If you are having trouble accessing any of these papers online,
this webpage from UW Library may be of assistance:
https://uwaterloo.ca/library/services/get-access-anywhere

Papers on Trust

1. J. Templeton and T. Tran;
A cluster-based integrated trust establishment model for intelligent agents;
Proceedings of Trust workshop at AAMAS 2021; 12 pages.
https://ceur-ws.org/Vol-3022/paper8.pdf

2. S. Adhikari and P. Gmytrasiewicz;
Telling friend from foe - towards a Bayesian approach to sincerity and
deception; Proc. of Trust workshop at AAMAS 2021; 15 pages.
https://ceur-ws.org/Vol-3022/paper7.pdf

3. S. Sebo et al.
The ripple effects of vulnerability: the effects of a robot's vulnerable
behavior on trust in human-robot teams; Proceedings of HRI 2018; 9 pages.
https://scazlab.yale.edu/sites/default/files/files/strohkorb%20sebo_HRI18.pdf

4. J. Bentahar et al.
Quantitative group trust: a two-stage verification approach;
Proceedings of AAMAS 2022; 9 pages.
https://dl.acm.org/doi/abs/10.5555/3535850.3535863

5. L. Zeynalvand et al.;
COBRA: Context-aware Bernoulli neural networks for reputation assessment;
Proceedings of AAAI 2020; 9 pages.
https://arxiv.org/abs/1912.08446

6. H. Fang et al;
A trust model stemmed from the diffusion theory for opinion evaluation;
Proceedings of AAMS 2013; 8 pages.
https://www.ifaamas.org/Proceedings/aamas2013/docs/p805.pdf

7. G. Guo et al;
Trust SVD: Collaborative filtering with both the explicit
and implicit influence of user trust and of item ratings;
Proceedings of AAAI 2015; 7 pages; 2015.
https://personal.ntu.edu.sg/zhangj/paper/aaai15-guo.pdf

8. Z. Zahedi et al;
Trust-aware planning: modeling trust evolution in longitudinal
human-robot interaction;
Proceedings of XAIP workshop at ICAPS 2021; 8 pages; 2021.
https://arxiv.org/abs/2105.01220

9. K. Yu et al;
User trust dynamics: an investigation driven by differences
in system performance;
Proceedings of IUI 2017; 10 pagest; 2017.
https://shlomo-berkovsky.github.io/files/pdf/IUI17b.pdf

10. G. Venkatadari et al.;
Strengthening weak identities through inter-domain trust transfer;
Proceedings of IW3C2 2016; 2016; 11 pages.
https://www.lix.polytechnique.fr/~goga/papers/transfer_trust_WWW2016.pdf

11. D. Di Scala and P. Yolum;
PCCART: Reinforcing trust in multiuser privacy agreement systems;
Proceedings of AAMAS 2023; 9 pages; 2023.
https://arxiv.org/pdf/2302.13650v1.pdf

12. P. Gutierrez et al;
Trust-based community assessment;
Pattern Recognition Letters; 2015; 11 pages.
https://digital.csic.es/bitstream/10261/130828/1/PRL(67)_49-58.pdf

13. A. Coates et al;
Simulating the impact of personality on fake news;
Proceedings of Trust workshop at AAMAS 2021; 12 pages; 2021.
https://ceur-ws.org/Vol-3022/paper6.pdf

14. D. Calvaresi et al;
Reputation management in multi-agent systems using permissioned
blockchain technology;
Proceedings of 2018 Conference on Web Intelligence; 8 pages; 2018/
https://www.researchgate.net/publication/328692648_Reputation_Management_in_Mult
i-Agent_Systems_Using_Permissioned_Blockchain_Technology/link/5bdc43034585150b2b
993e96/download

15. P. Zhou et al;
A priori trust inference with context-aware stereotypical deep learning;
Knowledge-Based Systems, Vol. 88, Iss. C, pp.97-106; 2015.
https://dl.acm.org/doi/abs/10.1016/j.knosys.2015.08.003
https://personal.ntu.edu.sg/zhangj/paper/kbs-zhou.pdf

