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Dynamics of information diffusion and its applications on complex networks
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Items 201–294 of 294
Network Reconstruction Based on Evolutionary-Game Data via Compressive Sensing
Wang, W.-X., Lai, Y.-C., Grebogi, C., & Ye, J. (2011) · Physical Review X · doi:10.1103/physrevx.1.021021 · Cited by 159
An individual-based approach to SIR epidemics in contact networks
Youssef, M., & Scoglio, C. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2011.05.029 · Cited by 156
Non-Mean-Field Behavior of the Contact Process on Scale-Free Networks
Castellano, C., & Pastor‐Satorras, R. (2006) · Physical Review Letters · doi:10.1103/physrevlett.96.038701 · Cited by 156
A study of rumor control strategies on social networks
Tripathy, R. M., Bagchi, A., & Mehta, S. (2010) · International Conference on Information and Knowledge Management · doi:10.1145/1871437.1871737 · Cited by 155
Bursts of Vertex Activation and Epidemics in Evolving Networks
Rocha, L. E. C., & Blondel, V. D. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002974 · Cited by 155
Modeling and Predicting Retweeting Dynamics on Microblogging Platforms
Gao, S., Ma, J., & Chen, Z. (2015) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2684822.2685303 · Cited by 154
Diffusive Logistic Model Towards Predicting Information Diffusion in Online Social Networks
Wang, F., Wang, H., & Xu, K. (2012) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw.2012.16 · Cited by 152
Automata, Languages and Programming
Bugliesi, M., Wegener, I., Sassone, V., & Preneel, B. (2006) · Lecture notes in computer science · doi:10.1007/11787006 · Cited by 152
The production of information in the attention economy
Ciampaglia, G. L., Flammini, A., & Menczer, F. (2015) · Scientific Reports · doi:10.1038/srep09452 · Cited by 150
Competition-Induced Criticality in a Model of Meme Popularity
Gleeson, J. P., Ward, J. A., O’Sullivan, K. P., & Lee, W. T. (2014) · Physical Review Letters · doi:10.1103/physrevlett.112.048701 · Cited by 150
Learning Continuous-Time Information Diffusion Model for Social Behavioral Data Analysis
Saito, K., Kimura, M., Ohara, K., & Motoda, H. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-05224-8_25 · Cited by 148
Kleinberg, J. M. (2007) · Nature · doi:10.1038/449287a · Cited by 148
Dynamics of social contagions with memory of nonredundant information
Wang, W., Tang, M., Zhang, H.-F., & Lai, Y. (2015) · Physical Review E · doi:10.1103/physreve.92.012820 · Cited by 148
Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms
Prakash, B. A., Tong, H., Valler, N. C., Faloutsos, M., & Faloutsos, C. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15939-8_7 · Cited by 145
Uncovering the structure and temporal dynamics of information propagation
Gomez-Rodriguez, M., Leskovec, J., Balduzzi, D., & Schölkopf, B. (2014) · Network Science · doi:10.1017/nws.2014.3 · Cited by 145
Complex contagion process in spreading of online innovation
Karsai, M., Iñiguez, G., Kaski, K. K., & Kertész, J. (2014) · Journal of The Royal Society Interface · doi:10.1098/rsif.2014.0694 · Cited by 144
Meier, P. (2012) · Journal of Map & Geography Libraries · doi:10.1080/15420353.2012.663739 · Cited by 141
On the Vulnerability of Large Graphs
Tong, H., Prakash, B. A., Tsourakakis, C. E., Eliassi‐Rad, T., Faloutsos, C., & Chau, D. H. (2010) · Proceedings · doi:10.1109/icdm.2010.54 · Cited by 141
Cascading outbreak prediction in networks: a data-driven approach
Cui, P., Jin, S., Yu, L., Wang, F., Zhu, W., & Yang, S. (2013) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2487575.2487639 · Cited by 140
Inner Composition Alignment for Inferring Directed Networks from Short Time Series
Hempel, S., Koseska, A., Kurths, J., & Nikoloski, Z. (2011) · Physical Review Letters · doi:10.1103/physrevlett.107.054101 · Cited by 131
Mixture of Mutually Exciting Processes for Viral Diffusion
Yang, S.-H., & Zha, H. (2013) · Cited by 126
Reflecting on the DARPA Red Balloon Challenge
Tang, J. C., Cebrián, M., Giacobe, N. A., Kim, H. J., Kim, T. J., & Wickert, D. (2011) · Communications of the ACM · doi:10.1145/1924421.1924441 · Cited by 125
Threshold model of cascades in empirical temporal networks
Karimi, F., & Holme, P. (2013) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2013.03.050 · Cited by 119
Minimizing the spread of contamination by blocking links in a network
