revealing Network Connectivity from Response Dynamics
Timme, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.224101 · Cited by 333
Challenges and business models for mobile location-based services and advertising
Dhar, S., & Varshney, U. (2011) · Communications of the ACM · doi:10.1145/1941487.1941515 · Cited by 331
Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts
Rocha, L. E. C., Liljeros, F., & Holme, P. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001109 · Cited by 322
Inferring the origin of an epidemic with a dynamic message-passing algorithm
Lokhov, A. Y., Mézard, M., Ohta, H., & Zdeborová, L. (2014) · Physical Review E · doi:10.1103/physreve.90.012801 · Cited by 322
Rise and fall patterns of information diffusion: model and implications
Matsubara, Y., Sakurai, Y., Prakash, B. A., Li, L., & Faloutsos, C. (2012) · Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2339530.2339537 · Cited by 306
Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks
Scholtes, I., Wider, N., Pfitzner, R., Garas, A., Tessone, C. J., & Schweitzer, F. (2014) · Nature Communications · doi:10.1038/ncomms6024 · Cited by 304
Critical effect of dependency groups on the function of networks
Parshani, R., Buldyrev, S. V., & Havlin, S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1008404108 · Cited by 303
Percolation on Sparse Networks
Karrer, B., Newman, M. E. J., & Zdeborová, L. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.208702 · Cited by 303
Optimal Network Modularity for Information Diffusion
Nematzadeh, A., Ferrara, E., Flammini, A., & Ahn, Y.-Y. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.088701 · Cited by 300
Collaborative personalized tweet recommendation
Chen, K., Chen, T., Zheng, G., Jin, O., Yao, E., & Yu, Y. (2012) · Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/2348283.2348372 · Cited by 296
Seed size strongly affects cascades on random networks
Gleeson, J. P., & Cahalane, D. J. (2007) · Physical Review E · doi:10.1103/physreve.75.056103 · Cited by 273
Structure and dynamics of information pathways in online media
Gomez Rodriguez, M., Leskovec, J., & Schölkopf, B. (2013) · Proceedings of the sixth ACM international conference on Web search and data mining · doi:10.1145/2433396.2433402 · Cited by 271
Evidence for a bimodal distribution in human communication
Wu, Y., Zhou, C., Xiao, J., Kurths, J., & Schellnhuber, H. J. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1013140107 · Cited by 269
Searching for superspreaders of information in real-world social media
Pei, S., Muchnik, L., Andrade, Jr., Zheng, Z. M., & Makse, H. A. (2014) · CUNY Academic Works (City University of New York) · Cited by 265
Epidemic Spreading in Complex Networks with Degree Correlations
Boguá, M., Pastor-Satorras, R., & Vespignani, A. (2003) · Lecture Notes in Physics · doi:10.1007/978-3-540-44943-0_8 · Cited by 263
Mathematical models and applications, with emphasis on the social, life, and management sciences
Mäki, D. P., & Thompson, M. (1973) · Prentice Hall eBooks · Cited by 259
Contributions to the mathematical theory of epidemics—II. The problem of endemicity
Kermack, W. O., & McKendrick, A. G. (1991) · Bulletin of Mathematical Biology · doi:10.1007/bf02464424 · Cited by 254
Endemic disease, awareness, and local behavioural response
Funk, S., Gilad, E., & Jansen, V. A. A. (2010) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.02.032 · Cited by 250
Characterizing and Modeling the Dynamics of Online Popularity
Ratkiewicz, J., Fortunato, S., Flammini, A., Menczer, F., & Vespignani, A. (2010) · Physical Review Letters · doi:10.1103/physrevlett.105.158701 · Cited by 249
Absence of influential spreaders in rumor dynamics
Borge-Holthoefer, J., & Moreno, Y. (2012) · Physical Review E · doi:10.1103/physreve.85.026116 · Cited by 248
