Social Media Mining: An Introduction

The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.

[1]  A. M. Madni,et al.  Recommender systems in e-commerce , 2014, 2014 World Automation Congress (WAC).

[2]  Philip S. Yu,et al.  Link Mining: Models, Algorithms, and Applications , 2014, Link Mining.

[3]  Reza Zafarani,et al.  Connecting users across social media sites: a behavioral-modeling approach , 2013, KDD.

[4]  Huan Liu,et al.  Exploiting Local and Global Social Context for Recommendation , 2013, IJCAI.

[5]  Huan Liu,et al.  Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose , 2013, ICWSM.

[6]  Huan Liu,et al.  Unsupervised sentiment analysis with emotional signals , 2013, WWW.

[7]  Huan Liu,et al.  CoSelect: Feature Selection with Instance Selection for Social Media Data , 2013, SDM.

[8]  Reza Zafarani,et al.  Whom should I follow?: identifying relevant users during crises , 2013, HT.

[9]  Huan Liu,et al.  Provenance Data in Social Media , 2013, Synthesis Lectures on Data Mining and Knowledge Discovery.

[10]  Mehrbakhsh Nilashi,et al.  Collaborative filtering recommender systems , 2013 .

[11]  Huan Liu,et al.  Exploiting social relations for sentiment analysis in microblogging , 2013, WSDM.

[12]  Huan Liu,et al.  Exploiting homophily effect for trust prediction , 2013, WSDM.

[13]  Brendan D. McKay,et al.  Practical graph isomorphism, II , 2013, J. Symb. Comput..

[14]  Huan Liu,et al.  gSCorr: modeling geo-social correlations for new check-ins on location-based social networks , 2012, CIKM.

[15]  Huan Liu,et al.  eTrust: understanding trust evolution in an online world , 2012, KDD.

[16]  Huan Liu,et al.  Unsupervised feature selection for linked social media data , 2012, KDD.

[17]  Huan Liu,et al.  Community detection via heterogeneous interaction analysis , 2012, Data Mining and Knowledge Discovery.

[18]  Reza Zafarani,et al.  Maximizing benefits from crowdsourced data , 2012, Computational and Mathematical Organization Theory.

[19]  Huan Liu,et al.  Exploring Social-Historical Ties on Location-Based Social Networks , 2012, ICWSM.

[20]  Huan Liu,et al.  Feature Selection with Linked Data in Social Media , 2012, SDM.

[21]  Mohammad Ali Abbasi,et al.  Real-World Behavior Analysis through a Social Media Lens , 2012, SBP.

[22]  Huan Liu,et al.  mTrust: discerning multi-faceted trust in a connected world , 2012, WSDM '12.

[23]  Huan Liu,et al.  Spectral Feature Selection for Data Mining , 2011 .

[24]  Huan Liu,et al.  Exploiting vulnerability to secure user privacy on a social networking site , 2011, KDD.

[25]  Reza Zafarani,et al.  Understanding User Migration Patterns in Social Media , 2011, AAAI.

[26]  Cecilia Mascolo,et al.  NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems , 2011, Pervasive.

[27]  Huiji Gao,et al.  Harnessing the Crowdsourcing Power of Social Media for Disaster Relief , 2011, IEEE Intelligent Systems.

[28]  Bruno S. Silvestre,et al.  Social Media? Get Serious! Understanding the Functional Building Blocks of Social Media , 2011 .

[29]  Huan Liu,et al.  Promoting Coordination for Disaster Relief - From Crowdsourcing to Coordination , 2011, SBP.

[30]  Chao Liu,et al.  Recommender systems with social regularization , 2011, WSDM '11.

[31]  Duncan J. Watts,et al.  Everyone's an influencer: quantifying influence on twitter , 2011, WSDM '11.

[32]  Huan Liu,et al.  Discovering Overlapping Groups in Social Media , 2010, 2010 IEEE International Conference on Data Mining.

[33]  Jure Leskovec,et al.  Modeling Information Diffusion in Implicit Networks , 2010, 2010 IEEE International Conference on Data Mining.

[34]  Linyuan Lu,et al.  Link Prediction in Complex Networks: A Survey , 2010, ArXiv.

[35]  Gerhard Friedrich,et al.  Recommender Systems - An Introduction , 2010 .