Kimura, M., Saito, K., & Motoda, H. (2008) · National Conference on Artificial Intelligence · Cited by 119
Information spreading in context
Wang, D., Wen, Z., Tong, H., Lin, C., Song, C., & Barabási, A.-Ĺ. (2011) · International World Wide Web Conference (WWW) · doi:10.1145/1963405.1963508 · Cited by 119
Threshold-limited spreading in social networks with multiple initiators
Singh, P., Sreenivasan, S., Szymanski, B. K., & Korniss, G. (2013) · Scientific Reports · doi:10.1038/srep02330 · Cited by 118
Inferring user interests in the Twitter social network
Bhattacharya, P., Zafar, M. B., Ganguly, N., Ghosh, S., & Gummadi, K. P. (2014) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2645710.2645765 · Cited by 118
Network Reconstruction from Random Phase Resetting
Levnajić, Z., & PIKOVSKY, A. S. (2011) · Physical Review Letters · doi:10.1103/physrevlett.107.034101 · Cited by 116
Branching dynamics of viral information spreading
Iribarren, J. L., & Moro, E. (2011) · Physical Review E · doi:10.1103/physreve.84.046116 · Cited by 116
Spreading dynamics following bursty human activity patterns
Min, B., Goh, K.-I., & Vázquez, A. (2011) · Physical Review E · doi:10.1103/physreve.83.036102 · Cited by 114
Bursty Communication Patterns Facilitate Spreading in a Threshold-Based Epidemic Dynamics
Takaguchi, T., Masuda, N., & Holme, P. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068629 · Cited by 113
Efficient algorithms for mining high-utility itemsets in uncertain databases
Lin, J. C., Gan, W., Fournier‐Viger, P., Hong, T., & Tseng, V. S. M. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.12.019 · Cited by 113
Time-series–based prediction of complex oscillator networks via compressive sensing
Wang, W.-X., Yang, R., Lai, Y.-C., Kovanis, V. I., & Harrison, M. A. F. (2011) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/94/48006 · Cited by 111
Locating privileged spreaders on an online social network
Borge‐Holthoefer, J., Rivero, A., & Moreno, Y. (2012) · Physical Review E · doi:10.1103/physreve.85.066123 · Cited by 110
Locating the source of diffusion in complex networks by time-reversal backward spreading
Shen, Z., Cao, S., Wang, W.-X., Di, Z., & Stanley, E. (2016) · Physical review. E · doi:10.1103/physreve.93.032301 · Cited by 107
Proceedings of the 2016 SIAM International Conference on Data Mining
(2016) · doi:10.1137/1.9781611974348 · Cited by 103
Rapid innovation diffusion in social networks
Kreindler, G. E., & Young, H. P. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400842111 · Cited by 102
Threshold Conditions for Arbitrary Cascade Models on Arbitrary Networks
Prakash, B. A., Chakrabarti, D., Faloutsos, M., Valler, N. C., & Faloutsos, C. (2011) · Proceedings · doi:10.1109/icdm.2011.145 · Cited by 101
Selecting Information Diffusion Models over Social Networks for Behavioral Analysis
Saito, K., Kimura, M., Ohara, K., & Motoda, H. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15939-8_12 · Cited by 99
Adaptive Steganalysis Based on Embedding Probabilities of Pixels
Tang, W., Li, H., Luo, W., & Huang, J. (2015) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2015.2507159 · Cited by 97
Wang, W., Tang, M., Shu, P., & Wang, Z. (2016) · New Journal of Physics · doi:10.1088/1367-2630/18/1/013029 · Cited by 95
Blocking Simple and Complex Contagion by Edge Removal
Kuhlman, C. J., Tuli, G., Swarup, S., MARATHE, M. V., & Ravi, S. S. (2013) · Proceedings · doi:10.1109/icdm.2013.47 · Cited by 94
Epidemic profiles and defense of scale-free networks
Briesemeister, L., Lincoln, P. D., & Porras, P. A. (2003) · Workshop on Rapid Malcode · doi:10.1145/948187.948200 · Cited by 88
Ego network structure in online social networks and its impact on information diffusion
Arnaboldi, V., Conti, M., Gala, M. L., Passarella, A., & Pezzoni, F. (2015) · Computer Communications · doi:10.1016/j.comcom.2015.09.028 · Cited by 88
Balcázar, J. L., Sebag, M., Gionis, A., & Bonchi, F. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15880-3 · Cited by 88
Phenomenological models of socioeconomic network dynamics
Ehrhardt, G. C. M. A., Matteo Marsili, A., & Vega‐Redondo, F. (2006) · Physical Review E · doi:10.1103/physreve.74.036106 · Cited by 84
Reconstructing a credit network
Caldarelli, G., Chessa, A., Fabio Pammolli, A., Gabrielli, A., & Puliga, M. (2013) · Nature Physics · doi:10.1038/nphys2580 · Cited by 80