16. S. Jiang et al.;
An evolutionary model for constructing robust trust networks;
Proceedings of AAMAS 2013; 2013.
https://www.ifaamas.org/Proceedings/aamas2013/docs/p813.pdf

17. M. Cheng et al;
A general trust framework for multi-agent systems;
Proceedings of AAMAS 2021; 2021.
https://www.ifaamas.org/Proceedings/aamas2021/pdfs/p332.pdf

18. Y. Zhao et al.;
Seeing isn't believing: towards more robust adversarial attack
against real world object detectors;
Proceedings of ACM SIGSAC conference 2019; 2019; 16 pages.
https://arxiv.org/pdf/1812.10217.pdf

19. P. Shiwaswamy and D. Garcia-Garcia;
Adversary or friend? An adversarial approach to improving recommender systems;
Proceedings of ACM Rec Sys confefence 2022; 2022; 9 pages.
https://dl.acm.org/doi/pdf/10.1145/3523227.3546784

20.  Y. Zhang et al.;
Effect of confidence and explanation on accuracy and trust
calibration in AI-assisted decision making;
Proceedings of FAT 2020 conference; 11 pages.
https://arxiv.org/pdf/2001.02114.pdf

21.  Y. Che et al.;
Efficient and trustworthy social navigation via explicit and
implicit robot-human communication;
IEEE Transactions on Robotics 36 (3), 692-707; 16 pages; 2020.
https://arxiv.org/pdf/1810.11556.pdf

22. Z. Yang et al;
Whom to trust? Elective learning for distributed Gaussian process
regression; Proceedings of AAMAS 2024; 9 pages.
https://arxiv.org/pdf/2402.03014.pdf

23. T. Eghtesad et al.;
Hierarchical multi-agent reinforcement learning for assessing
false-data injection attacks on transportation networks;
Proceedings of AAMAS 2024; 8 pages.
https://arxiv.org/pdf/2312.14625.pdf

24.  M. do Carmo Alves et al.;
It is among us: identifying adversaries in ad-hoc domains
using Q-valued Bayesian estimations;
Proceedings of AAMAS 2024; 9 pages.
https://yelkhatib.github.io/papers/Alves2024amongus.pdf

25. M. Mechergui and S. Sreedharan;
Goal alignment: re-analyzing value alignment problems using human-aware AI;
ALA workshop at AAMAS 2023; 8 pages.
https://arxiv.org/pdf/2302.00813.pdf

26. Z. Yu et al;
KGTrust: evaluating trustworthiness of SIoT via knowledge
enhanced graph neural networks;
Proceedings of ACM TheWeb Conference 2023; 10 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583549

27. N. Scharowski et al.;
Certification labels for trustworthy AI: insights from an
empirical mixed-method study; Proceedings of FAccT 2023; 2023; 13 pages.
https://arxiv.org/pdf/2305.18307.pdf

28. S. Sharma et al.;
REFRESH: Responsible and efficient feature reselection guided by SHAP values;
Proceedings of AIES 2023; 2023; 11 pages.
https://dl.acm.org/doi/abs/10.1145/3600211.3604706

29. S. Beckers et al.;
Quantifying harm; Proceedings of IJCAI 2023; 2023; 9 pages.
https://www.ijcai.org/proceedings/2023/0041.pdf

30. Y. Cui et al.;
Controllable universal fair representation learning;
Proceedings of ACM TheWeb Conference 2023; 11 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583260

31. G. Rjoub et al.;
Explainable trust-aware selection of autonomous vehicles using LIME
for one-shot federated learning;
Proc. of 2023 International Wireless Communications and Mobile Computing; 
https://ieeexplore.ieee.org/document/10182876

