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Latent Space Model for Multi-Modal Social Data

Published: 11 April 2016 Publication History

Abstract

With the emergence of social networking services, researchers enjoy the increasing availability of large-scale heterogenous datasets capturing online user interactions and behaviors. Traditional analysis of techno-social systems data has focused mainly on describing either the dynamics of social interactions, or the attributes and behaviors of the users. However, overwhelming empirical evidence suggests that the two dimensions affect one another, and therefore they should be jointly modeled and analyzed in a multi-modal framework. The benefits of such an approach include the ability to build better predictive models, leveraging social network information as well as user behavioral signals. To this purpose, here we propose the Constrained Latent Space Model (CLSM), a generalized framework that combines Mixed Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA) incorporating a constraint that forces the latent space to concurrently describe the multiple data modalities. We derive an efficient inference algorithm based on Variational Expectation Maximization that has a computational cost linear in the size of the network, thus making it feasible to analyze massive social datasets. We validate the proposed framework on two problems: prediction of social interactions from user attributes and behaviors, and behavior prediction exploiting network information. We perform experiments with a variety of multi-modal social systems, spanning location-based social networks (Gowalla), social media services (Instagram, Orkut), e-commerce and review sites (Amazon, Ciao), and finally citation networks (Cora). The results indicate significant improvement in prediction accuracy over state of the art methods, and demonstrate the flexibility of the proposed approach for addressing a variety of different learning problems commonly occurring with multi-modal social data.