[36]  Yu He,et al.  The YouTube video recommendation system , 2010, RecSys '10.

[37]  David Carmel,et al.  Social media recommendation based on people and tags , 2010, SIGIR.

[38]  Fei Wang,et al.  The importance of spatial-temporal issues for case-based reasoning in disaster management , 2010, 2010 18th International Conference on Geoinformatics.

[39]  Maria Luisa Sapino,et al.  Data Management for Multimedia Retrieval , 2010 .

[40]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[41]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[42]  Brendan T. O'Connor,et al.  From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series , 2010, ICWSM.

[43]  Huan Liu,et al.  Community Detection and Mining in Social Media , 2010 .

[44]  Cosma Rohilla Shalizi,et al.  Homophily and Contagion Are Generically Confounded in Observational Social Network Studies , 2010, Sociological methods & research.

[45]  Lars Backstrom,et al.  Find me if you can: improving geographical prediction with social and spatial proximity , 2010, WWW '10.

[46]  Jennifer Neville,et al.  Randomization tests for distinguishing social influence and homophily effects , 2010, WWW '10.

[47]  Jure Leskovec,et al.  Empirical comparison of algorithms for network community detection , 2010, WWW '10.

[48]  Michael F. Goodchild,et al.  Please Scroll down for Article International Journal of Digital Earth Crowdsourcing Geographic Information for Disaster Response: a Research Frontier Crowdsourcing Geographic Information for Disaster Response: a Research Frontier , 2022 .

[49]  Reza Zafarani,et al.  Sentiment Propagation in Social Networks: A Case Study in LiveJournal , 2010, SBP.

[50]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[51]  Michael A. Stefanone,et al.  Face off: Implications of visual cues on initiating friendship on Facebook , 2010, Comput. Hum. Behav..

[52]  Jiahui Liu,et al.  Personalized news recommendation based on click behavior , 2010, IUI '10.

[53]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[54]  Qi He,et al.  TwitterRank: finding topic-sensitive influential twitterers , 2010, WSDM '10.

[55]  Matthew O. Jackson,et al.  Naïve Learning in Social Networks and the Wisdom of Crowds , 2010 .

[56]  Arun Sundararajan,et al.  Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks , 2009, Proceedings of the National Academy of Sciences.

[57]  Jukka-Pekka Onnela,et al.  Spontaneous emergence of social influence in online systems , 2009, Proceedings of the National Academy of Sciences.

[58]  Michael R. Lyu,et al.  Learning to recommend with trust and distrust relationships , 2009, RecSys '09.

[59]  I. Fowler Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives , 2009 .

[60]  Andrea Lancichinetti,et al.  Community detection algorithms: a comparative analysis: invited presentation, extended abstract , 2009, VALUETOOLS.

[61]  Ioannis Konstas,et al.  On social networks and collaborative recommendation , 2009, SIGIR.

[62]  Anna Monreale,et al.  WhereNext: a location predictor on trajectory pattern mining , 2009, KDD.

[63]  Jure Leskovec,et al.  Meme-tracking and the dynamics of the news cycle , 2009, KDD.

[64]  Huan Liu,et al.  Relational learning via latent social dimensions , 2009, KDD.

[65]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[66]  John Riedl,et al.  Tagommenders: connecting users to items through tags , 2009, WWW '09.

[67]  Michael J. Muller,et al.  Make new friends, but keep the old: recommending people on social networking sites , 2009, CHI.

[68]  Reza Zafarani,et al.  Connecting Corresponding Identities across Communities , 2009, ICWSM.

[69]  Ted G. Lewis,et al.  Network Science: Theory and Applications , 2009 .

[70]  Jaime G. Carbonell,et al.  Document Representation and Query Expansion Models for Blog Recommendation , 2008, ICWSM.

[71]  Michael R. Lyu,et al.  SoRec: social recommendation using probabilistic matrix factorization , 2008, CIKM '08.

[72]  Alessandro Vespignani,et al.  Dynamical Processes on Complex Networks , 2008 .

[73]  M. Jackson Social and Economic Networks , 2008 .

[74]  Ravi Kumar,et al.  Influence and correlation in social networks , 2008, KDD.

[75]  Roelof van Zwol,et al.  Flickr tag recommendation based on collective knowledge , 2008, WWW.