32. F. Cordoba et al.;
Analyzing intentional behavior in autonomous agents under uncertainty;
Proceedings of IJCAI 2023; 2023; 10 pages.
https://www.ijcai.org/proceedings/2023/0042.pdf

33. Z. Lu et al.;
Strategic adversarial attacks in AI-assisted decision making to
reduce human trust and reliance; Proceedings of IJCAI 2023; 2023; 9 pages.
https://www.ijcai.org/proceedings/2023/0337.pdf

34. H. Salman et al.;
Raising the cost of malicious AI-powered image editing;
Proceedings of ICML 2023; 2023; 11 pages plus appendices.
https://openreview.net/pdf?id=mSKJS7YbwU

35. A. Khaddaj et al.;
Rethinking backdoor attacks; Proceedings of ICML 2023; 11 pages plus appendix.
https://openreview.net/pdf?id=mSKJS7YbwU

36. W. Cai et al.;
Adaptive sampling strategies to construct equitable training datasets;
Proceedings of FAccT 2022; 2022; 11 pages.
https://dl.acm.org/doi/abs/10.1145/3531146.3533203

37. M. Dreyer et al.;
From hope to safety: unlearning biases of deep models via
gradient penalization in latent space;
Proceedings of AAAI 2024; 2024; 13 pages plus appendices.
https://arxiv.org/pdf/2308.09437.pdf

Papers on Explainability

1. U. Soni et al;
Not all users are the same: providing personalized explanations
for sequential decision making problems;
Proceedings of XAIP workshop at ICAPS 2021; 8 pages; 2021.
https://arxiv.org/pdf/2106.12207.pdf

2. C. Wan et al.;
Explainability's gain is optimality's loss? How explanations
bias decision making;
Proceedings of AI, Ethics and Society (AIES) 2022 conference; 10 pages; 2022.
https://arxiv.org/pdf/2206.08705.pdf

3. E. Sklar and M. Zhar; Explanation through argumentation;
Proceedings of HAI 2018;
https://dl.acm.org/doi/pdf/10.1145/3284432.3284470

4. A. Bell et al; It's just not that simple: An empirical study of
the accuracy-explainability trade-off in machine learning for public
policy; Proceedings of the Fairness, Accountability and Transparency
2022 Conference;
https://dl.acm.org/doi/pdf/10.1145/3531146.3533090

5. M. Persiani and T. Hellstrom;
The mirror agent model: a Bayesian architecture for interpretable agent
behaviour; Proceedings of Extraamas workshop at AAMAS 2022; 14 pages.
https://www.diva-portal.org/smash/get/diva2:1656416/FULLTEXT02

6. V. Ordonez et al;
Integration of local and global features explanation via CIU and
explainable layers for improving global rules generation in ECLAIRE;
Proc. Extraamas workshop at AAMAS 2022; 18 pages.
https://www.researchgate.net/publication/361560732_Integration_of_local_and_global_features_explanation_with_global_rules_extraction_and_generation_tools

7. M. Brandao et al.;
Explainability in multi-agent path/motion planning: user study-driven
taxonomy and requirements; Proceedings of AAAMS 2022; 9 pages.
https://www.martimbrandao.com/papers/Brandao2022-aamas.pdf
https://dl.acm.org/doi/10.5555/3535850.3535871

8. A. Jacq et al;
Lazy-MDPs: towards interpretable reinforcement learning by learning when to
act; Proceedings of AAMAS 2022; 14 pages.
https://arxiv.org/abs/2203.08542

9. P. Qian and V. Unhelkar;
Evaluating the role of interactivity on improving transparency in
autonomous agents; Proceedings of AAMAS 2022; 9 pages.
https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1083.pdf

10. F. Barsotti et al.;
Transparency, detection and imitation in strategic classification;
Proceedings of IJCAI 2022; 9 pages;
https://www.ijcai.org/proceedings/2022/0010.pdf

11. Q. Li et al;
Optimal local explainer aggregration for interpretable prediction;
Proceedings of AAAI 2022; 9 pages.
https://arxiv.org/abs/2003.09466
https://ojs.aaai.org/index.php/AAAI/article/view/21458