References

[1]
L. A. Adamic and N. Glance. The political blogosphere and the 2004 us election: divided they blog. In Proceedings of the 3rd international workshop on Link discovery, pages 36--43. ACM, 2005.
[2]
S. Agreste, P. De Meo, E. Ferrara, S. Piccolo, and A. Provetti. Analysis of a heterogeneous social network of humans and cultural objects. IEEE Transactions on Systems, Man and Cybernetics: Systems, 45(4):559--570, 2015.
[3]
E. M. Airoldi, D. M. Blei, S. E. Fienberg, and E. P. Xing. Mixed membership stochastic blockmodels. J. Mach. Learn. Res., 9:1981--2014, June 2008.
[4]
F. Al Zamal, W. Liu, and D. Ruths. Homophily and latent attribute inference: Inferring latent attributes of twitter users from neighbors. In Proc. 6th International AAAI Conference on Weblogs and Social Media (ICWSM), pages 387--390, 2012.
[5]
E. Bakshy, I. Rosenn, C. Marlow, and L. Adamic. The role of social networks in information diffusion. In Proceedings of the 21st international conference on World Wide Web, pages 519--528. ACM, 2012.
[6]
R. A. Ba\ nos, J. Borge-Holthoefer, and Y. Moreno. The role of hidden influentials in the diffusion of online information cascades. EPJ Data Science, 2(1):1--16, 2013.
[7]
D. M. Blei. Probabilistic topic models. Communications of the ACM, 55(4):77--84, 2012.
[8]
D. M. Blei and M. I. Jordan. Modeling annotated data. In Proceedings of the 26th Annual International ACM the IR Conference on Research and Development in Informaion Retrieval, 2003.
[9]
D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, Mar. 2003.
[10]
C. Budak, D. Agrawal, and A. El Abbadi. Limiting the spread of misinformation in social networks. In Proceedings of the 20th international conference on World wide web, pages 665--674. ACM, 2011.
[11]
M. Cha, F. Benevenuto, Y.-Y. Ahn, and K. P. Gummadi. Delayed information cascades in flickr: Measurement, analysis, and modeling. Computer Networks, 56(3):1066--1076, 2012.
[12]
J. Chang and D. M. Blei. Relational topic models for document networks. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.
[13]
J. Cheng, L. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec. Can cascades be predicted? In Proceedings of the 23rd international conference on World wide web, pages 925--936. ACM, 2014.
[14]
E. Cho, S. A. Myers, and J. Leskovec. Friendship and mobility: User movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011.
[15]
Y.-S. Cho, G. Ver Steeg, and A. Galstyan. Co-evolution of selection and influence in social networks. In Proc. of the Twenty-Fifth Conference on Artificial Intelligence (AAAI-11), 2011.
[16]
Y.-S. Cho, G. Ver Steeg, and A. Galstyan. Socially relevant venue clustering from check-in data. In KDD Workshop on Mining and Learning with Graphs, 2013.
[17]
D. J. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher, and J. Kleinberg. Inferring social ties from geographic coincidences. Proceedings of the National Academy of Sciences, 107(52):22436--22441, Dec. 2010.
[18]
P. De Meo, E. Ferrara, F. Abel, L. Aroyo, and G.-J. Houben. Analyzing user behavior across social sharing environments. ACM Transactions on Intelligent Systems and Technology (TIST), 5(1):14, 2013.
[19]
P. De Meo, E. Ferrara, G. Fiumara, and A. Provetti. On facebook, most ties are weak. Communications of the ACM, 57(11):78--84, 2014.
[20]
P. A. Dow, L. A. Adamic, and A. Friggeri. The anatomy of large facebook cascades. In Proc. 7th International AAAI Conference on Weblogs and Social Media (ICWSM), pages 145--154, 2013.
[21]
E. Ferrara. A large-scale community structure analysis in facebook. EPJ Data Science, 1(9):1--30, 2012.
[22]
E. Ferrara, R. Interdonato, and A. Tagarelli. Online popularity and topical interests through the lens of instagram. In Proceedings of the 25th ACM conference on Hypertext and social media, pages 24--34. ACM, 2014.
[23]
E. Ferrara, O. Varol, F. Menczer, and A. Flammini. Traveling trends: social butterflies or frequent fliers' In Proceedings of the first ACM conference on Online social networks, pages 213--222. ACM, 2013.
[24]
E. Ferrara and Z. Yang. Quantifying the effect of sentiment on information diffusion in social media. PeerJ Computer Science, 1:e26, 2015.
[25]
A. Friggeri, L. A. Adamic, D. Eckles, and J. Cheng. Rumor cascades. In Proceedings of the Eighth International AAAI Conference on Weblogs and Social Media, pages 101--110, 2014.
[26]
S. Goel, D. J. Watts, and D. G. Goldstein. The structure of online diffusion networks. In Proceedings of the 13th ACM conference on electronic commerce, pages 623--638. ACM, 2012.
[27]
N. Z. Gong, A. Talwalkar, L. Mackey, L. Huang, E. C. R. Shin, E. Stefanov, E. R. Shi, and D. Song. Joint link prediction and attribute inference using a social-attribute network. ACM Transactions on Intelligent Systems and Technology (TIST), 5(2):27, 2014.
[28]
P. Gopalan, D. M. Mimno, S. Gerrish, M. J. Freedman, and D. M. Blei. Scalable inference of overlapping communities. In Proceedings of the Advances in Neural Information Processing Systems 25, 2012.
[29]
P. D. Hoff, A. E. Raftery, and M. S. Handcock. Latent space approaches to social network analysis. Journal of the American Statistical Association, 97:1090--1098, 2001.
[30]
B. Huberman, D. Romero, and F. Wu. Social networks that matter: Twitter under the microscope. First Monday, 14(1), 2008.
[31]
R. J. Hyndman and A. B. Koehler. Another look at measures of forecast accuracy. International journal of forecasting, 22(4):679--688, 2006.
[32]
J. Jiang, C. Wilson, X. Wang, W. Sha, P. Huang, Y. Dai, and B. Y. Zhao. Understanding latent interactions in online social networks. ACM Trans. Web, 7(4):18:1--18:39, 2013.
[33]
M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul. An introduction to variational methods for graphical models. 1999.