[76]  Fabio Porto,et al.  A conceptual view on trajectories , 2008, Data Knowl. Eng..

[77]  Philip S. Yu,et al.  Identifying the influential bloggers in a community , 2008, WSDM '08.

[78]  D. Watts,et al.  Influentials, Networks, and Public Opinion Formation , 2007 .

[79]  S Riley,et al.  A model to control the epidemic of H5N1 influenza at the source , 2007, BMC infectious diseases.

[80]  E. Young Contagion , 2015, New Scientist.

[81]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[82]  M. Jackson,et al.  An Economic Model of Friendship: Homophily, Minorities and Segregation , 2007 .

[83]  Danah Boyd,et al.  Social network sites: definition, history, and scholarship , 2007, IEEE Engineering Management Review.

[84]  Philip S. Yu,et al.  GraphScope: parameter-free mining of large time-evolving graphs , 2007, KDD '07.

[85]  Jon M. Kleinberg,et al.  Challenges in mining social network data: processes, privacy, and paradoxes , 2007, KDD '07.

[86]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[87]  Abhinandan Das,et al.  Google news personalization: scalable online collaborative filtering , 2007, WWW '07.

[88]  Tu Minh Phuong,et al.  A Gaussian Mixture Model for Mobile Location Prediction , 2007, 2007 IEEE International Conference on Research, Innovation and Vision for the Future.

[89]  Christos Faloutsos,et al.  Cascading Behavior in Large Blog Graphs , 2007 .

[90]  Nguyen Thanh,et al.  A Gaussian Mixture Model for Mobile Location Prediction , 2007, The 9th International Conference on Advanced Communication Technology.

[91]  E. Holmes,et al.  The evolution of epidemic influenza , 2007, Nature Reviews Genetics.

[92]  A. Raftery,et al.  Model‐based clustering for social networks , 2007 .

[93]  Bing Liu,et al.  Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data , 2006, Data-Centric Systems and Applications.

[94]  Jon M. Kleinberg,et al.  Group formation in large social networks: membership, growth, and evolution , 2006, KDD '06.

[95]  Ravi Kumar,et al.  Structure and evolution of online social networks , 2006, KDD '06.

[96]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[97]  John P. Nohl The Black Death: A Chronicle of the Plague , 2006 .

[98]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[99]  J. Golbeck,et al.  FilmTrust: movie recommendations using trust in web-based social networks , 2006, CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006..

[100]  Marc Teboulle,et al.  Grouping Multidimensional Data - Recent Advances in Clustering , 2006 .

[101]  Gueorgi Kossinets,et al.  Empirical Analysis of an Evolving Social Network , 2006, Science.

[102]  Andries Petrus Engelbrecht,et al.  Fundamentals of Computational Swarm Intelligence , 2005 .

[103]  Mathias Drehmann,et al.  Herding and Contrarian Behavior in Financial Markets: An Internet Experiment , 2005 .

[104]  M. Newman,et al.  Vertex similarity in networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[105]  M. Keeling,et al.  Networks and epidemic models , 2005, Journal of The Royal Society Interface.

[106]  Stephen P. Boyd,et al.  Convex Optimization , 2010, IEEE Transactions on Automatic Control.

[107]  Ravi Kumar,et al.  Discovering Large Dense Subgraphs in Massive Graphs , 2005, VLDB.

[108]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[109]  Gediminas Adomavicius,et al.  Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions , 2005, IEEE Transactions on Knowledge and Data Engineering.

[110]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.

[111]  Huan Liu,et al.  Toward integrating feature selection algorithms for classification and clustering , 2005, IEEE Transactions on Knowledge and Data Engineering.

[112]  Boris Mirkin,et al.  Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science) , 2005 .

[113]  Barry Smyth,et al.  Trust in recommender systems , 2005, IUI.

[114]  Ramanathan V. Guha,et al.  Information diffusion through blogspace , 2004, WWW '04.

[115]  Paolo Avesani,et al.  Trust-Aware Collaborative Filtering for Recommender Systems , 2004, CoopIS/DOA/ODBASE.

[116]  N. Christakis Social networks and collateral health effects , 2004, BMJ : British Medical Journal.

[117]  T. Lindvall ON A ROUTING PROBLEM , 2004, Probability in the Engineering and Informational Sciences.