The Simple Rules of Social Contagion
Hodas, N. O., & Lerman, K. (2014) · Scientific Reports · doi:10.1038/srep04343 · Cited by 246
Social influence locality for modeling retweeting behaviors
Zhang, J., Liu, B., Tang, J., Chen, T., & Li, J. (2013) · Cited by 246
Topic-aware social influence propagation models
Barbieri, N., Bonchi, F., & Manco, G. (2013) · Knowledge and Information Systems · doi:10.1007/s10115-013-0646-6 · Cited by 232
small world yields the most effective information spreading
Lü, L., Chen, D., & Zhou, T. (2012) · reroDoc Digital Library · Cited by 226
Gelling, and melting, large graphs by edge manipulation
Tong, H., Prakash, B. A., Eliassi-Rad, T., Faloutsos, M., & Faloutsos, C. (2012) · Proceedings of the 21st ACM international conference on Information and knowledge management · doi:10.1145/2396761.2396795 · Cited by 225
Perpetual Contact: Mobile Communication, Private Talk, Public Performance
Phillips, D. J. (2003) · Sociological Research Online · doi:10.1177/136078040300800107 · Cited by 223
Epidemic spreading on heterogeneous networks with identical infectivity
Yang, R., Wang, B.-H., Ren, J., Bai, W.-J., Shi, Z.-W., Wang, W.-X., & Zhou, T. (2007) · Physics Letters A · doi:10.1016/j.physleta.2006.12.021 · Cited by 221
Multiplexity-facilitated cascades in networks
Brummitt, C. D., Lee, K.-M., & Goh, K.-I. (2012) · Physical Review E · doi:10.1103/physreve.85.045102 · Cited by 217
Reconstructing propagation networks with natural diversity and identifying hidden sources
Shen, Z., Wang, W.-X., Fan, Y., Di, Z., & Lai, Y.-C. (2014) · Nature Communications · doi:10.1038/ncomms5323 · Cited by 216
Model for rumor spreading over networks
Trpevski, D., Tang, W. K. S., & Kocarev, L. (2010) · Physical Review E · doi:10.1103/physreve.81.056102 · Cited by 215
The small world yields the most effective information spreading
Lü, L., Chen, D.-B., & Zhou, T. (2011) · New Journal of Physics · doi:10.1088/1367-2630/13/12/123005 · Cited by 215
Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations
Cui, L., Li, G., Lin, Q., Chen, J., & Lu, N. (2016) · Computers & Operations Research · doi:10.1016/j.cor.2015.09.006 · Cited by 213
Ranking stability and super-stable nodes in complex networks
Ghoshal, G., & Barabási, A.-L. (2011) · Nature Communications · doi:10.1038/ncomms1396 · Cited by 205
Oh, O., Kwon, K. H., & Rao, H. R. (2010) · International Conference on Information Systems · Cited by 203
Proceedings of the 2015 SIAM International Conference on Data Mining
(2015) · doi:10.1137/1.9781611974010 · Cited by 201
Who Influenced You? Predicting Retweet via Social Influence Locality
Zhang, J., Tang, J., Li, J., Liu, Y., & Xing, C. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2700398 · Cited by 199
Proceedings of the 7th ACM conference on Recommender systems
(2013) · doi:10.1145/2507157 · Cited by 198
Insights from unifying modern approximations to infections on networks
House, T., & Keeling, M. J. (2011) · Journal of The Royal Society Interface · doi:10.1098/rsif.2010.0179 · Cited by 194
A predictive model for the temporal dynamics of information diffusion in online social networks
Guille, A., & Hacid, H. (2012) · Proceedings of the 21st International Conference on World Wide Web · doi:10.1145/2187980.2188254 · Cited by 192
Proceedings of the 2008 ACM conference on Computer supported cooperative work
(2008) · doi:10.1145/1460563 · Cited by 191
On Universality in Human Correspondence Activity
Malmgren, R. D., Stouffer, D. B., Campanharo, A. S. L. O., & Amaral, L. A. N. (2009) · Science · doi:10.1126/science.1174562 · Cited by 189
On the spread of viruses on the internet
Berger, N., Borgs, C., Chayes, J., & Saberi, A. (2005) · doi:10.5555/1070432.1070475 · Cited by 183