12. D. Rajapaksha and C. Bergmeir;
LIMREF: local interpretable model agnostic rule-based explanations
for forecasting, with an application to electricity smart meter data;
Proceedings of AAAI 2022; 9 pages.
https://arxiv.org/abs/2202.07766

13. F. Mosca and J. Such;
ELVIRA: an explainable agent for value and utility-driven nultiuser privacy;
Proceedings of AAMAS 2021; 9 pages.
https://www.ifaamas.org/Proceedings/aamas2021/pdfs/p916.pdf

14. X. Wang et al;
A reinforcement learning framework for explainable recommendation;
Proceedings of IEEE Conference on Data Mining 2018; 10 pages; 2018.
https://www.microsoft.com/en-us/research/uploads/prod/2018/08/main.pdf

15. K. Framling et al;
Comparison of contextual importance and utility with
LIME and Shapley values;
Proceedings of EXTRAAMAS workshop at AAMAS 2021; 15 pages; 2021.
https://dl.acm.org/doi/abs/10.1007/978-3-030-82017-6_3
https://acris.aalto.fi/ws/portalfiles/portal/76979599/SCI_Fr_mling_Comparison_of
_Contextual_Importance_and_Utility_with_LIME_and_Shapley_Values.pdf

16. X. Dai et al;
Counterfactual explanations for prediction and diagnosis in XAI;
Proceedings of AIES 2022; 2022; 12 pages.
https://dl.acm.org/doi/pdf/10.1145/3514094.3534144

17. V. Contreras et al.;
Explanation generation via decompositional rules extraction
for head and neck cancer classification;
Proceedings of Extraamas workshop at AAMAS 2023; p.187-212.
https://link.springer.com/book/10.1007/978-3-031-40878-6

18. J.Zhang et al.;
Model debiasing via gradient-based explanation on representation;
Proceedings of AIES 2023; 11 pages.
https://arxiv.org/pdf/2305.12178.pdf

19. A. Artelt and B. Hammer;
Explain it in the same way: model-agnostic group fairness of
counterfactual explanations; Proceedings of IJCAI 2023 XAI workshop; 13 pages.
https://arxiv.org/pdf/2211.14858.pdf

20. W. Zhang et al.; 
Neuro-symbolic interpretable collaborative filtering for attribute-based
recommendation; Proceedings of WWW 2022; 10 pages.
https://dl.acm.org/doi/10.1145/3485447.3512042
https://web.archive.org/web/20220504023001id_/https://dl.acm.org/doi/pdf/10.1145/3485447.3512042

21. R. Fong and A. Vedaldi;
Interpretable explanations of black boxes by meaningful perturbation;
Proceedings of ICCV 2017; 9 pages.
https://arxiv.org/pdf/1704.03296.pdf

22. P. Madumal et al.;
Explainable reinforcement learning through a causal lens;
Proceedings of AAAI 2020; 8 pages.
https://arxiv.org/pdf/1905.10958.pdf

23. C-K. Yeh et al.;
On the (in)fidelity and sensitivity of explanations;
Proceedings of Neurips 2019; 12 pages plus appendices.
https://arxiv.org/pdf/1901.09392.pdf

24. M. Dombrowski et al.;
Trade-offs in fine-tuned diffusion models between accuracy and
interpretability; Proceedings of AAAI 2024; 12 pages.
https://arxiv.org/pdf/2303.17908.pdf

25. K. Boggess, S. Kraus and L. Feng;
Explainable multi-agent reinforcement learning for temporal queries;
Proceedings of IJCAI 2023; 9 pages.
https://arxiv.org/pdf/2305.10378.pdf

26.  Y. Du et al.;
Towards explainable collaborative filtering with taste clusters learning;
Proceedings of ACM TheWeb Conference 2023; 11 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583303

