|
|
|
| Review of Information Diffusion in Online Social Networks |
| HU Changjun XU Wenwen HU Ying FANG Mingzhe LIU Feng |
| (School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China) |
|
|
|
|
Abstract Online social networks are now recognized as an important platform for the spread of information. A lot of effort is made to understand this phenomenon, including popularity analysis, diffusion modeling, and information source locating. This paper presents a survey of representative methods dealing with these issues and summarizes the state of the art. To facilitate future work, analytical discussion regarding their shortcomings and related open problems are provided.
|
|
Received: 25 October 2016
Published: 07 March 2017
|
|
|
| Fund: The National Key Basic Research and Department Program of China (2013CB329605) |
|
Corresponding Authors:
XU Wenwen
E-mail: xuwenwenustb@163.com
|
|
|
|
| [1] |
PHAN T Q and AIROLDI E M. A natural experiment of social network formation and dynamics[J]. Proceedings of the National Academy of Sciences, 2015, 112(21): 6595-6600. doi: 10.1073/pnas.1404770112.
|
| [2] |
ZHANG Y, TANG J, YANG Z, et al. Cosnet: Connecting heterogeneous social networks with local and global consistency[C]. Proceedings of the 21th International Conference on Knowledge Discovery and Data Mining, Sydney, 2015: 1485-1494.
|
| [3] |
SAITO K, KIMURA M, OHARA K, et al. Super mediator A new centrality measure of node importance for information diffusion over social network[J]. Information Sciences, 2016, 329: 985-1000. doi: 10.1016/j.ins.2015.03.034.
|
| [4] |
ANDERSON A, HUTTENLOCHER D, KLEINBERG J, et al. Global diffusion via cascading invitations: Structure, growth, and homophily[C]. Proceedings of the 24th International Conference on World Wide Web, Florence, 2015: 66-76.
|
| [5] |
BARBIERI N, BONCHI F, and MANCO G. Who to follow and why: link prediction with explanations[C]. Proceedings of the 20th International Conference on Knowledge Discovery and Data Mining, New York, 2014: 1266-1275.
|
| [6] |
CHANEY A J B, BLEI D M, and ELIASSI-RAD T. A probabilistic model for using social networks in personalized item recommendation[C]. Proceedings of the 9th Conference on Recommender Systems, Vienna, 2015: 43-50.
|
| [7] |
MYERS S A and LESKOVEC J. The bursty dynamics of the twitter information network[C]. Proceedings of the 23rd International Conference on World Wide Web, Seoul, 2014: 913-924.
|
| [8] |
FANG B, JIA Y, HAN Y, et al. A survey of social network and information dissemination analysis[J]. Chinese Science Bulletin, 2014, 59(32): 4163-4172. doi: 10.1007/s11434-014- 0368-5.
|
| [9] |
GUILLE A, HACID H, FAVRE C, et al. Information diffusion in online social networks: A survey[J]. ACM SIGMOD Record, 2013, 42(2): 17-28. doi: 10.1145/2503792. 2503797.
|
| [10] |
WU B and SHEN H. Analyzing and predicting news popularity on Twitter[J]. International Journal of Information Management, 2015, 35(6): 702-711. doi: 10.1016 /j.ijinfomgt.2015.07.003.
|
| [11] |
SZABO G and HUBERMAN B A. Predicting the popularity of online content[J]. Communications of the ACM, 2010, 53(8): 80-88. doi: 10.1145/1787234.1787254.
|
| [12] |
MAITY S K, GUPTA A, GOYAL P, et al. A stratified learning approach for predicting the popularity of Twitter idioms[C]. Proceedings of the 9th International AAAI Conference on Web and Social Media, Oxford, 2015: 642-645.
|
| [13] |
CHOOBDAR S, RIBEIRO P, PARTHASARATHY S, et al. Dynamic inference of social roles in information cascades[J]. Data Mining and Knowledge Discovery, 2015, 29(5): 1152-1177. doi: 10.1007/s10618-015-0402-5.
|
| [14] |
YANG F, ZHANG R, YAO Y, et al. Locating the propagation source on complex networks with Propagation Centrality algorithm[J]. Knowledge-Based Systems, 2016, 100(C): 112-123. doi: 10.1016/j.knosys.2016.02.013.
|
| [15] |
ZHU K, CHEN Z, and YING L. Locating the contagion source in networks with partial timestamps[J]. Data Mining and Knowledge Discovery, 2014, 30(5): 1217-1248. doi: 10. 1007/s10618-015-0435-9.
