2012 RiseandFallPatternsofInformatio: Difference between revisions

Latest revision as of 06:36, 8 March 2024

(Matsubara et al., 2012) ⇒ Yasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li, and Christos Faloutsos. (2012). “Rise and Fall Patterns of Information Diffusion: Model and Implications.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339537

Subject Headings:

Notes

Cited By

Quotes

Author Keywords

Data mining; information diffusion; social networks

Abstract

The recent explosion in the adoption of search engines and new media such as blogs and Twitter have facilitated faster propagation of news and rumors. How quickly does a piece of news spread over these media? How does its popularity diminish over time? Does the rising and falling pattern follow a simple universal law?

In this paper, we propose SpikeM, a concise yet flexible analytical model for the rise and fall patterns of influence propagation. Our model has the following advantages: (a) unification power: it generalizes and explains earlier theoretical models and empirical observations; (b) practicality: it matches the observed behavior of diverse sets of real data; (c) parsimony: it requires only a handful of parameters; and (d) usefulness: it enables further analytics tasks such as forecasting, spotting anomalies, and interpretation by reverse-engineering the system parameters of interest (e.g. quality of news, count of interested bloggers, etc.).

Using SpikeM, we analyzed 7.2GB of real data, most of which were collected from the public domain. We have shown that our SpikeM model accurately and succinctly describes all the patterns of the rise-and-fall spikes in these real datasets.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 RiseandFallPatternsofInformatio	B. Aditya Prakash Christos Faloutsos Lei Li Yasushi Sakurai Yasuko Matsubara			Rise and Fall Patterns of Information Diffusion: Model and Implications				10.1145/2339530.2339537		2012

