Syllabus and Textbooks
The required reading will be from the following list as well as select journal articles and conference papers (see below) which are available online to UML students:
[NCM] Networks, Crowds, and Markets: Reasoning About a Highly Connected World
David Easley and Jon Kleinberg
[MMD] Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeffrey D. Ullman
[SNA] Social Network Data Analytics
Editor: Charu C. Aggarwal
Syllabus
Prior to every class, we provide a list of Spotlight Papers - select research papers relevant to a specific lecture. For some classes, students are expected to choose and read one of the provided spotlight papers in-depth and write a summary/review of the paper before the class. See further details in Policies section.
| W01: Jan 21 - Jan 24 | Introduction and Basics: course overview, connectivity, bfs, shortest path, bipartite graphs | Slide 0,1,2,3 |
| Ch.01 Overview [NCM] Ch.10.1 Social Networks as Graphs [MMD] Ch.02 Graphs [NCM] Ch.22 Elementary Graph Algorithms [CLRS] Ch.24 Single-Source Shortest Paths [CLRS] | What is Twitter, a social network or a news media? Kwak, H., et al. WWW’10 Understanding the demographics of twitter users. Mislove, A., et al. AAAI’11  TED talk: The birth of a word. Roy, D. 2011. | |
| W02: Jan 27 - Jan 31 | Strong and Weak Ties: centrality, betweenness, graph clustering | Slide 1,2 |
| Ch.03 Strong and Weak Ties [NCM] Ch.07, 10.2 Clustering [MMD] Ch.04 Community Discovery [SNA] Ch.07 Social Influence [SNA] | Community detection in graphs. Fortunato, S. Physics reports’10 Structure and tie strengths in mobile communication networks. Onnela, J.P., et al. PNAS’07. Searching for superspreaders of information in real-world social media. Pei, S., et al. Scientific reports’14. Fragile online relationship: a first look at unfollow dynamics in twitter. Kwak, H., et al. SIGCHI’11. | |
| W03: Feb 03 - Feb 07 | Node Analysis: node similarity, homophily, and link prediction | Slide 1,2 |
| Ch.04 Networks in Surrounding Context [NCM] Ch.09 Link Prediction [SNA] | Group formation in large social networks: membership, growth, and evolution. Backstrom, L., et al. SIGKDD’06. Feedback effects between similarity and social influence in online communities. Crandall, D., et al. SIGKDD’08. Empirical analysis of an evolving social network. Kossinets, G. and Watts, D.J. Science’06. How to hide one's relationships from link prediction algorithms. Waniek, M., et al. Scientific reports’19. | |
| W04: Feb 10 - Feb 14 | Web Graph and Network Popularity: connected components, bow-tie structure, power Law | Slide 1,2 |
| Ch.13 The Structure of the Web [NCM] Ch.18 Power Laws [NCM] Ch.18 Rich-Get-Richer Phenomena [NCM] | Finding strongly connected components Inequality and unpredictability in an artificial cultural market. Salganik, M.J., et al. Science’06. Network structure from rich but noisy data. Newman, M.E.J. Nature Physics’18. Graph structure in the web. Broder, A., et al. Computer networks’00. | |
| W05: Feb 17 - Feb 21 | Information Cascading: information diffusion and basic cascade model | Slide 1,2 |
| Ch.16 Information Cascades [NCM] Ch.19 Cascading Behavior in Networks [NCM] | Do cascades recur? Cheng, J., et al. WWW’16. Multi-winner contests for strategic diffusion in social networks. Shen, W., et al. AAAI’19. Lecture: Information diffusion on Twitter. Nikolov, S. 2012. TED talk: How to start a movement. Sivers, D. 2010. | |
| W06: Feb 24 - Feb 28 | Small world Phenomenon: six degree separation, decentralized search, HITS and PageRank | Slide 1,2 |