Temporal Networks: Slowing Down Diffusion by Long Lasting Interactions
Masuda, N., Klemm, K., & Eguíluz, V. M. (2013) · Physical Review Letters · doi:10.1103/physrevlett.111.188701 · Cited by 180
Who’s Talking First? Consensus or Lack Thereof in Coevolving Opinion Formation Models
Nardini, C., Kozma, B., & Barrat, A. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.158701 · Cited by 179
Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia
Gilbert, M., Golding, N., Zhou, H., Wint, G. R. W., Robinson, T. P., Tatem, A. J., Lai, S., Zhou, S., Jiang, H., Guo, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5116 · Cited by 176
Proceedings of the 20th international conference on World wide web
(2011) · doi:10.1145/1963405 · Cited by 175
Analysis of complex contagions in random multiplex networks
Yağan, O., & Gligor, V. (2012) · Physical Review E · doi:10.1103/physreve.86.036103 · Cited by 174
Efficiency and reliability of epidemic data dissemination in complex networks
Moreno, Y., Nekovee, M., & Vespignani, A. (2004) · Physical Review E · doi:10.1103/physreve.69.055101 · Cited by 172
Calling patterns in human communication dynamics
Jiang, Z.-Q., Xie, W.-J., Li, M.-X., Podobnik, B., Zhou, W.-X., & Stanley, H. E. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1220433110 · Cited by 172
The Role of Social Networks in Information DiffusionPreprint
Bakshy, E., Rosenn, I., Marlow, C. A., & Adamic, L. A. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1201.4145 · Cited by 171
Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences
Kovanen, L., Kaski, K., Kertész, J., & Saramäki, J. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1307941110 · Cited by 169
Bleeding Frame and Region Detection in the Wireless Capsule Endoscopy Video
Yuan, Y., Li, B., & Meng, M. Q.-H. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2399502 · Cited by 167
Mining structural hole spanners through information diffusion in social networks
Lou, T., & Tang, J. (2013) · Proceedings of the 22nd international conference on World Wide Web · doi:10.1145/2488388.2488461 · Cited by 164
Viral marketing for the real world
Watts, D. J., Peretti, J., & Watts, D. J. (2007) · Cited by 164
Approximate Orthogonal Sparse Embedding for Dimensionality Reduction
Lai, Z., Wong, W. K., Xu, Y., Yang, J., & Zhang, D. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2422994 · Cited by 163
Circadian pattern and burstiness in mobile phone communication
Jo, H.-H., Karsai, M., Kertész, J., & Kaski, K. (2012) · New Journal of Physics · doi:10.1088/1367-2630/14/1/013055 · Cited by 160
Cited by
Coevolution spreading in complex networks
Wang, W., Liu, Q.-H., Liang, J., Hu, Y., & Zhou, T. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.07.001 · Cited by 286
A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
Zhou, F., Xu, X., Trajcevski, G., & Zhang, K. (2022) · ACM Computing Surveys · doi:10.1145/3433000 · Cited by 259
Information cascades in complex networks
Jalili, M., & Perc, M. (2017) · Journal of Complex Networks · doi:10.1093/comnet/cnx019 · Cited by 248
Kindness as a public health action
Nguyen, T.-L., & Lee, J.-Y. (2025) · Communications Medicine · doi:10.1038/s43856-025-00861-3 · Cited by 3
“In analogy to epidemics, any so-called “ individual ” behavioural action (e.g., thought, speech, or physical action) could in fact have a collective impact, through the emergence of cascading phenomena 17 – 20 .”
Metadata
- Authors
- Zhang, Z.-K., Liu, C., Zhan, X.-X., Lu, X., Zhang, C.-X., & Zhang, Y.-C.
- Venue
- Physics Reports
- Publisher
- Elsevier BV
- Published
- 2016-09
- Volume
- 651
- Pages
- 1-34
- ISSN
- 0370-1573 (ISSN-L)
- OpenAlex
- W2493103841
- Cited by
- 484
- References
- 294
- FWCI
- 29.01
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