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-* ([[2012_RiseandFallPatternsofInformatio|Matsubara & al, 2012]]) &rArr; [[author::Yasuko Matsubara]], [[author::Yasushi Sakurai]], [[author::B. Aditya Prakash]], [[author::Lei Li]], and [[author::Christos Faloutsos]]. ([[year::2012]]). "Rise and Fall Patterns of Information Diffusion: Model and Implications." In: [[proceedings::Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[conference::KDD-2012]]). ISBN:978-1-4503-1462-6 [http://dx.doi.org/10.1145/2339530.2339537 doi:10.1145/2339530.2339537]
+* ([[2012_RiseandFallPatternsofInformatio|Matsubara et al., 2012]]) ⇒ [[author::Yasuko Matsubara]], [[author::Yasushi Sakurai]], [[author::B. Aditya Prakash]], [[author::Lei Li]], and [[author::Christos Faloutsos]]. ([[year::2012]]). “Rise and Fall Patterns of Information Diffusion: Model and Implications.” In: [[proceedings::Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining]] ([[conference::KDD-2012]]). ISBN:978-1-4503-1462-6 [http://dx.doi.org/10.1145/2339530.2339537 doi:10.1145/2339530.2339537]
 <B>Subject Headings:</B>
-==Notes==
+== Notes ==
-==Cited By==
+== Cited By ==
 * http://scholar.google.com/scholar?q=%222012%22+Rise+and+Fall+Patterns+of+Information+Diffusion%3A+Model+and+Implications
 * http://dl.acm.org/citation.cfm?id=2339530.2339537&preflayout=flat#citedby
-==Quotes==
+== Quotes ==
-===Author Keywords===
+=== Author Keywords ===
 * [[Data mining]]; [[information diffusion]]; [[social network]]s
-===Abstract===
+=== Abstract ===
-The recent explosion in the adoption of [[search engine]]s and new media such as [[blog]]s and [[Twitter]] have facilitated faster [[propagation]] of [[new]]s and [[rumor]]s. </s>
+The recent explosion in the adoption of [[search engine]]s and [[new media]] such as [[blog]]s and [[Twitter]] have facilitated faster [[propagation]] of [[news]] and [[rumor]]s. </s>
 How quickly does a piece of [[news spread]] over these [[media]]? </s>
-How does its popularity diminish over [[time]]? </s>
+How does its [[popularity]] diminish over [[time]]? </s>
-Does the [[rising]] and [[falling pattern]] follow a simple universal [[law]]? </s>
+Does the [[rising and falling pattern]] follow a simple [[universal law]]? </s>
-In [[this paper]], we propose [[SpikeM]], a concise yet flexible [[analytical model]] for the rise and [[fall pattern]]s of [[influence propagation]]. </s>
+[[In this paper, we]] propose [[SpikeM]], a concise yet flexible [[analytical model]] for the [[rise and fall pattern]]s of [[influence propagation]]. </s>
-[[Our model]] has the following advantages: (a) [[unification power]]: it generalizes and explains earlier [[theoretical model]]s and [[empirical observation]]s; (b) practicality: it matches the [[observed behavior]] of diverse [[sets of real data; (c) parsimony]]: it requires only a [[handful of parameter]]s; and (d) usefulness: it enables further analytics tasks such as fore - [[casting]], [[spotting anomali]]es, and interpretation by reverse - [[engineering the system parameter]]s of interest (e.g. </s>
+[[Our model]] has the following advantages: (a) [[unification power]]: it generalizes and explains earlier [[theoretical model]]s and [[empirical observation]]s; (b) [[practicality]]: it matches the [[observed behavior]] of diverse [[sets of real data]]; (c) [[parsimony]]: it requires only a handful of [[parameter]]s; and (d) [[usefulness]]: it enables further [[analytics task]]s such as [[forecasting]], [[spotting anomali]]es, and interpretation by [[reverse-engineering]] the [[system parameter]]s of interest (e.g.
-[[quality of new]]s, [[count]] of interested bloggers, etc.). </s>
+[[quality of news]], [[count]] of interested [[blogger]]s, etc.). </s>
-Using [[SpikeM]], we analyzed [[7.2GB]] of [[real data]], most of which were collected from the [[public domain]]. </s>
+Using [[SpikeM]], we analyzed 7.2[[GB]] of [[real data]], most of which were collected from the [[public domain]]. </s>
-[[We]] have shown that our [[SpikeM model accurately]] and [[succinctly]] describes all the [[pattern]]s of the [[rise-and-fall spike]]s in these [[real dataset]]s. </s>
+[[We]] have shown that our [[SpikeM]] model [[accurately]] and [[succinctly]] describes all the [[pattern]]s of the [[rise-and-fall spike]]s in these [[real dataset]]s. </s>
-==References==
+== References ==
 {{#ifanon:|
 * 1. R. M. Anderson and R. M. May. Infectious Diseases of Humans. Oxford University Press, 1991.
@@ Line 43: / Line 44: @@
 * 13. Ankur Jain, Edward Y. Chang, Yuan-Fang Wang, Adaptive Stream Resource Management Using Kalman Filters, Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, June 13-18, 2004, Paris, France [http://doi.acm.org/10.1145/1007568.1007573 doi:10.1145/1007568.1007573]