| Ch.20 The Small-World Phenomenon [NCM] Ch.14 Link Analysis and Web search [NCM] Ch.05 Link Analysis [MMD] | The anatomy of the facebook social graph. Ugander, J., et al. arXiv’11. Four degrees of separation . Backstrom L., et al. WebSci’12. The small world problem . Milgram S. Psychology Today’1967. Documentary: Connected: the power of six degrees. A documentary on networks, social and otherwise. 2008. | |
| W07: Mar 02 - Mar 06 | Midterm exam! | |
| Midterm exam | ||
| W08: Mar 09 - Mar 13 | Spring Recess! | |
| No class | ||
| W09: Mar 16 - Mar 20 | Representation Learning 1: texts | Slide 1,2,3 |
| A neural probabilistic language model. Bengio, Yoshua, et al. JMLR’03. Distributed representations of words and phrases and their compositionality. Mikolov, T., et al. NIPS’13. Glove: global vectors for word representation. Pennington, J., et al. EMNLP’14. Retrofitting word vectors to semantic lexicons. Faruqui, M., et al. NAACL’15. AI2 talk: Beyond the Distributional Hypothesis: Learning Better Word Representations. Manaal F. 2016. | ||
| W10: Mar 23 - Mar 27 | Representation Learning 2: graphs | Slide 1,2,3,4 |
| Deepwalk: online learning of social representations. Perozzi, B., et al. SIGKDD’14. Node2vec: scalable feature learning for networks. Grover, A. and Leskovec, J. SIGKDD’16. Learning convolutional neural networks for graphs. Niepert, M., et al. ICML’16. Inductive representation learning on large graphs. Hamilton, W., et al. NIPS’17. Semi-supervised classification with graph convolutional networks. Kipf, T.N., et al. ICLR’17. Course Lecture: Graph Representation Learning. Leskovec J. 2019. | ||
| W11: Mar 30 - Apr 03 | Health Informatics: digital epidemiology and food computing | Slide 1,2 |
| Guest lecture: Isaac S. Kohane | Estimating county health statistics with twitter. Culotta, A. CHI’14. Normalising medical concepts in social media texts by learning semantic representation. Limsopatham, N., et al. ACL’16. Semantic mapping of natural language input to database entries via CNNs. Korpusik, M., et al. ICASSP’17. Toward large-scale and multi-facet analysis of first-person alcohol drinking. Amiri, H., et al. AMIA’18 Machine learning in medicine. Rajkomar, A., et al. NEJM’19. | |
| W12: Apr 06 - Apr 10 | Network Analysis of Language: redundancy, specificity, churn, and polarization | Slide 1,2,3 |
| Linguistic redundancy in twitter. Zanzotto, F.M., et al. EMNLP’11. Global connectivity and multilinguals in the Twitter network. Hale, S.A. SIGCHI’14. Short text representation for detecting churn in microblogs. Amiri, H. and Daumé III, H. AAAI’16. Analyzing polarization in social media. Demszky, D., et al. NAACL’19. Predicting and analyzing language specificity in social media posts. Gao, Yifan, et al. AAAI’19. | ||
| W13: Apr 13 - Apr 17 | Search and Factuality: microblog search, moment retrieval, and fact checking | Slide 1,2,3 |
| Guest Lecture: Mitra Mohtarami | Overview of the trec-2014 microblog track. Lin J., et al. TREC’14. Attentive moment retrieval in videos. Liu, M., et al. SIGIR’18. Automatic stance detection using end-to-end memory networks. Mohtarami, M., et al. NAACL’18. Integrating video retrieval and moment detection in a unified corpus for video QA. Luo H., et al. InterSpeech’19. Contrastive language adaptation for cross-lingual stance detection. Mohtarami, M., et al. EMNLP’19. | |
| W14: Apr 20 - Apr 24 | Topic Detection and Tracking: early prediction | Slide 1,2 |
| Emerging topic detection for organizations from microblogs. Chen, Y., et al. SIGIR’13. Learning evolving and emerging topics in social media. Saha, A. et al. WSDM’12 Lecture: Trend detection in Twitter social data. Tsioutsiouliklis, K. 2012. | ||
| W15: Apr 27 - May 01 | Project presentations | |
| Project presentations. |