|
| [16] |
PRAKASH B A, VREEKEN J, and FALOUTSOS C. Spotting culprits in epidemics: how many and which ones?[C]. International Conference on Data Mining, Las Vegas, 2012: 11-20.
|
| [17] |
AGGARWAL C C. An Introduction to Social Network Data Analytics[M]. New York: Springer US, 2011: 1-15.
|
| [18] |
KWAK H, LEE C, PARK H, et al. What is Twitter, a social network or a news media?[C]. Proceedings of the 19th International Conference on World Wide Web, North Carolina, 2010: 591-600.
|
| [19] |
ZINOVIEV D. Information Diffusion in Social Networks[M]. The United States of America: Social Networking and Community Behavior Modeling: Qualitative and Quantitative Measures: Qualitative and Quantitative Measures, 2011: 146-163.
|
| [20] |
PINTO H, ALMEIDA J M, and GONÇALVES M A. Using early view patterns to predict the popularity of youtube videos[C]. Proceedings of the 6th ACM International Conference on Web Search and Data Mining, Rome, 2013: 365-374.
|
| [21] |
BAO P, SHEN H W, HUANG J, et al. Popularity prediction in microblogging network: a case study on sina weibo[C]. Proceedings of the 22nd International Conference on World Wide Web, Rio de Janeiro, 2013: 177-178.
|
| [22] |
LERMAN K and HOGG T. Using a model of social dynamics to predict popularity of news[C]. Proceedings of the 19th International Conference on World Wide Web, North Carolina, 2010: 621-630.
|
| [23] |
PALUCK E L, SHEPHERD H, and ARONOW P M. Changing climates of conflict: A social network experiment in 56 schools[J]. Proceedings of the National Academy of Sciences, 2016, 113(3): 566-571. doi: 10.1073/pnas. 1514483113.
|
| [24] |
HE X, GAO M, KAN M Y, et al. Predicting the popularity of web 2.0 items based on user comments[C]. Proceedings of the 37th International Conference on Research & Development in Information Retrieval, Gold Coast, 2014: 233-242.
|
| [25] |
ZHAO Q, ERDOGDU M A, HE H Y, et al. SEISMIC: A self-exciting point process model for predicting tweet popularity[C]. Proceedings of the 21th International Conference on Knowledge Discovery and Data Mining, Sydney, 2015: 1513-1522.
|
| [26] |
SANLI C and LAMBIOTTE R. Local variation of hashtag spike trains and popularity in Twitter[J]. PloS One, 2015, 10(7): e0131704. doi: 10.1371/journal.pone.0131704.
|
| [27] |
PERVIN N, PHAN T Q, DATTA A, et al. Hashtag popularity on twitter: Analyzing co-occurrence of multiple hashtags[C]. International Conference on Social Computing and Social Media, Los Angeles, 2015: 169-182.
|
| [28] |
HU C, HU Y, XU W, et al. Understanding popularity evolution patterns of hot topics based on time series features[C]. Asia-Pacific Web Conference, Changsha, 2014: 58-68.
|
| [29] |
GOLDER S A, WILKINSON D M, and HUBERMAN B A. Rhythms of Social Interaction: Messaging Within a Massive Online Network[M]. London: Springer, 2007: 41-66.
|
| [30] |
RODRIGUEZ M G, BALDUZZI D, SCHOLKOPF B, et al. Uncovering the temporal dynamics of diffusion networks[C]. International Conference on Machine Learning, Washington, 2011: 561-568.
|
| [31] |
YANG J and LESKOVEC J. Patterns of temporal variation in online media[C]. Proceedings of the 4th International Conference on Web Search and Data Mining, Hong Kong, 2011: 177-186.
|
| [32] |
MATSUBARA Y, SAKURAI Y, PRAKASH B A, et al. Rise and fall patterns of information diffusion: model and implications[C]. Proceedings of the 18th International Conference on Knowledge Discovery and Data Mining, Beijing, 2012: 6-14.
|
| [33] |
CRANE R and SORNETTE D. Robust dynamic classes revealed by measuring the response function of a social system[J]. Proceedings of the National Academy of Sciences, 2008, 105(41): 15649-15653. doi: 10.1073/pnas.0803685105.
|
| [34] |
CRANE R and SORNETTE D. Viral, quality, and junk videos on youtube: Separating content from noise in an information-rich environment[C]. AAAI Spring Symposium: Social Information Processing, California, 2008: 18-20.