27. S. Zhang et al.;
PaGE-Link: path-based graph neural network explanation for
heterogeous link prediction;
Proceedings of ACM TheWeb Conference 2023; 10 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583511

28. J. Shen et al.;
"Why is this misleading?": Detecting news headline hallucinations
with explanations; Proceedings of ACM TheWeb Conference 2023; 11 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583375

29.  S. Chandramouli et al.;
Interactive personalization of classifiers for explainability
using multi-objective Bayesian optimization;
Proceedings of UMAP 2023; 12 pages.
https://dl.acm.org/doi/pdf/10.1145/3565472.3592956

30. A. Boggust et al.;
Saliency cards: a framework to characterize and compare saliency methods;
Proceedings of FaCCT Conference 2023; 12 pages.
https://dl.acm.org/doi/pdf/10.1145/3593013.3593997

31. Z. Lin et al.;
Contrastive explanations for reinforcement learning via embedded
self predictions;
Proceedings of ICLR 2021; 12 pages plus appendices.
https://arxiv.org/pdf/2010.05180.pdf

32. S. Geng et al.;
Improving personalized explanation generation through visualization;
Proceedings of ACL 2022; 2022; 12 pages.
https://aclanthology.org/2022.acl-long.20.pdf

33. M. Hee et al.;
Decoding the underlying meaning of multimodal hateful memes;
Proceedings of IJCAI 2023; 2023; 9 pages.
https://www.ijcai.org/proceedings/2023/0665.pdf

34. H. Wang et al.;
Evaluating GPT-3 generated explanations for hateful content moderation;
Proceedings of IJCAI 2023; 2023; 9 pages.
https://www.ijcai.org/proceedings/2023/0694.pdf

35. J. Gajcin and I. Dusparic;
RACCER: Towards reachable and certain counterfactual explanstions
for reinforcement learning; Proceedings of AAMAS 2024; 2024; 10 pages.
https://arxiv.org/pdf/2303.04475.pdf

36. F. Leofante and N. Potyka;
Promoting counterfactual robustness through diversity;
Proceedings of AAAI 2024; 11 pages.
https://arxiv.org/pdf/2312.06564.pdf

37. Y. Amitai et al.;
Explaining reinforcement learning agents through counterfactual
action outcomes; Proceedings of AAAI 2024; 12 pages.
https://arxiv.org/pdf/2312.11118.pdf

38. S. Sreedharan et al;
Bridging the gap: Providing post-hoc symbolic explanations for sequential
decision-making problems with inscrutable representations;
Proceedings of ICLR 2022; 12 pages plus appendices.
https://arxiv.org/pdf/2002.01080.pdf

39. M. Millecamp et al.;
To explain or not to explain: the effects of personal characteristics
when explaining music recommendations; Proceedings of IUI 2019; 2019; 11 pages.
https://www.cs.ubc.ca/~conati/522/532b-2019/papers/MillecampIUI2019.pdf

40. C. Burger et al.;
"Are your Explanations Reliable?" Investigating the stability of LIME
in explaining text classfiers by marrying XAIand adversarial attack;
Proceedings of EMNLP 2023; 2023; 14 pages.
https://aclanthology.org/2023.emnlp-main.792.pdf

41. N. Topin and M. Veloso;
Generation of policy-level explanations for reinforcement learning;
Proceedings of AAAI 2019; 2019; 8 pages.
https://ojs.aaai.org/index.php/AAAI/article/view/4097

42. V. Petsiuk et al.;
Black-box explanation of object detectors via saliency maps;
Proceedings of CVPR 2021; 2021; 15 pages.
https://arxiv.org/pdf/2006.03204.pdf

Papers on Social Networks

1. P. Berenbrink et al.;
Asynchronous opinion dynamics in social networks;
Proceedings of AAMAS 2022; 9 pages.
https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p109.pdf