[118]  Stuart J. Barnes,et al.  Mobile marketing: the role of permission and acceptance , 2004, Int. J. Mob. Commun..

[119]  Peter Sheridan Dodds,et al.  Universal behavior in a generalized model of contagion. , 2004, Physical review letters.

[120]  Guoping Zhao,et al.  Molecular Evolution of the SARS Coronavirus During the Course of the SARS Epidemic in China , 2004, Science.

[121]  Chaomei Chen,et al.  Mining the Web: Discovering knowledge from hypertext data , 2004, J. Assoc. Inf. Sci. Technol..

[122]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[123]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[124]  Lada A. Adamic,et al.  Friends and neighbors on the Web , 2003, Soc. Networks.

[125]  C. Dye,et al.  Modeling the SARS Epidemic , 2003, Science.

[126]  Uwe Hansmann,et al.  Pervasive Computing , 2003 .

[127]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[128]  I. J. Myung,et al.  Tutorial on maximum likelihood estimation , 2003 .

[129]  J. Berry The Influentials: One American in Ten Tells the Other Nine How to Vote, Where to Eat, and What to Buy , 2003 .

[130]  R. Burke Hybrid Recommender Systems: Survey and Experiments , 2002, User Modeling and User-Adapted Interaction.

[131]  Patrick Barwise,et al.  Permission-based mobile advertising , 2002 .

[132]  M. Newman,et al.  Mixing Patterns and Community Structure in Networks , 2002, cond-mat/0210146.

[133]  M. Newman,et al.  Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[134]  Stephanie Forrest,et al.  Email networks and the spread of computer viruses. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[135]  M. Macy,et al.  FROM FACTORS TO ACTORS: Computational Sociology and Agent-Based Modeling , 2002 .

[136]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[137]  Duncan J Watts,et al.  A simple model of global cascades on random networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[138]  Sandra Sudarsky,et al.  Massive Quasi-Clique Detection , 2002, LATIN.

[139]  T. Runge,et al.  Smallpox and the Native American , 2002, The American journal of the medical sciences.

[140]  S H Strogatz,et al.  Random graph models of social networks , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[141]  M. Newman,et al.  Random Graphs as Models of Networks , 2002, cond-mat/0202208.

[142]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[143]  M. McPherson,et al.  Birds of a Feather: Homophily in Social Networks , 2001 .

[144]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[145]  Hussein Dia,et al.  An object-oriented neural network approach to short-term traffic forecasting , 2001, Eur. J. Oper. Res..

[146]  U. Brandes A faster algorithm for betweenness centrality , 2001 .

[147]  John Riedl,et al.  Item-based collaborative filtering recommendation algorithms , 2001, WWW '01.

[148]  H. Bernard Social Research Methods: Qualitative and Quantitative Approaches , 2000 .

[149]  Herbert W. Hethcote,et al.  The Mathematics of Infectious Diseases , 2000, SIAM Rev..

[150]  Alessandro Vespignani,et al.  Epidemic spreading in scale-free networks. , 2000, Physical review letters.

[151]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[152]  M. Newman,et al.  Random graphs with arbitrary degree distributions and their applications. , 2000, Physical review. E, Statistical, nonlinear, and soft matter physics.

[153]  Hendrik Blockeel,et al.  Web mining research: a survey , 2000, SKDD.

[154]  Andrei Z. Broder,et al.  Graph structure in the Web , 2000, Comput. Networks.

[155]  S. Redner,et al.  Connectivity of growing random networks. , 2000, Physical review letters.

[156]  Albert,et al.  Topology of evolving networks: local events and universality , 2000, Physical review letters.

[157]  Huan Liu,et al.  Feature Selection for Clustering , 2000, Encyclopedia of Database Systems.

[158]  S. Bikhchandani,et al.  Herd Behavior in Financial Markets , 2000, IMF Staff Papers.

[159]  M. Newman,et al.  Epidemics and percolation in small-world networks. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[160]  E. Berger,et al.  Dynamic Monopolies of Constant Size , 1999, J. Comb. Theory B.

[161]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[162]  D. Watts Networks, Dynamics, and the Small‐World Phenomenon1 , 1999, American Journal of Sociology.