|
| [35] |
FIGUEIREDO F. On the prediction of popularity of trends and hits for user generated videos[C]. Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, Rome, 2013: 741-746.
|
| [36] |
FIGUEIREDO F, BENEVENUTO F, and ALMEIDA J M. The tube over time: Characterizing popularity growth of youtube videos[C]. Proceedings of the Fourth International Conference on Web Search and Data Mining, Hong Kong, 2011: 745-754.
|
| [37] |
ROMERO D M, MEEDER B, and KLEINBERG J. Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter[C]. Proceedings of the 20th International Conference on World Wide Web, Hyderabad, 2011: 695-704.
|
| [38] |
ASUR S, HUBERMAN B A, SZABO G, et al. Trends in social media: Persistence and decay[C]. Proceedings of the 5th International Conference on Weblogs and Social Media, Barcelona, 2011: 434-437.
|
| [39] |
ARDON S, BAGCHI A, MAHANTI A, et al. Spatio- temporal and events based analysis of topic popularity in twitter[C]. Proceedings of the 22nd International Conference on Information & Knowledge Management, Burlingame, 2013: 219-228.
|
| [40] |
LEHMANN J, GONÇALVES B, RAMASCO J J, et al. Dynamical classes of collective attention in twitter[C]. Proceedings of the 21st International Conference on World Wide Web, Lyon, 2012: 251-260.
|
| [41] |
SAITO K, KIMURA M, OHARA K, et al. Behavioral analyses of information diffusion models by observed data of social network[C]. International Conference on Social Computing, Behavioral Modeling, and Prediction, Bethesda, 2010: 149-158.
|
| [42] |
SAITO K, KIMURA M, OHARA K, et al. Selecting information diffusion models over social networks for behavioral analysis[C]. European Conference on Machine Learning and Knowledge Discovery in Databases, Barcelona, 2010: 180-195.
|
| [43] |
GUILLE A and HACID H. A predictive model for the temporal dynamics of information diffusion in online social networks[C]. Proceedings of the 21st International Conference Companion on World Wide Web, Lyon, 2012: 1145-1152.
|
| [44] |
ZHOU F, JIAO J R, and LEI B. A linear threshold-hurdle model for product adoption prediction incorporating social network effects[J]. Information Sciences, 2015, 307: 95-109. doi: 10.1016/j.ins.2015.02.027.
|
| [45] |
BOURIGAULT S, LAMPRIER S, and GALLINARI P. Representation learning for information diffusion through social networks: An embedded cascade model[C]. Proceedings of the Ninth International Conference on Web Search and Data Mining, California, 2016: 573-582.
|
| [46] |
ABDULLAH S and WU X. An epidemic model for news spreading on twitter[C]. Proceedings of the 23rd International Conference on Tools with Artificial Intelligence, Florida, 2011: 163-169.
|
| [47] |
XIONG F, LIU Y, ZHANG Z, et al. An information diffusion model based on retweeting mechanism for online social media[J]. Physics Letters A, 2012, 376(30): 2103-2108. doi: 10.1016/j.physleta.2012.05.021.
|
| [48] |
LIU D and CHEN X. Rumor propagation in online social networks like TwitterA simulation study[C]. Proceedings of the Third International Conference on Multimedia Information Networking and Security, Shanghai, 2011: 278-282.
|
| [49] |
YANG J and LESKOVEC J. Modeling information diffusion in implicit networks[C]. Proceedings of International Conference on Data Mining, Las Vegas, 2010: 599-608.
|
| [50] |
BARBIERI N, BONCHI F, and MANCO G. Topic-aware social influence propagation models[J]. Knowledge and Information Systems, 2013, 37(3): 555-584. doi: 10.1007/ s10115-013-0646-6.
|
| [51] |
BAKSHY E, KARRER B, and ADAMIC L A. Social influence and the diffusion of user-created content[C]. Proceedings of the 10th Conference on Electronic Commerce, Linz, 2009: 325-334.
|
| [52] |
BAKSHY E, HOFMAN J M, MASON W A, et al. Everyone's an influencer: Quantifying influence on Twitter[C]. Proceedings of the Fourth International Conference on Web Search and Data Mining, Cambridge, 2011: 65-74.
|
| [53] |
HA J, KIM S W, FALOUTSOS C, et al. An analysis on information diffusion through BlogCast in a blogosphere[J]. Information Sciences, 2015, 290(C): 45-62. doi: 10.1016/ j.ins.2014.08.042.