2. M. Irfan et al;
Cascades and overexposure in social networks: the budgeted case;
Proceedings of AAMAS 2022; 9 pages.
https://ifaamas.org/Proceedings/aamas2022/pdfs/p642.pdf

3. D. Zhang and A. Carver;
Segregation in social networks of heterogeneous agents acting
under incomplete information; Proceedings of AAMAS 2022; 9 pages.
https://www.ifaamas.org/Proceedings/aamas2022/pdfs/p1455.pdf

4. A. Abouzeid et al.;
Socially fair mitigation of misinformation on social networks via
constraint stochastic optimization;
Proceedings of AAAI 2022; 9 pages.
https://arxiv.org/abs/2203.12537

5. T. Le et al;
Socialbots on fire: modeling adversarial behaviors of socialbots
via multi-agent hierarchical reinforcement learning;
Proceedings of ACM The Web conference 2022; 10 pages; 2022.
https://arxiv.org/pdf/2110.10655.pdf

6. C. Schweimer et al;
Generating simple directed social network graphs for
information spreading;
Proceedings of ACM The Web conference 2022; 11 pages; 2022.
https://arxiv.org/pdf/2205.02485.pdf

7. W. Xu et al;
Evidence-aware fake news detection with graph neural networks;
Proceedings of ACM the Web conference 2022; 10 pages; 2022.
https://arxiv.org/pdf/2201.06885.pdf

8. M. Sun et al;
DDGCN: Dual dynamic graph convolutional networks for
rumour detection on social media;
Proceedings of AAAI 2022; 9 pages; 2022.
https://ojs.aaai.org/index.php/AAAI/article/view/20385

9. Z-Y Dou et al;
Harnessing social media to identify homeless youth at risk of
substance use;
Proceedings of AAAI 2021; 2021.
https://ojs.aaai.org/index.php/AAAI/article/view/17732

10. Q. Kong et al;
Slipping to the extreme: a mixed method to explain how extreme
opinions infiltrate online discussions;
Proceedings of ICSWM 2022 conference; 12 pages; 2022.
https://ojs.aaai.org/index.php/ICWSM/article/view/19312/19084

11. A. Tsang et al;
Group-fairness in influence maximization;
Proceedings of IJCAI 2019; 9 pages; 2019.
https://people.scs.carleton.ca/~alantsang/files/groupfair19.pdf

12. H. Kamarthi et al;
Influence maximization in unknown social networks: learning
policies for effective graph sampling;
Proceedings of AAMAS 2020; 10 pages; 2020.
https://arxiv.org/pdf/1907.11625.pdf

13. A. Mashhadi et al;
No walk in the park: the viability and fairness of social media analysis
for parks and recreation;
Proceedings of ICWSM 2021; 12 pages; 2021.
https://ojs.aaai.org/index.php/ICWSM/article/view/18071/17874

14. K. Solovev and N. Prollochs;
Moral emotions shape the virality of Covid-19 misinformation
on social media;
Proceedings of ACM the Web 2022; 12 pages; 2022.
https://dl.acm.org/doi/pdf/10.1145/3485447.3512266

15. L. Tian et al.;
DUCK: Rumour detection on social media by modelling
user and comment propagation networks;
Proceedings of NAACL 2022; 11pages; 2022.
https://aclanthology.org/2022.naacl-main.364.pdf

16. P. Sobhani et al.;
Exploring deep neural networks for multitarget stance detection;
Computational Intelligence 2019; 16 pages.
https://onlinelibrary-wiley-com.proxy.lib.uwaterloo.ca/doi/pdf/10.1111/coin.12189

17. S. Aref and Z. Neal;
Detecting coalitions by optimally partitioning signed networks
of political collaboration;
Nature Scientific Reports 10, Article number 1506; 2020; 10 pages.
https://www.nature.com/articles/s41598-020-58471-z

18. R. Baten et al.;
Predicting future location categories of users in a large social platform;
Proceedings of ICSWM 2023; 12 pages.
https://ojs.aaai.org/index.php/ICWSM/article/view/22125/21904

