THIS LIST IS CLOSE TO ITS FINAL STATE AS OF SEP 1, 2018

CS886 - Fall 2018 - Paper Presentations

Each student will need to present 1 paper from the
1st Half List (Trust) and 1 paper from the 2nd Half List (Social Networks).

Students specify their preferences as email to
rcohen AT uwaterloo DOT ca
SPECIFYING the PAPER # (and authors) from the list clearly
INDICATING your NAME and your EMAIL
but sent ONLY in the following time windows:

First Half 1030am Monday to 1pm Friday, 1st week of term (Sep 10 to Sep 14)
Second Half 1030am Monday to 1pm Friday, 6th week of term (Oct 15 to Oct 19)

(Students who join the class after the 1st week will choose papers not
yet selected during the 1st week of class)

Please specify your TOP THREE choices, in order.
If your top choices are oversubscribed, you may be assigned a paper
that is not on your list, in order to ensure that each student
covers a different paper.

Students will do a 15 minute presentation of their paper AND will
complete a one-page point-form summary of the paper, to be distributed
in class (make enough copies for everyone) when their presentation begins.
You may use both sides of the one page.

Below are the lists of papers for presentation.

-------------------------------------------------------------------------

First Half List (Trust)

1. R. Kerr and R. Cohen;
Modeling Trust Using Transactional, Numerical Units;
Proceedings of PST 2006, 2006.
http://dl.acm.org/citation.cfm?id=1501460

2. K. Regan, P. Poupart and R. Cohen;
Bayesian Reputation Modeling in E-Marketplaces Sensitive to
Subjectivity, Deception and Change;
Proceedings of AAAI 2006, 2006.
citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.8344

3. C. Burnett, T. Norman and K. Sycara;
Bootstrapping trust evaluations through stereotypes;
Proceedings of AAMAS 2010, 2010.
https://pdfs.semanticscholar.org/5f51/5a5fc37fa1bae5370644226270fb971dad7a.pdf

4. M. Venanzi et al.;
Bayesian modelling of community-based multidimensional trust in
participatory sensing under data sparsity;
Proceedings of IJCAI 2015, 2015.
https://dl.acm.org/citation.cfm?id=2832349

5. G. Liu, Y. Wang and M. Orgun;
Trust Transitivity in Complex Social Networks;
Proceedings of AAAI 2011, 2011.
https://www.aaai.org/ocs/index.php/AAAI/AAAI11/paper/view/3633

6. S. Chen et al;
POMDP-Based Decision Making for Fast Event Handling in VANETs;
Proceedings of AAAI 2018, 2018.
https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16086

7. A. Kalia et al;
A model of trust, moods and emotions in multiagent systems and its
empirical evaluation;
Proceedings of AAMAS 2014 workshop on Trust, 2014.
http://www.dtic.mil/docs/citations/AD1008390

8. D. Pynadath et al.;
A nearest-neighbout approach to recognizing subjective beliefs
in human-robot interaction;
Proceedings of AAAI 2018 workshop on Plan and Activity Recognition, 2018.
people.ict.usc.edu/~nwang/PDF/AAAI18-PAIR-Pynadath.pdf

9. S. Miles and N. Griffiths;
Incorporating mitigating circumstances into reputation assessment;
Procedings of AAMAS 2015 workshop on multiagent foundations of
social computing, 2015.
http://dl.acm.org/citation.cfm?id=3100236

10. X. Liu and A. Datta;
Modeling context aware dynamic trust using hidden Markov model;
Proceedings of AAAI 2012, 2012.
www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/viewFile/4948/5355

11. G. Venkatadri et el.;
Strengthening weak identities through inter-domain trust transfer;
Proceedings of WWW 2016, 2016.
http://dl.acm.org/citation.cfm?id=2883015

12. R. Falcone and C. Castelfranchi;
Transitivity in Trust: A Discussed Property;
Proceedings of WOA 2010, 2010.
ceur-ws.org/Vol-621/paper22.pdf

13. W. Teacy, N. Jennings, A. Rogers and M. Luck;
A Hierarchical Bayesian Trust Model Based on Reputation and Group Behaviour;
6th European Workshop on Multiagent Systems, 2008.
eprints.soton.ac.uk/266836/1/habit.pdf

