no code implementations • 16 Apr 2024 • Bin Liu, Siqi Wu, Jin Wang, Xin Deng, Ao Zhou
Specifically, HiGraphDTI learns hierarchical drug representations from triple-level molecular graphs to thoroughly exploit chemical information embedded in atoms, motifs, and molecules.
no code implementations • 12 Sep 2023 • Angela Schöpke-Gonzalez, Siqi Wu, Sagar Kumar, Paul J. Resnick, Libby Hemphill
In designing instructions for annotation tasks to generate training data for these algorithms, researchers often treat the harm concepts that we train algorithms to detect - 'hateful', 'offensive', 'toxic', 'racist', 'sexist', etc.
no code implementations • 3 Feb 2021 • Minjeong Shin, Alasdair Tran, Siqi Wu, Alexander Mathews, Rong Wang, Georgiana Lyall, Lexing Xie
The collective attention on online items such as web pages, search terms, and videos reflects trends that are of social, cultural, and economic interest.
1 code implementation • 21 Mar 2020 • Siqi Wu, Marian-Andrei Rizoiu, Lexing Xie
This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades).
1 code implementation • 20 Aug 2019 • Siqi Wu, Marian-Andrei Rizoiu, Lexing Xie
In this paper, we first construct the Vevo network -- a YouTube video network with 60, 740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics.
no code implementations • 22 Feb 2019 • Yu Wang, Siqi Wu, Bin Yu
First, we obtain a necessary and sufficient norm condition for the reference dictionary $D^*$ to be a sharp local minimum of the expected $\ell_1$ objective function.
1 code implementation • 8 Sep 2017 • Siqi Wu, Marian-Andrei Rizoiu, Lexing Xie
The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance.
Social and Information Networks Human-Computer Interaction
no code implementations • 17 May 2015 • Siqi Wu, Bin Yu
Moreover, our local identifiability results also translate to the finite sample case with high probability provided that the number of signals $N$ scales as $O(K\log K)$.