Zobrazeno 1 - 10
of 342
pro vyhledávání: '"Hanghang Tong"'
Publikováno v:
Visual Informatics, Vol 4, Iss 1, Pp 43-57 (2020)
Hierarchical abstraction is a scalable strategy to deal with large networks. Existing visualization methods have allowed to aggregate the network nodes into hierarchies based on the node attributes or network topology, each of which has its own advan
Externí odkaz:
https://doaj.org/article/3f8b1178fb1b4f0e84ac09c41bdd676c
Publikováno v:
Big Data Mining and Analytics, Vol 2, Iss 1, Pp 35-47 (2019)
Learning the representations of nodes in a network can benefit various analysis tasks such as node classification, link prediction, clustering, and anomaly detection. Such a representation learning problem is referred to as network embedding, and it
Externí odkaz:
https://doaj.org/article/cafcb4edf5924a19b5c2bd0e26abe500
Autor:
Hansaim Lim, Aleksandar Poleksic, Yuan Yao, Hanghang Tong, Di He, Luke Zhuang, Patrick Meng, Lei Xie
Publikováno v:
PLoS Computational Biology, Vol 12, Iss 10, p e1005135 (2016)
Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be
Externí odkaz:
https://doaj.org/article/aef9968234e04cfe8f620306ce987b4a
Publikováno v:
ACM Transactions on Information Systems. 41:1-28
Graph learning-based collaborative filtering (GLCF), which is built upon the message-passing mechanism of graph neural networks (GNNs), has received great recent attention and exhibited superior performance in recommender systems. However, although G
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a2e4c111d190cb687950c7e1cdc611e
https://doi.org/10.1145/3572834
https://doi.org/10.1145/3572834
Publikováno v:
ACM Transactions on Information Systems. 41:1-28
Neural graph collaborative filtering has received great recent attention due to its power of encoding the high-order neighborhood via the backbone graph neural networks. However, their robustness against noisy user-item interactions remains largely u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::065d6a9b3238129262b2c1fca2631e2f
https://doi.org/10.1145/3568396
https://doi.org/10.1145/3568396
Publikováno v:
ACM Transactions on Intelligent Systems and Technology. 14:1-26
Analyzing the urban trajectory in cities has become an important topic in data mining. How can we model the human mobility consisting of stay and travel states from the raw trajectory data? How can we infer these mobility states from a single user’
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac3cb5e51763ed535f66a6bd54196385
https://doi.org/10.1145/3563457
https://doi.org/10.1145/3563457
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:5211-5224
Learning node representations in a network has a wide range of applications. Most of the existing work focuses on improving the performance of the learned node representations by designing advanced network embedding models. In contrast to these work,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4fcb1e45b54bc2d647e47d9eba07978f
https://doi.org/10.1109/tkde.2021.3056884
https://doi.org/10.1109/tkde.2021.3056884
Publikováno v:
Knowledge and Information Systems. 65:613-640
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fdc17277901eca6aeea97b274a2b49bf
https://doi.org/10.1007/s10115-022-01768-4
https://doi.org/10.1007/s10115-022-01768-4
Publikováno v:
IEEE/ACM Transactions on Computational Biology and Bioinformatics. 19:2605-2612
Due to the shortage of COVID-19 viral testing kits, radiology is used to complement the screening process. Deep learning methods are promising in automatically detecting COVID-19 disease in chest x-ray images. Most of these works first train a Convol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::69c5d239feaabdf8198fbae8009f611a
https://doi.org/10.1109/tcbb.2021.3066331
https://doi.org/10.1109/tcbb.2021.3066331
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 16:1-29
With the rapid development of text mining, many studies observe that text generally contains a variety of implicit information, and it is important to develop techniques for extracting such information. Named Entity Recognition (NER), the first step
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::03867df3a1552966992f477e2bf6c856
https://doi.org/10.1145/3522593
https://doi.org/10.1145/3522593