 * 14. Tamer Kahveci, Ambuj K. Singh, Efficient Index Structures for String Databases, Proceedings of the 27th International Conference on Very Large Data Bases, p.351-360, September 11-14, 2001
-* 15. David Kempe, Jon Kleinberg, Éva Tardos, Maximizing the Spread of Influence through a Social Network, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, D.C. [http://doi.acm.org/10.1145/956750.956769 doi:10.1145/956750.956769]
+* 15. [[David Kempe]], [[Jon Kleinberg]], Éva Tardos, Maximizing the Spread of Influence through a Social Network, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, D.C. [http://doi.acm.org/10.1145/956750.956769 doi:10.1145/956750.956769]
 * 16. Eamonn Keogh, Themistoklis Palpanas, Victor B. Zordan, Dimitrios Gunopulos, Marc Cardle, Indexing Large Human-motion Databases, Proceedings of the Thirtieth International Conference on Very Large Data Bases, p.780-791, August 31-September 03, 2004, Toronto, Canada
-* 17. Jon Kleinberg, Bursty and Hierarchical Structure in Streams, Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton, Alberta, Canada [http://doi.acm.org/10.1145/775047.775061 doi:10.1145/775047.775061]
+* 17. [[Jon Kleinberg]], Bursty and Hierarchical Structure in Streams, Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton, Alberta, Canada [http://doi.acm.org/10.1145/775047.775061 doi:10.1145/775047.775061]
 * 18. Ravi Kumar, Mohammad Mahdian, Mary McGlohon, Dynamics of Conversations, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 25-28, 2010, Washington, DC, USA [http://doi.acm.org/10.1145/1835804.1835875 doi:10.1145/1835804.1835875]
-* 19. Theodoros Lappas, Evimaria Terzi, Dimitrios Gunopulos, Heikki Mannila, Finding Effectors in Social Networks, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 25-28, 2010, Washington, DC, USA [http://doi.acm.org/10.1145/1835804.1835937 doi:10.1145/1835804.1835937]
+* 19. Theodoros Lappas, Evimaria Terzi, Dimitrios Gunopulos, [[Heikki Mannila]], Finding Effectors in Social Networks, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 25-28, 2010, Washington, DC, USA [http://doi.acm.org/10.1145/1835804.1835937 doi:10.1145/1835804.1835937]
-* 20. Jure Leskovec, Lada A. Adamic, Bernardo A. Huberman, The Dynamics of Viral Marketing, ACM Transactions on the Web (TWEB), v.1 n.1, p.5-es, May 2007 [http://doi.acm.org/10.1145/1232722.1232727 doi:10.1145/1232722.1232727]
+* 20. [[Jure Leskovec]], Lada A. Adamic, Bernardo A. Huberman, The Dynamics of Viral Marketing, ACM Transactions on the Web (TWEB), v.1 n.1, p.5-es, May 2007 [http://doi.acm.org/10.1145/1232722.1232727 doi:10.1145/1232722.1232727]
-* 21. Jure Leskovec, Lars Backstrom, Jon Kleinberg, Meme-tracking and the Dynamics of the News Cycle, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28-July 01, 2009, Paris, France [http://doi.acm.org/10.1145/1557019.1557077 doi:10.1145/1557019.1557077]
+* 21. [[Jure Leskovec]], [[Lars Backstrom]], [[Jon Kleinberg]], Meme-tracking and the Dynamics of the News Cycle, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28-July 01, 2009, Paris, France [http://doi.acm.org/10.1145/1557019.1557077 doi:10.1145/1557019.1557077]
 * 22. J. Leskovec, M. McGlohon, C. Faloutsos, N. S. Glance, and M. Hurst. Patterns of Cascading Behavior in Large Blog Graphs. In SDM, 2007.
 * 23. K. Levenberg. A Method for the Solution of Certain Non-linear Problems in Least Squares. Quarterly Journal of Applied Mathmatics, II(2):164--168, 1944.
-* 24. Lei Li, Chieh-Jan Mike Liang, Jie Liu, Suman Nath, Andreas Terzis, Christos Faloutsos, ThermoCast: A Cyber-physical Forecasting Model for Datacenters, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 21-24, 2011, San Diego, California, USA [http://doi.acm.org/10.1145/2020408.2020611 doi:10.1145/2020408.2020611]
+* 24. [[Lei Li]], Chieh-Jan Mike Liang, Jie Liu, Suman Nath, Andreas Terzis, Christos Faloutsos, ThermoCast: A Cyber-physical Forecasting Model for Datacenters, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 21-24, 2011, San Diego, California, USA [http://doi.acm.org/10.1145/2020408.2020611 doi:10.1145/2020408.2020611]
-* 25. Lei Li, James McCann, Nancy S. Pollard, Christos Faloutsos, DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28-July 01, 2009, Paris, France [http://doi.acm.org/10.1145/1557019.1557078 doi:10.1145/1557019.1557078]
+* 25. [[Lei Li]], James McCann, Nancy S. Pollard, Christos Faloutsos, DynaMMo: Mining and Summarization of Coevolving Sequences with Missing Values, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28-July 01, 2009, Paris, France [http://doi.acm.org/10.1145/1557019.1557078 doi:10.1145/1557019.1557078]
 * 26. L. Li and B. A. Prakash. Time Series Clustering: Complex is Simpler! In ICML, 2011.