[34]
K. Joseph, C. H. Tan, and K. M. Carley. Beyond "local", "categories" and "friends": clustering foursquare users with latent "topics". In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, 2012.
[35]
D. Jurafsky and J. H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall PTR, Upper Saddle River, NJ, USA, 1st edition, 2000.
[36]
M. Kim and J. Leskovec. Modeling social networks with node attributes using the multiplicative attribute graph model. In 27th Conference on Uncertainty in Artificial Intelligence (UAI 2011), pages 400--409, 2011.
[37]
M. Kosinski, D. Stillwell, and T. Graepel. Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15):5802--5805, 2013.
[38]
H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, pages 591--600. ACM, 2010.
[39]
K. Lerman and R. Ghosh. Information contagion: an empirical study of spread of news on digg and twitter social networks. In Proceedings of 4th International Conference on Weblogs and Social Media (ICWSM), pages 90--97, 2010.
[40]
A. K. McCallum, K. Nigam, J. Rennie, and K. Seymore. Automating the construction of internet portals with machine learning. Information Retrieval, 3(2):127--163, 2000.
[41]
M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1):415--444, 2001.
[42]
A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, pages 29--42. ACM, 2007.
[43]
P. J. Mucha, T. Richardson, K. Macon, M. A. Porter, and J.-P. Onnela. Community structure in time-dependent, multiscale, and multiplex networks. science, 328(5980):876--878, 2010.
[44]
S. A. Myers and J. Leskovec. The bursty dynamics of the twitter information network. In Proceedings of the 23rd international conference on World wide web, pages 913--924. ACM, 2014.
[45]
R. M. Nallapati, A. Ahmed, E. P. Xing, and W. W. Cohen. Joint latent topic models for text and citations. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008.
[46]
M. Newman and A. Clauset. Structure and inference in annotated networks. arXiv preprint arXiv:1507.04001, 2015.
[47]
Y. Pang, Q. Hao, Y. Yuan, T. Hu, R. Cai, and L. Zhang. Summarizing tourist destinations by mining user-generated travelogues and photos. Comput. Vis. Image Underst., 115(3):352--363, Mar. 2011.
[48]
T. P. Peixoto. Inferring the mesoscale structure of layered, edge-valued, and time-varying networks. Phys. Rev. E, 92:042807, Oct 2015.
[49]
D. M. Romero, C. Tan, and J. Ugander. On the interplay between social and topical structure. Proc. 7th International AAAI Conference on Weblogs and Social Media (ICWSM), pages 516--525, 2013.
[50]
A. Sadilek, H. Kautz, and J. P. Bigham. Finding your friends and following them to where you are. In Proceedings of the 5th ACM International Conference on Web Search and Data Mining, 2012.
[51]
P. Sen, G. M. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3):93--106, 2008.
[52]
M. Szell, R. Lambiotte, and S. Thurner. Multirelational organization of large-scale social networks in an online world. Proceedings of the National Academy of Sciences, 107(31):13636--13641, 2010.
[53]
D. Wang, D. Pedreschi, C. Song, F. Giannotti, and A.-L. Barabasi. Human mobility, social ties, and link prediction. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011.
[54]
S. Wu, J. M. Hofman, W. A. Mason, and D. J. Watts. Who says what to whom on twitter. In Proceedings of the 20th international conference on World wide web, pages 705--714. ACM, 2011.
[55]
J. Yang and J. Leskovec. Defining and evaluating network communities based on ground-truth. In ICDM, pages 745--754. IEEE Computer Society, 2012.
[56]
J. Yang, J. McAuley, and J. Leskovec. Community detection in networks with node attributes. In 2013 IEEE 13th International Conference on Data Mining (ICDM), pages 1151--1156, 2013.
[57]
Y. Zhu, X. Yan, L. Getoor, and C. Moore. Scalable text and link analysis with mixed-topic link models. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '13, pages 473--481, New York, NY, USA, 2013. ACM.

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Published In

cover image ACM Other conferences
WWW '16: Proceedings of the 25th International Conference on World Wide Web
April 2016
1482 pages
ISBN:9781450341431

Sponsors

  • IW3C2: International World Wide Web Conference Committee

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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Author Tags

  1. LDA
  2. multi-modal social networks
  3. topic models

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  • Research-article

Funding Sources

  • DARPA

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WWW '16
Sponsor:
  • IW3C2
WWW '16: 25th International World Wide Web Conference
April 11 - 15, 2016
Québec, Montréal, Canada

Acceptance Rates

WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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  • (2019)Human Values and Attitudes towards Vaccination in Social MediaCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316489(248-254)Online publication date: 13-May-2019
  • (2019)Knowledge Graph Enhanced Community Detection and CharacterizationProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3291031(51-59)Online publication date: 30-Jan-2019
  • (2019)CrossSimON: A Novel Probabilistic Approach to Cross-Platform Online Social Network Simulation2019 IEEE International Conference on Intelligence and Security Informatics (ISI)10.1109/ISI.2019.8823276(7-12)Online publication date: Jul-2019
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  • (2018)A social interaction activity based time-varying user vectorization method for online social networksProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304296(3790-3796)Online publication date: 13-Jul-2018
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