[163]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[164]  Jon Kleinberg,et al.  Authoritative sources in a hyperlinked environment , 1999, SODA '98.

[165]  P. Faloutsos,et al.  On power-law relationships of the Internet topology , 1999, SIGCOMM '99.

[166]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[167]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.

[168]  Christos Faloutsos,et al.  Proceedings of the 1999 ACM SIGMOD international conference on Management of data , 1999, SIGMOD 1999.

[169]  Ravi Kumar,et al.  Trawling the Web for Emerging Cyber-Communities , 1999, Comput. Networks.

[170]  H. Peyton Young,et al.  Individual Strategy and Social Structure , 2020 .

[171]  S. Bikhchandani,et al.  Learning from the behavior of others : conformity, fads, and informational cascades , 1998 .

[172]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[173]  Hiroshi Motoda,et al.  Feature Extraction, Construction and Selection: A Data Mining Perspective , 1998 .

[174]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[175]  Hans-Peter Kriegel,et al.  Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.

[176]  Charles A. Holt,et al.  Information Cascades in the Laboratory , 1998 .

[177]  J. Bouchaud,et al.  HERD BEHAVIOR AND AGGREGATE FLUCTUATIONS IN FINANCIAL MARKETS , 1997, Macroeconomic Dynamics.

[178]  Huan Liu,et al.  Feature Selection for Classification , 1997, Intell. Data Anal..

[179]  Paul Resnick,et al.  Recommender systems , 1997, CACM.

[180]  Lisa R. Anderson,et al.  Classroom Games: Information Cascades , 1996 .

[181]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[182]  D. Peleg Local Majority Voting, Small Coalitions and Controlling Monopolies in Graphs: A Review , 1996 .

[183]  I. Welch,et al.  Rational herding in financial economics , 1996 .

[184]  Douglas Gale,et al.  What have we learned from social learning , 1996 .

[185]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[186]  D. West Introduction to Graph Theory , 1995 .

[187]  Pat Langley Elements of Machine Learning , 1995 .

[188]  R. Shiller Conversation, Information, and Herd Behavior , 1995 .

[189]  Tommy R. Jensen,et al.  Graph Coloring Problems , 1994 .

[190]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[191]  Jiawei Han,et al.  Efficient and Effective Clustering Methods for Spatial Data Mining , 1994, VLDB.

[192]  Martin G. Everett,et al.  Two algorithms for computing regular equivalence , 1993 .

[193]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[194]  A. Banerjee,et al.  A Simple Model of Herd Behavior , 1992 .

[195]  I. Welch Sequential Sales, Learning, and Cascades , 1992 .

[196]  M. Macy Chains of Cooperation: Threshold Effects in Collective Action , 1991 .

[197]  Robert M. May,et al.  Infectious Diseases of Humans: Dynamics and Control , 1991 .

[198]  P. Rousseeuw Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[199]  Douglas H. Fisher,et al.  Improving Inference through Conceptual Clustering , 1987, AAAI.

[200]  R. Tarjan Algorithm design , 1987, CACM.

[201]  Andrew V. Goldberg,et al.  A new approach to the maximum flow problem , 1986, STOC '86.

[202]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[203]  Lawrence R. Rabiner,et al.  Combinatorial optimization:Algorithms and complexity , 1984 .

[204]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[205]  E. Hirschman Innovativeness, Novelty Seeking, and Consumer Creativity , 1980 .

[206]  Donald W. Bouldin,et al.  A Cluster Separation Measure , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[207]  Robert A. Peterson,et al.  Innovation Diffusion in a Dynamic Potential Adopter Population , 1978 .

[208]  Mark S. Granovetter Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[209]  N. Ling The Mathematical Theory of Infectious Diseases and its applications , 1978 .

[210]  D. Midgley,et al.  Innovativeness: The Concept and Its Measurement , 1978 .

[211]  J. A. Bondy,et al.  Graph Theory with Applications , 1978 .

[212]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[213]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[214]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[215]  R. L. Hamblin,et al.  A mathematical theory of social change , 1975 .

[216]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[217]  S. Milgram Obedience to Authority: An Experimental View , 1975 .

[218]  Virginia Gray,et al.  Innovation in the States: A Diffusion Study , 1973, American Political Science Review.

[219]  J. Hopcroft,et al.  Algorithm 447: efficient algorithms for graph manipulation , 1973, CACM.