14. A. Irissappane, F. Oliehoek and J. Zhang;
A POMDP Based Approach to Optimally Select Sellers in Electronic Marketplaces;
Proceedings of AAMAS 2014, 2014.
www.ntu.edu.sg/home/zhangj/paper/aamas14-athirai1.pdf

15. M. Sensoy et al.; Reasoning About Uncertain Information and Conflict
Resolution through Trust Revision;
Proceedings of AAMAS 2013, 2013.
dl.acm.org/citation.cfm?id=2485053

16. S. Sen, A. Ridgway and M. Ripley;
Adaptive Budgeted Bandit Algorithms for Trust Development
in a Supply-Chain;
Proceedings of AAMAS 2015, 2015.
dl.acm.org/citation.cfm?id=2772900

17. V. Raykar et al.;
Supervised Learning from Multiple Experts: Whom to Trust When
Everyone Lies a Bit;
Proceedings of ICML 2009, 2009.
dl.acm.org/citation.cfm?id=1553488

18. C. Burnett, L. Chen, P. Edwards and T. Norman;
TRAAC: Trust and Risk Aware Access Control;
Proceedings of PST 2014, 2014.
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6890962

19. L. Liu et al;
Machine to machine trust in the IoT era;
Proceedings of AAMAS 2016 Trust workshop, 2016.
http://dl.acm.org/citation.cfm?id=3054119

20. A. Josang et al;
Trust network analysis with subjective logic;
Proceedings of 2006 Australiasian Computer Conference, 2006.
https://dl.acm.org/citation.cfm?id=1151710

21. A. Josang, G. Guo, M. Pini,, F. Santini and Y. Xu;
Combining Recommender and Reputation Systems to Produce Better Online Advice;
MDAI 2013, 2013.
www.luckymoon.me/papers/audun2013combining.pdf

22. G. Guo, J. Zhang and N. Yorke-Smith;
TrustSVD: Collaborative Filtering with Both the Explicit and Implicit
Influence of User Trust and of Item Ratings;
Proceedings of AAAI 2015, 2015.
http://dl.acm.org/citation.cfm?id=2887025

23. P. Taylor et al;
Stereotype reputation with limited observability;
Proceedings of AAMAS 2017 workshop on Trust, 2017.
www.dcs.warwick.ac.uk/~nathan/resources/Publications/trust-2017-taylor.pdf

24. V. Gangal et al.;
Trust and Distrust Across Coalitions - Shapley Value Centrality
Measures for Signed Networks;
Proceedings of NIPS 2015 workshop on Social Networks, 2015.
http://stanford.edu/~jugander/NetworksNIPS2015/

25. A. Buendia and D. Boley;
Optimized graph-based trust mechanism using hitting times;
Proceedings of AAMAS 2017 Trust workshop, 2017.
www-users.cs.umn.edu/~boley/publications/papers/trust17.pdf

26. D. Wang et al.;
Is it harmful when advisors only pretend to be honest?;
Proceedings of AAAI 2016, 2016.
http://dl.acm.org/citation.cfm?id=3016257

27. C. Xu and J. Zhang;
Towards collusive fraud detection in online reviews;
Proceedings of ICDM 2015, 2015.
http://dl.acm.org/citation.cfm?id=2920543

28. T. Muller et al.;
The fallacy of endogenous discounting of trust recommendations;
Procedings of AAMAS 2015, 2015.
http://dl.acm.org/citation.cfm?id=2772951

29. R. Falcone et al.;
Trusting information sources through their categories;
Proceedings of PAAMS 2015, 2015.
https://link.springer.com/chapter/10.1007/978-3-319-18944-4_7

30. A. Seth, J. Zhang and R. Cohen;
Bayesian credibility modeling for personalized recommendation
in participatory media;
Proceedings of UMAP 2010, 2010.
www.ntu.edu.sg/home/zhangj/paper/umap10.pdf

31. S. Yu et al;
Adversarial Classification on Social Networks;
Proceedings of AAMAS 2018, 2018.
https://arxiv.org/abs/1801.08159

32. J. O'Donovan and B. Smyth;
Trust in Recommender Systems;
Proceedings of IUI 2005, 2005.
dl.acm.org/ft_gateway.cfm?id=1040870

33. T. DuBois et al;
Predicting trust and distrust in social networks;
Proceedings of PST 2011, 2011.
https://ieeexplore.ieee.org/abstract/document/6113143/

