TY - GEN
T1 - TrustLOG
T2 - 31st ACM International Conference on Information and Knowledge Management, CIKM 2022
AU - Kang, Jian
AU - Zhang, Shuaicheng
AU - Li, Bo
AU - He, Jingrui
AU - Pei, Jian
AU - Zhou, Dawei
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/10/17
Y1 - 2022/10/17
N2 - Learning on graphs (LOG) plays a pivotal role in various high-impact application domains. The past decades have developed tremendous theories, algorithms, and open-source systems in answering what/who questions on graphs. However, recent studies reveal that the state-of-the-art techniques for learning on graphs (LOG) are often not trustworthy in practice with respect to several social aspects (e.g., fairness, transparency, security). A natural research question to ask is: how can we make learning algorithms on graphs trustworthy? To answer this question, we propose a paradigm shift, from answering what and who LOG questions to understanding how and why LOG questions. The TrustLOG workshop provides a venue for presenting, discussing, and promoting frontier research on trustworthy learning on graphs. Moreover, TrustLOG will serve as an impulse for the LOG community to identify novel research problems and shed new light on future directions.
AB - Learning on graphs (LOG) plays a pivotal role in various high-impact application domains. The past decades have developed tremendous theories, algorithms, and open-source systems in answering what/who questions on graphs. However, recent studies reveal that the state-of-the-art techniques for learning on graphs (LOG) are often not trustworthy in practice with respect to several social aspects (e.g., fairness, transparency, security). A natural research question to ask is: how can we make learning algorithms on graphs trustworthy? To answer this question, we propose a paradigm shift, from answering what and who LOG questions to understanding how and why LOG questions. The TrustLOG workshop provides a venue for presenting, discussing, and promoting frontier research on trustworthy learning on graphs. Moreover, TrustLOG will serve as an impulse for the LOG community to identify novel research problems and shed new light on future directions.
KW - graph learning
KW - graph mining
KW - trustworthiness
UR - http://www.scopus.com/inward/record.url?scp=85140873042&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140873042&partnerID=8YFLogxK
U2 - 10.1145/3511808.3557497
DO - 10.1145/3511808.3557497
M3 - Conference contribution
AN - SCOPUS:85140873042
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 5169
EP - 5170
BT - CIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 17 October 2022 through 21 October 2022
ER -