Search Results for author: Siqi Wu

Found 6 papers, 3 papers with code

AttentionFlow: Visualising Influence in Networks of Time Series

no code implementations3 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.

Time Series

Variation across Scales: Measurement Fidelity under Twitter Data Sampling

1 code implementation21 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).

Estimating Attention Flow in Online Video Networks

1 code implementation20 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.

Navigate Recommendation Systems

Unique Sharp Local Minimum in $\ell_1$-minimization Complete Dictionary Learning

no code implementations22 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.

Dictionary Learning

Beyond Views: Measuring and Predicting Engagement in Online Videos

1 code implementation8 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

Local identifiability of $l_1$-minimization dictionary learning: a sufficient and almost necessary condition

no code implementations17 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 $O(K\log K)$.

Dictionary Learning

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