34. A. Vasalou et al;
In praise of forgiveness: ways of repairing trust breakdowns
in one-off online interactions;
Int. J. Human-Computer Studies Vol. 66; 2008.
https://dl.acm.org/citation.cfm?id=1363363.1363417

35. D. Rafailidis and F. Crestani;
Learning to rank with trust and distrust in recommender systems;
Proceedings of Recommender Systems 2017;
https://dl.acm.org/citation.cfm?id=3109879

36. A. Xu and G. Dudek;
Maintaining efficient collaboration with trust-seeking robots;
Proceedings of IROS 2016;
https://www.cim.mcgill.ca/~mrl/pubs/anqixu/iros2016_tactic.pdf

-------------------------------------------------------------------------

Second Half List (Social Networks)
** The former last paper in Trust has been added here September 23.
** It is ideal for students to indicate top FOUR preferences, if possible.

1. S. Tang et al.;
Echo chambers in investment discussion boards;
Proceedings of ICWSM 2017, 2017 (p.240).
http://www.aaai.org/Library/ICWSM/icwsm17contents.php

2. A. Tsang and K. Larson;
The Echo Chamber: strategic voting and homophily in social networks;
Procedings of AAMAS 2016, 2016.
http://dl.acm.org/citation.cfm?id=2936979

3. J. Su et al;
The effect of recommendations on network structure;
Proceedings of WWW 2016, 2016.
http://dl.acm.org/citation.cfm?id=2883040

4. H. Franks, N. Griffiths and S. Singh Anand;
Learning Influence in Complex Social Networks;
Proceedings of AAMAS 2013, 2013.
ial.eecs.ucf.edu/Reading/Papers/p447.pdf

6. M. Rizoiu and L. Xie;
Online popularity under promotion: viral potential, forecasting and
the economics of time;
Proceedings of ICWSM 2017, 2017 (p.182).
http://www.aaai.org/Library/ICWSM/icwsm17contents.php

7. J. Wachs et al.;
Why do men get more attention? Exploring factors behind success
in an online design community;
Proceedings of ICWSM 2017, 2017 (p299).
http://www.aaai.org/Library/ICWSM/icwsm17contents.php

8. O. Varol et al.;
Online human-bot interactions: detection, estimation and characterization;
Proceedings of ICWSM 2017, 2017 (p.280).
http://www.aaai.org/Library/ICWSM/icwsm17contents.php

9. L. Hu et al.;
Activating the breakfast club: modeling influence spread in natural-world
social networks;
Proceedings of AAMAS 2018, 2018.
teamcore.usc.edu/papers/2018/aamas_ajm.pdf

10. G. Stoddard;
Popularity and Quality in Social News Aggregators: A Study of
Reddit and Hacker News;
Proceedings of ICWSM 2015, 2015.
arxiv.org/pdf/1501.07860

11. Y. Tausczik et al.;
Which size matters? Effects of crowd size on solution quality in Big Data
Q&A communities;
Proceedings of ICSWM 2017, 2017 (p.260).
http://www.aaai.org/Library/ICWSM/icwsm17contents.php

12. D. Romero, D. Huttenlocher and J. Kleinberg;
Coordination and Efficiency in Decentralized Collaboration;
Proceedings of ICWSM 2015, 2015.
http://arxiv.org/abs/1503.07431

13. J. Cheng et al.;
How Community Feedback Shapes User Behavior;
Proceedings of ICWSM 2014, 2014.
cs.stanford.edu/people/jure/pubs/disqus-icwsm14.pdf

14. M. Rizoiu et al;
#DebateNight: The role and influence of socialbots on Twitter
during the 1st 2016 U.S. Presidential debate;
Proceedings of ICWSM 2018, 2018.
https://arxiv.org/abs/1802.09808

15. J. Cheng, C. Danescu-Niculescu-Mizil and J. Leskovec;
Antisocial Behavior in Online Discussion Communities;
Proceedings of ICWSM 2015, 2015.
arxiv.org/pdf/1504.00680v1.pdf%20

16. M. Yin et al;
The communication network within the crowd;
Proceedings of WWW 2016, 2016.
http://dl.acm.org/citation.cfm?id=2883036

17. G. Hine et al;
Kek, Cucks, and God Emperor Trump: A measurement study of 4chan's
politically incorrect forum and its effects on the web;
Proceedings of ICWSM 2017, 2017 (p.92).
http://www.aaai.org/Library/ICWSM/icwsm17contents.php