19. X. Wang, O. Varol and T. Eliassi-Rad;
Information access equality on generative models of complex networks;
Applied Network Science 7, Article 54; 2022; 20 pages.
https://appliednetsci.springeropen.com/articles/10.1007/s41109-022-00494-8

20. C. Hays et al.;
Simplistic collection and labelling practices limit the utility of
benchmark datasets for Twitter bot detection;
Proceedings of ACM TheWeb Conference 2023; 10 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583214

21. Y. Dong et al.;
A generalized deep Markov random fields framework for fake news detection;
Proceedings of IJCAI 2023; 2023; 8 pages.
https://www.ijcai.org/proceedings/2023/0529.pdf

22.  U. Chitra and C. Musco;
Analyzing the impact of filter bubbles on social network polarization;
Proceedings of ACM Web Search and Data Mining Conference 2020; 9 pages.
https://www.researchgate.net/profile/Uthsav-Chitra/publication/338758106_An
alyzing_the_Impact_of_Filter_Bubbles_on_Social_Network_Polarization/links/5
eda6c4692851c9c5e81af45/Analyzing-the-Impact-of-Filter-Bubbles-on-Social-Ne
twork-Polarization.pdf

23. H. Huang et al.; Multi-aspect diffusion network inference;
Proceedings of ACM TheWeb Conference 2023; 9 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583228

24.  X. Wu et al;
ConsRec: learning consensus behind interactions for group recommendation;
Proceedings of ACM TheWeb Conference 2023; 11 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583277

25.  C. Gao et al.;
Pairwise-interactions-based Bayesian inference of network structure
from information cascades;
Proceedings of ACM TheWeb Conference 2023; 11 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583231

26.  Z. Wang et al.;
Lightweight source localization for large-scale social networks;
Proceedings of ACM TheWeb Conference 2023; 9 pages;
https://dl.acm.org/doi/pdf/10.1145/3543507.3583299

27.  X. Song et al.;
xGCN: an extreme graph convolutional network for large-scale
social link prediction;
Proceedings of ACM TheWeb Conference 2023; 11 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583340

28. A. Zehmakan et al.;
Why rumors spread fast in social networks and how to stop it;
Proceedings of IJCAI 2023; 2023; 9 pages.
https://www.ijcai.org/proceedings/2023/0027.pdf

29. X. Lu et al.;
Continuous-time graph learning for cascade popularity prediction;
Proceedings of IJCAI 2023; 2023; 9 pages.
https://www.ijcai.org/proceedings/2023/0247.pdf

30. Z. Yu et al.;
Commonsense knowledge enhanced sentiment dependency graph
for sarcasm detection; Proceedings of IJCAI 2023; 2023; 9 pages.
https://www.ijcai.org/proceedings/2023/0269.pdf

31. Y. Yuan et al.;
Mental health coping stories on social media: a causal-inference
study of Papageno effect;
Proceedings of ACM TheWeb Conference 2023; 9 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583350

32.  B. He et al.;
Reinforcement learning-based counter-misinformation response
generation: a case study of Covid-19 vaccine misinformation;
Proceedings of ACM TheWeb Conference 2023; 12 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583388

33.  Y. Wang et al.;
Label information enhanced fraud detection against low homophily in graphs;
Proceedings of ACMTheWeb Conference 2023; 11 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583373

34. G. Zhang et al.;
An attentional multi-scale co-evolving model for dynamic link prediction;
Proceedings of ACMTheWeb Conference 2023; 9 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583396

35. S. Diao et al.;
Hashtag-guided low-resource tweet classification;
Proceedings of ACM TheWeb Conference 2023; 12 pages.
https://dl.acm.org/doi/pdf/10.1145/3543507.3583194

36.  Y. Luo et al.;
Improving (dis)agreement detection with inductive social relation
information from comment-reply interactions;
Proceedings of ACM TheWeb Conference 2023; 10 pages.
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