[220]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[221]  Richard M. Karp,et al.  Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems , 1972, Combinatorial Optimization.

[222]  Thomas C. Schelling,et al.  Dynamic models of segregation , 1971 .

[223]  Stanley Milgram,et al.  An Experimental Study of the Small World Problem , 1969 .

[224]  Philip Ziegler THE BLACK DEATH , 1969 .

[225]  S. Milgram,et al.  Note on the drawing power of crowds of different size. , 1969 .

[226]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[227]  J. Coleman,et al.  Medical Innovation: A Diffusion Study. , 1967 .

[228]  Klaus Dietz,et al.  Epidemics and Rumours: A Survey , 1967 .

[229]  Torsten Hägerstrand,et al.  Innovation Diffusion As a Spatial Process , 1967 .

[230]  T. S. Robertson The Process of Innovation and the Diffusion of Innovation , 1967 .

[231]  F. Collingwood THE GREAT PLAGUE OF LONDON, 1665. , 1965, Nursing times.

[232]  Stephen J. Garland,et al.  Algorithm 97: Shortest path , 1962, Commun. ACM.

[233]  E. Mansfield TECHNICAL CHANGE AND THE RATE OF IMITATION , 1961 .

[234]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[235]  Richard Bellman,et al.  ON A ROUTING PROBLEM , 1958 .

[236]  R. Prim Shortest connection networks and some generalizations , 1957 .

[237]  Z. Griliches HYBRID CORN: AN EXPLORATION IN THE ECONOMIC OF TECHNOLOGICAL CHANGE , 1957 .

[238]  P. Lazarsfeld,et al.  Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[239]  H. Simon,et al.  ON A CLASS OF SKEW DISTRIBUTION FUNCTIONS , 1955 .

[240]  A. Rapoport,et al.  Connectivity of random nets , 1951 .

[241]  H. Leibenstein Bandwagon, Snob, and Veblen Effects in the Theory of Consumers' Demand , 1950 .

[242]  G. Simmel The Sociology of Sociability , 1949, American Journal of Sociology.

[243]  S S Stevens,et al.  On the Theory of Scales of Measurement. , 1946, Science.

[244]  W. O. Kermack,et al.  Contributions to the Mathematical Theory of Epidemics. II. The Problem of Endemicity , 1932 .

[245]  G. Yule,et al.  A Mathematical Theory of Evolution Based on the Conclusions of Dr. J. C. Willis, F.R.S. , 1925 .

[246]  T. Veblen The Theory of the Leisure Class , 1901 .

[247]  Andrea Klug,et al.  Understanding Social Networks Theories Concepts And Findings , 2016 .

[248]  อนิรุธ สืบสิงห์ Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[249]  Ted K. Ralphs,et al.  Integer and Combinatorial Optimization , 2013 .

[250]  Huan Liu,et al.  Mining Social Media: A Brief Introduction , 2012 .

[251]  S. Bornholdt,et al.  Handbook of Graphs and Networks , 2012 .

[252]  Sergiy Butenko,et al.  Clique Relaxation Models in Social Network Analysis , 2012 .

[253]  Panos M. Pardalos,et al.  Handbook of Optimization in Complex Networks , 2012 .

[254]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[255]  Helmut Leopold,et al.  Social Media , 2012, Elektrotech. Informationstechnik.

[256]  Jiliang Tang,et al.  Mobile Location Prediction in Spatio-Temporal Context , 2012 .

[257]  Tom A. B. Snijders,et al.  Social Network Analysis , 2011, International Encyclopedia of Statistical Science.

[258]  A. Kaplan,et al.  Users of the world, unite! The challenges and opportunities of Social Media , 2010 .

[259]  Karimnagar Salim Jiwani,et al.  A Survey on clustering , 2010 .

[260]  Taghi M. Khoshgoftaar,et al.  A Survey of Collaborative Filtering Techniques , 2009, Adv. Artif. Intell..

[261]  H. Simon,et al.  American Association for Public Opinion Research Bandwagon and Underdog Effects and the Possibility of Election Predictions , 2009 .

[262]  J. Stockman The Spread of Obesity in a Large Social Network over 32 Years , 2009 .