 * 27. Jessica Lin, Eamonn Keogh, Stefano Lonardi, Jeffrey P. Lankford, Donna M. Nystrom, Visually Mining and Monitoring Massive Time Series, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 22-25, 2004, Seattle, WA, USA [http://doi.acm.org/10.1145/1014052.1014104 doi:10.1145/1014052.1014104]
 * 28. Yasuko Matsubara, Yasushi Sakurai, Masatoshi Yoshikawa, Scalable Algorithms for Distribution Search, Proceedings of the 2009 Ninth IEEE International Conference on Data Mining, p.347-356, December 06-09, 2009 [http://dx.doi.org/10.1109/ICDM.2009.51 doi:10.1109/ICDM.2009.51]
-* 29. M. McGlohon, J. Leskovec, C. Faloutsos, M. Hurst, and N. Glance. Finding Patterns in Blog Shapes and Blog Evolution. In International Conference on Weblogs and Social Media, Boulder, Colo., March 2007.
+* 29. M. McGlohon, J. Leskovec, C. Faloutsos, M. Hurst, and N. Glance. Finding Patterns in Blog Shapes and Blog Evolution. In: Proceedings of The International Conference on Weblogs and Social Media, Boulder, Colo., March 2007.
 * 30. Panagiotis Papapetrou, Vassilis Athitsos, Michalis Potamias, George Kollios, Dimitrios Gunopulos, Embedding-based Subsequence Matching in Time-series Databases, ACM Transactions on Database Systems (TODS), v.36 n.3, p.1-39, August 2011 [http://doi.acm.org/10.1145/2000824.2000827 doi:10.1145/2000824.2000827]
 * 31. Nish Parikh, Neel Sundaresan, Scalable and Near Real-time Burst Detection from ECommerce Queries, Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2008, Las Vegas, Nevada, USA [http://doi.acm.org/10.1145/1401890.1402006 doi:10.1145/1401890.1402006]
 * 32. B. Aditya Prakash, Alex Beutel, Roni Rosenfeld, Christos Faloutsos, Winner Takes all: Competing Viruses Or Ideas on Fair-play Networks, Proceedings of the 21st International Conference on World Wide Web, April 16-20, 2012, Lyon, France [http://doi.acm.org/10.1145/2187836.2187975 doi:10.1145/2187836.2187975]
 * 33. B. Aditya Prakash, Deepayan Chakrabarti, Michalis Faloutsos, Nicholas Valler, Christos Faloutsos, Threshold Conditions for Arbitrary Cascade Models on Arbitrary Networks, Proceedings of the 2011 IEEE 11th International Conference on Data Mining, p.537-546, December 11-14, 2011 [http://dx.doi.org/10.1109/ICDM.2011.145 doi:10.1109/ICDM.2011.145]
-* 34. Y. Sakurai, C. Faloutsos, and M. Yamamuro. Stream Monitoring under the Time Warping Distance. In Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, April 15-20, 2007, The Marmara Hotel, Istanbul, Turkey, Pages 1046--1055, 2007.
+* 34. Y. Sakurai, C. Faloutsos, and M. Yamamuro. Stream Monitoring under the Time Warping Distance. In: Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, April 15-20, 2007, The Marmara Hotel, Istanbul, Turkey, Pages 1046--1055, 2007.
 * 35. Yasushi Sakurai, Spiros Papadimitriou, Christos Faloutsos, BRAID: Stream Mining through Group Lag Correlations, Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, June 14-16, 2005, Baltimore, Maryland [http://doi.acm.org/10.1145/1066157.1066226 doi:10.1145/1066157.1066226]
 * 36. D. Shah, T. Zaman, Rumors in a Network: Who's the Culprit?, IEEE Transactions on Information Theory, v.57 n.8, p.5163-5181, August 2011 [http://dx.doi.org/10.1109/TIT.2011.2158885 doi:10.1109/TIT.2011.2158885]
@@ Line 68: / Line 69: @@
 * 38. M. Vlachos, S. S. Kozat, and P. S. Yu. Optimal Distance Bounds on Time-series Data. In SDM, Pages 109--120, 2009.
 * 39. A. S. Weigend and N. A. Gerschenfeld. Time Series Prediction: Forecasting the Future and Understanding the Past. Addison Wesley, 1994.
-* 40. Jaewon Yang, Jure Leskovec, Modeling Information Diffusion in Implicit Networks, Proceedings of the 2010 IEEE International Conference on Data Mining, p.599-608, December 13-17, 2010 [http://dx.doi.org/10.1109/ICDM.2010.22 doi:10.1109/ICDM.2010.22]
+* 40. Jaewon Yang, [[Jure Leskovec]], Modeling Information Diffusion in Implicit Networks, Proceedings of the 2010 IEEE International Conference on Data Mining, p.599-608, December 13-17, 2010 [http://dx.doi.org/10.1109/ICDM.2010.22 doi:10.1109/ICDM.2010.22]
-* 41. Jaewon Yang, Jure Leskovec, Patterns of Temporal Variation in Online Media, Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, February 09-12, 2011, Hong Kong, China [http://doi.acm.org/10.1145/1935826.1935863 doi:10.1145/1935826.1935863]
+* 41. Jaewon Yang, [[Jure Leskovec]], Patterns of Temporal Variation in Online Media, Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, February 09-12, 2011, Hong Kong, China [http://doi.acm.org/10.1145/1935826.1935863 doi:10.1145/1935826.1935863]
 * 42. Yunyue Zhu, Dennis Shasha, Efficient Elastic Burst Detection in Data Streams, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, D.C. [http://doi.acm.org/10.1145/956750.956789 doi:10.1145/956750.956789]