17B. M. Raghavan et al;
Mapping the invocation structure of online political interaction;
Proceedings of WWW 2018, 2018.
https://dl.acm.org/citation.cfm?doid=3178876.3186129

18. S. Volkova and J. Jang;
Misleading or falsification? Inferring deceptive strategies and types
in online news and social media;
Proceedings of WWW 2018 (Journalism, Misinformation, Fact Checking), 2018.
https://dl.acm.org/citation.cfm?id=3188728

19. B. Nushi, O. Alonso, M. Hentschel and V. Kandylas;
CrowdSTAR: A Social Task Routing Framework for Online Communities;
Proceedings of NIPS2014 workshop on Crowdsourcing and Machine Learning, 2014.
arxiv.org/pdf/1407.6714

20. A. Yadav et al;
Please be an influencer? Contigency-aware influence maximization;
Proceedings of AAMAS 2018, 2018.
teamcore.usc.edu/papers/2018/influencer-contingency-aware.pdf

21. A. Das, S. Gollapudi and K. Munagala;
Modeling Opinion Dynamics in Social Networks;
Proceedings of WSDM 2014, 2014.
https://users.cs.duke.edu/~kamesh/opinion_model.pdf

22. E. Newell et al.;
User migration in online social networks: a case study on Reddit during
a period of community unrest;
Proceedings of ICSWM 2016, 2016.
https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/view/13137/12729

23. J. Kang and K. Lerman;
User Effort and Network Structure Mediate Access to Information in Networks;
Proceedings of ICWSM 2015, 2015.
http://arxiv.org/abs/1504.01760

24. A. Chakraborty et al.;
Who makes trends? Understanding demographic biases in crowdsourced
recommendations;
Proceedings of ICWSM 2017, 2017 (p.22).
http://www.aaai.org/Library/ICWSM/icwsm17contents.php

25. J. Cheng et al.;
Predicting reciprocity in social networks;
Proceedings of PASSAT Privacy, Security, Risk and Trust Conference 2011, 2011.
https://www.cs.cornell.edu/home/kleinber/socialcom11-recip.pdf

26. M. Eslami et al.;
Be careful: things can be worse than they appear; Understanding
biased algorithms and users' behavior around them in rating platforms;
Proceedings of ICWSM 2017, 2017.
https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/download/15697/14795

27. M. Kumar et al;
Community interaction and conflict on the web;
Proceedings of WWW 2018, 2018.
https://arxiv.org/pdf/1803.03697

28. D. Romero et al;
Social networks under stress;
Proceedings of WWW 2016, 2016.
http://dl.acm.org/citation.cfm?id=2883063

29. T. Martin et al;
Exploring limits to prediction in complex social systems;
Proceedings of WWW 2016, 2016.
http://dl.acm.org/citation.cfm?id=2883001

30. J. Zhang and P. Yu;
PCT: partial co-alignment of social networks;
Proceedings of WWW 2016, 2016.
http://dl.acm.org/citation.cfm?id=2883038

31. M. ElSherief et al.;
Peer to peer hate: hate speech instigators and their targets;
Proceedings of ICWSM 2018, 2018.
https://arxiv.org/abs/1804.04649

32. A. Stoica et al;
Algorithmic glass ceiling in social networks'
Proceedings of TheWeb 2018, 2018.
https://dl.acm.org/citation.cfm?id=3186140

33. X. Li et al;
FILE: A novel framework for predicting social status in signed networks;
Proceedings of AAAI 2018, 2018.
https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16702

34. T. Hao and L. Huang;
A social interaction activity based time-varying user vectorization
method for online social networks;
Proceedings of IJCAI 2018, 2018.
www.wind23.com/documents/social2018hao_ijcai.pdf

35. A. Fayazi et al;
Uncovering crowdsourced manipulation of online reviews;
Proceedings of SIGIR 2015;
https://dl.acm.org/citation.cfm?id=2767742

36. N. Alipourfard, P. Fennell and K. Lerman;
Can you trust the trend? Discovering Simpson's paradoxes in social data;
Proceedings of WSDM 2018;
https://arxiv.org/abs/1801.04385

37. G. Radanovic and B. Faltings;
Incentives for subjective evaluations with private beliefs;
Proceedings of AAAI 2015;
https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9719