[263]  S. Bikhchandani,et al.  You have printed the following article : A Theory of Fads , Fashion , Custom , and Cultural Change as Informational Cascades , 2007 .

[264]  Kees van Montfort,et al.  Longitudinal models in the behavioral and related sciences , 2007 .

[265]  Barry Smyth,et al.  Recommendation to Groups , 2007, The Adaptive Web.

[266]  T. Snijders,et al.  Modeling the Coevolution of Networks and Behavior , 2007 .

[267]  Kathleen C. Schwartzman,et al.  DIFFUSION IN ORGANIZATIONS AND SOCIAL MOVEMENTS: From Hybrid Corn to Poison Pills , 2007 .

[268]  Albert Y. Zomaya Handbook of Nature-Inspired and Innovative Computing - Integrating Classical Models with Emerging Technologies , 2006 .

[269]  Mark E. J. Newman,et al.  Structure and Dynamics of Networks , 2009 .

[270]  Nicholas Walliman,et al.  Social research methods , 2006 .

[271]  Mohammad Al Hasan,et al.  Link prediction using supervised learning , 2006 .

[272]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[273]  Chih-Jen Lin,et al.  Feature Extraction, Foundations and Applications , 2006 .

[274]  Patrick Barwise,et al.  STRONG, PERMISSION-BASED MOBILE ADVERTISING , 2002 .

[275]  Sergio Rajsbaum,et al.  LATIN 2002: Theoretical Informatics , 2002, Lecture Notes in Computer Science.

[276]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[277]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[278]  P. ERDbS ON THE STRENGTH OF CONNECTEDNESS OF A RANDOM GRAPH , 2001 .

[279]  Russ Bubley,et al.  Randomized algorithms , 1995, CSUR.

[280]  A. Andrew,et al.  Emergence of Scaling in Random Networks , 1999 .

[281]  R. Cialdini,et al.  Social influence: Social norms, conformity and compliance. , 1998 .

[282]  Michel Bierlaire,et al.  DynaMIT: a simulation-based system for traffic prediction and guidance generation , 1998 .

[283]  H. Hethcote A Thousand and One Epidemic Models , 1994 .

[284]  André Hardy,et al.  An examination of procedures for determining the number of clusters in a data set , 1994 .

[285]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[286]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[287]  W. O. Kermack,et al.  Contributions to the mathematical theory of epidemics—I , 1991, Bulletin of mathematical biology.

[288]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[289]  Robert A. Peterson,et al.  Models for innovation diffusion , 1985 .

[290]  B. Bollobás The evolution of random graphs , 1984 .

[291]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[292]  S. Levin Lectu re Notes in Biomathematics , 1983 .

[293]  H. Hethcote PERIODICITY AND STABILITY IN EPIDEMIC MODELS: A SURVEY , 1981 .

[294]  M. Kochen,et al.  Contacts and influence , 1978 .

[295]  T. Schelling Micromotives and Macrobehavior , 1978 .

[296]  Alexander Grey,et al.  The Mathematical Theory of Infectious Diseases and Its Applications , 1977 .

[297]  Elwood S. Buffa,et al.  Graph Theory with Applications , 1977 .

[298]  Joseph Paul Martino,et al.  Technological Forecasting for Decisionmaking , 1975 .

[299]  J. Dunn Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[300]  Frank Harary,et al.  Graph Theory , 2016 .

[301]  Stephen Warshall,et al.  A Theorem on Boolean Matrices , 1962, JACM.

[302]  S. Asch Studies of independence and conformity: I. A minority of one against a unanimous majority. , 1956 .

[303]  D. R. Fulkerson,et al.  Maximal Flow Through a Network , 1956, Canadian Journal of Mathematics.

[304]  J. Kruskal On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[305]  B. Ryan The diffusion of hybrid seed corn in two Iowa communities , 1943 .

[306]  G. Yule,et al.  A Mathematical Theory of Evolution, Based on the Conclusions of Dr. J. C. Willis, F.R.S. , 1925 .

[307]  P. Jaccard Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines , 1901 .

[308]  HighWire Press Philosophical Transactions of the Royal Society of London , 1781, The London Medical Journal.

[309]  D. Liben-Nowell,et al.  The link prediction problem for social networks , 2003, CIKM '03.

[310]  Bengt Holmstrom,et al.  Herd Behavior and Investment , 2022 .