Lin, Q., Liu, Z., Yan, Q., Du, Z., Coello, C. A. C., Liang, Z., Wang, W., & Chen, J. (2015) · Information Sciences · doi:10.1016/j.ins.2015.12.022 · Cited by 80
Twitter tsunami early warning network : a social network analysis of Twitter information flows
Chatfield, A. T., & Brajawidagda, U. (2012) · Research Online (University of Wollongong) · Cited by 76
Make It or Break It: Manipulating Robustness in Large Networks
Chan, H., Akoglu, L., & Tong, H. (2014) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611973440.37 · Cited by 76
Topicality and Impact in Social Media: Diverse Messages, Focused Messengers
Weng, L., & Menczer, F. (2015) · PLoS ONE · doi:10.1371/journal.pone.0118410 · Cited by 75
The marketing consequences of competitor lawsuits
Gelb, B. D., & Bush, D. (2006) · Dialnet (Universidad de la Rioja) · Cited by 73
Global Diffusion via Cascading Invitations: Structure, Growth, and Homophily
Anderson, A., Huttenlocher, D. P., Kleinberg, J. M., Leskovec, J., & Tiwari, M. (2015) · International World Wide Web Conference (WWW) · doi:10.1145/2736277.2741672 · Cited by 67
A novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm
Zhu, Q., Lin, Q., Du, Z., Liang, Z., Wang, W., Zhu, Z., Chen, J., Huang, P.-Z., & Ming, Z. (2016) · Information Sciences · doi:10.1016/j.ins.2016.01.046 · Cited by 67
Modeling the structure and evolution of discussion cascades
Gómez, V., Kappen, H. J., & Kaltenbrunner, A. (2011) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/1995966.1995992 · Cited by 64
Exploring the Complex Pattern of Information Spreading in Online Blog Communities
Pei, S., Muchnik, L., Tang, S., Zheng, Z., & Makse, H. A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126894 · Cited by 64
Cumulative Effect in Information Diffusion: Empirical Study on a Microblogging Network
Bao, P., Shen, H., Chen, W., & Cheng, X. (2013) · PLoS ONE · doi:10.1371/journal.pone.0076027 · Cited by 63
Effects of temporal correlations on cascades: Threshold models on temporal networks
Backlund, V.-P., Saramäki, J., & Pan, R. K. (2014) · Physical Review E · doi:10.1103/physreve.89.062815 · Cited by 63
Tight Lower Bound for Percolation Threshold on an Infinite Graph
Hamilton, K. E., & Pryadko, L. P. (2014) · Physical Review Letters · doi:10.1103/physrevlett.113.208701 · Cited by 63
Towards combating rumors in social networks: Models and metrics
Tripathy, R. M., Bagchi, A., & Mehta, S. (2013) · Intelligent Data Analysis · doi:10.3233/ida-120571 · Cited by 61
Spatio-temporal and events based analysis of topic popularity in twitter
Ardon, S., Bagchi, A., Mahanti, A., Ruhela, A., Seth, A., Tripathy, R. M., & Triukose, S. (2013) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2505515.2505525 · Cited by 60
Social Computing and Behavioral Modeling
Young, M. J., Salerno, J., & Liu, H. (2009) · Springer eBooks · doi:10.1007/978-1-4419-0056-2 · Cited by 56
Burst Time Prediction in Cascades
Wang, S., Zhao, Y., Hu, X., Yu, P. S., & Li, Z. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9157 · Cited by 56
Reinforcement-driven spread of innovations and fads
Krapivsky, P., Redner, S., & Volovik, D. (2011) · arXiv.org · doi:10.1088/1742-5468/2011/12/p12003 · Cited by 54
Empirical analysis of collective human behavior for extraordinary events in the blogosphere
Sano, Y., Yamada, K., Watanabe, H., Hideki Takayasu, C., & Takayasu, M. (2013) · Physical Review E · doi:10.1103/physreve.87.012805 · Cited by 54
Online Social Network Analysis: A Survey of Research Applications in Computer SciencePreprint
Kurka, D. B., Godoy, A., & Zuben, F. J. V. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.05655 · Cited by 51
Spreading dynamics on networks: the role of burstiness, topology and non-stationarity
Horváth, D. X., & Kertész, J. (2014) · New Journal of Physics · doi:10.1088/1367-2630/16/7/073037 · Cited by 50
Traffic anomaly detection based on image descriptor in videos
Li, Y., Liu, W., & Huang, Q. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-015-2637-y · Cited by 44
Representing spatial interactions in simple ecological models
Morris, A. J. (2026) · Warwick Research Archive Portal (University of Warwick) · doi:10.82444/warw.33481111 · Cited by 41
Link operations for slowing the spread of disease in complex networks
Bishop, A. N., & Shames, I. (2011) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/95/18005 · Cited by 41
A Survey of Twitter Rumor Spreading Simulations