|
| [54] |
LIM S, JUNG I, LEE S, et al. Analysis of information diffusion for threshold models on arbitrary networks[J]. European Physical Journal B, 2015, 88(8): 1-14. doi: 10.1140/ epjb/e2015-60263-6.
|
| [55] |
YANG Y, TANG J, LEUNG C W, et al. RAIN: Social role-aware information diffusion[C]. Proceedings of National Conference on Artificial Intelligence, Austin Texas, 2015: 367-373.
|
| [56] |
DU N, LIANG Y, BALCAN M, et al. Influence function learning in information diffusion networks[C]. International Conference on Machine Learning, Beijing, 2014: 2016-2024.
|
| [57] |
DU N, SONG L, GOMEZRODRIGUEZ M, et al. Scalable influence estimation in continuous-time diffusion networks[C]. Neural Information Processing Systems, Nevada, 2013: 3147-3155.
|
| [58] |
HOREL T and SINGER Y. Scalable methods for adaptively seeding a social network[C]. Proceedings of the 24th International Conference on World Wide Web, Florence, 2015: 441-451.
|
| [59] |
RONG Y, CHENG H, and MO Z. Why it happened: identifying and modeling the reasons of the happening of social events[C]. Proceedings of the 21th International Conference on Knowledge Discovery and Data Mining, Sydney, 2015: 1015-1024.
|
| [60] |
MYERS S A and LESKOVEC J. Clash of the contagions: cooperation and competition in information diffusion[C]. International Conference on Data Mining, Las Vegas, 2012: 539-548.
|
| [61] |
GOMEZRODRIGUEZ M, LESKOVEC J, SCHOELKOPF B, et al. Modeling information propagation with survival theory[C]. International Conference on Machine Learning, Atlanta, 2013: 666-674.
|
| [62] |
WENG L, FLAMMINI A, VESPIGNANI A, et al. Competition among memes in a world with limited attention [J]. Scientific Reports, 2012, 2(7391): 335-342. doi: 10.1038/ srep00335.
|
| [63] |
SU Y, ZHANG X, LIU L, et al. Understanding information interactions in diffusion: An evolutionary game-theoretic perspective[J]. Frontiers of Computer Science, 2016, 10(3): 518-531. doi: 10.1007/s11704-015-5008-y.
|
| [64] |
BEUTEL A, PRAKASH B A, ROSENFELD R, et al. Interacting viruses in networks: can both survive?[C]. Proceedings of the 18th International Conference on Knowledge Discovery and Data Mining, Beijing, 2012: 426-434.
|
| [65] |
CASTELLANO C and PASTOR-SATORRAS R. Competing activation mechanisms in epidemics on networks[J]. Scientific Reports, 2012, 2(16): 371-376. doi: 10.1038/srep00371.
|
| [66] |
SAHNEH F D, SCOGLIO C, VAN Mieghem P, et al. Generalized epidemic mean-field model for spreading processes over multilayer complex networks[J]. ACM Transactions on Networking, 2013, 21(5): 1609-1620. doi: 10.1109/TNET.2013.2239658.
|
| [67] |
FIORITI V and CHINNICI M. Predicting the sources of an outbreak with a spectral technique[J]. Computer Science, 2012, 8(1): 6775-6782. doi: 10.12988/ams.2014.49693.
|
| [68] |
COMIN C H and DA FONTOURA C L. Identifying the starting point of a spreading process in complex networks[J]. Physical Review E, 2011, 84(5): 056105. doi: 10.1103/ PhysRevE.84.056105.
|
| [69] |
LOKHOV A Y, MÉZARD M, OHTA H, et al. Inferring the origin of an epidemic with a dynamic message-passing algorithm[J]. Physical Review E, 2014, 90(1): 012801. doi: 10.1103/PhysRevE.90.012801.
|
| [70] |
ANTULOV-FANTULIN N, LANCIC A, STEFANCIC H, et al. Statistical inference framework for source detection of contagion processes on arbitrary network structures[C]. Proceedings of Eighth International Conference on Self-Adaptive and Self-Organizing Systems Workshops, London, 2014: 78-83.
|
| [71] |
ZANG W, ZHANG P, ZHOU C, et al. Discovering multiple diffusion source nodes in social networks[J]. Procedia Computer Science, 2014, 29: 443-452. doi: 10.1016/j.procs. 2014.05.040.
|
| [72] |
KONG S, MEI Q, FENG L, et al. Predicting bursts and popularity of hashtags in real-time[C]. International Conference on Research and Development in Information Retrieval, Gold Coast, 2014: 927-930.
|
| [73] |
KONG S, FENG L, SUN G, et al. Predicting lifespans of popular tweets in microblog[C]. International Conference on Research and Development in Information Retrieval, Oregon, 2012: 1129-1130.
|
| [74] |
BAO P, SHEN H W, CHEN W, et al. Cumulative effect in information diffusion: Empirical study on a microblogging network[J]. PloS One, 2013, 8(10): e76027. doi: 10.1371/ journal.pone.0076027.
|
| [75] |
BERNABÉ-MORENO J, TEJEDA-LORENTE A, PORCEL C, et al. A new model to quantify the impact of a topic in a location over time with Social Media[J]. Expert Systems with Applications, 2015, 42(7): 3381-3395. doi: 10.1016/j.eswa. 2014.11.067.
|
| [76] |
WENG L and MENCZER F. Topicality and impact in social media: Eiverse messages, focused messengers[J]. PloS One, 2015, 10(2): e0118410. doi: 10.1371/journal.pone.0118410.
|
| [77] |
XU B, HUANG Y, KWAK H, et al. Structures of broken ties: Exploring unfollow behavior on twitter[C]. Computer Supported Cooperative Work, Texas, 2013: 871-876.
|
| [78] |
ZHANG J, TANG J, LI J, et al. Who influenced you? predicting retweet via social influence locality[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2015, 9(3): 25. doi: 10.1145/2700398.
|
| [79] |
FENG L, HU Y, LI B, et al. Competing for attention in social media under information overload conditions[J]. PloS One, 2015, 10(7): e0126090. doi: 10.1371/journal.pone.0126090.
|
| [80] |
PAPADOPOULOS F, KITSAK M, SERRANO M A, et al. Popularity versus similarity in growing networks[J]. Nature, 2011, 489(7417): 537-540. doi: 10.1038/nature11459.
|
| [81] |
GALLOS L K, RYBSKI D, LILJEROS F, et al. How people interact in evolving online affiliation networks[J]. Physical Review X, 2011, 2(3). doi: 10.1103/PhysRevX.2.031014.
|
| [82] |
GONCALVES B, PERRA N, VESPIGNANI A, et al. Modeling users' activity on twitter networks: Validation of Dunbar's number[J]. PloS One, 2011, 6(8). doi: 10.1371/ journal.pone.0022656.
|
| [83] |
LIN S, HU Q, WANG F, et al. Steering information diffusion dynamically against user attention limitation[C]. International Conference on Data Mining, Shenzhen, 2014: 330-339.
|
| [84] |
CHOOBDAR S, RIBEIRO P, PARTHASARATHY S, et al. Dynamic inference of social roles in information cascades[J]. Data Mining and Knowledge Discovery, 2015, 29(5): 1152-1177. doi: 10.1007/s10618-015-0402-5.
|
| [85] |
LI Y, QIAN M, JIN D, et al. Revealing the efficiency of information diffusion in online social networks of microblog[J]. Information Sciences, 2015, 293(1): 383-389. doi: 10.1016/ j.ins.2014.09.019.
|
| [86] |
GOMEZ R M and SONG L. Diffusion in social and information networks: Research problems, probabilistic models and machine learning methods[C]. Proceedings of the 21th International Conference on Knowledge Discovery and Data Mining, Sydney, 2015: 2315-2316.
|
| [87] |
MYERS S A, ZHU C, and LESKOVEC J. Information diffusion and external influence in networks[C]. Proceedings of the 18th International Conference on Knowledge Discovery and Data Mining, Beijing, 2012: 33-41.
|
| [88] |
ST Clair J J H, BURNS Z T, BETTANEY E M, et al. Experimental resource pulses influence social-network dynamics and the potential for information flow in tool-using crows[J]. Nature Communications, 2015, 6(1): 1-8. doi: 10.1038/ncomms8197.
|
| [89] |
DANESHMAND H, GOMEZRODRIGUEZ M, SONG L, et al. Estimating diffusion network structures: Recovery conditions, sample complexity & soft-thresholding algorithm [C]. International Conference on Machine Learning, Beijing, 2014: 793-801.
|
| [90] |
TAXIDOU I and FISCHER P M. Online analysis of information diffusion in twitter[C]. Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion, Seoul, 2014: 1313-1318.
|
| [91] |
DE NIES T, TAXIDOU I, DIMOU A, et al. Towards multi- level provenance reconstruction of information diffusion on social media[C]. Proceedings of the 24th International on Conference on Information and Knowledge Management, Melbourne, 2015: 1823-1826.
|
|
|
|