Serrano, E., Iglesias, C. Á., & Garijo, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24069-5_11 · Cited by 40
The Bursty Dynamics of the Twitter Information NetworkPreprint
Myers, S. A., & Leskovec, J. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.2732 · Cited by 38
Pivot selection for metric-space indexing
Mao, R., Zhang, P., Li, X., Liu, X., & Lu, M. (2016) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-016-0504-4 · Cited by 37
Lack of consensus in social systems
Benczik, I. J., Benczik, S., Schmittmann, B., & Zia, R. K. P. (2008) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/82/48006 · Cited by 36
Average is Boring: How Similarity Kills a Meme's Success
Coscia, M. (2014) · Scientific Reports · doi:10.1038/srep06477 · Cited by 36
Evolution of the Digital Society Reveals Balance between Viral and Mass Media Influence
Kleineberg, K.-K., & Boguñá, M. (2014) · Physical Review X · doi:10.1103/physrevx.4.031046 · Cited by 35
Epidemic dynamics on information-driven adaptive networks
Zhan, X., Liu, C., Sun, G., & Zhang, Z. (2018) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2018.02.010 · Cited by 35
Self-citationDynamic theory of cascades on finite clustered random networks with a threshold rule
Whitney, D. E. (2010) · Physical Review E · doi:10.1103/physreve.82.066110 · Cited by 34
Predictability of Extreme Events in Social Media
Miotto, J. M., & Altmann, E. G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0111506 · Cited by 33
Information Diffusion and External Influence in NetworksPreprint
Myers, S. A., Zhu, C., & Leskovec, J. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.1331 · Cited by 32
Transmission of Rumor and Criticism in Twitter after the Great Japan EarthquakePreprint
Tanaka, Y., Sakamoto, Y., & Matsuka, T. (2012) · SSRN Electronic Journal · Cited by 27
Reducing Influenza Spreading Over the Airline Network
Marcelino, J., & Kaiser, M. (2009) · PLoS Currents · doi:10.1371/currents.rrn1005 · Cited by 26
Predicting the evolution of spreading on complex networks
Chen, D., Xiao, R., & Zeng, A. (2014) · Scientific Reports · doi:10.1038/srep06108 · Cited by 26
Modeling Bursty Temporal Pattern of Rumors
Kwon, S., & Cha, M. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14494 · Cited by 26
Information content of contact-pattern representations and predictability of epidemic outbreaks
Holme, P. (2015) · Scientific Reports · doi:10.1038/srep14462 · Cited by 25
Segmenting time series with connected lines under maximum error bound
Zhao, H., Dong, Z., Li, T., Wang, X., & Pang, C. (2015) · Information Sciences · doi:10.1016/j.ins.2015.09.017 · Cited by 25
Ice bucket challenge cash may help derisk ALS drug research
Vaidya, M. (2014) · Nature Medicine · doi:10.1038/nm1014-1080 · Cited by 25
Finding influential neighbors to maximize information diffusion in twitter
Kim, H., Beznosov, K., & Yoneki, E. (2014) · International World Wide Web Conference (WWW) · doi:10.1145/2567948.2579358 · Cited by 19
Proceedings of the 4th international conference on Ubiquitous Intelligence and Computing
Indulska, J., Ma, J., Yang, L. T., et al. (2007) · Ubiquitous Intelligence and Computing · Cited by 16
Time evolution of predictability of epidemics on networks
Holme, P., & Takaguchi, T. (2015) · Physical Review E · doi:10.1103/physreve.91.042811 · Cited by 14
A Novel Approach for Generating Personalized Mention List on Micro-Blogging System
Zhou, G., Yu, L., Zhang, C., Liu, C., Zhang, Z., & Zhang, J. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.51 · Cited by 13
Self-citationQuantifying Regional Differences in the Length of Twitter Messages
Alis, C., Lim, M. T., Moat, H. S., Barchiesi, D., Preis, T., & Bishop, S. R. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122278 · Cited by 12
Circadian pattern and burstiness in human communication activityPreprint
Jo, H.-H., Karsai, M., Kertész, J., & Kaski, K. K. (2011) · arXiv (Cornell University) · Cited by 11
Mutual Feedback Between Epidemic Spreading and Information DiffusionPreprint
Zhan, X., Liu, C., Zhou, G., Zhang, Z., Sun, G., & Zhu, J. J. H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03932 · Cited by 4
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Zhou, F., Xu, X., Trajcevski, G., & Zhang, K. (2022) · ACM Computing Surveys · doi:10.1145/3433000 · Cited by 259
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“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
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