Uncovering the Spatio-Temporal Dynamics of Memes in the Presence of Incomplete Information

Ge, Hancheng; Caverlee, James; Zhang, Nan; Squicciarini, Anna

doi:10.1145/2983323.2983782

Cited by 28 publications

(24 citation statements)

References 26 publications

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“…Illustration of the AirCP Framework. [59] Kashima et al [95]. By combining them with standard decomposition approaches and applying to tensor completion problem, the auxiliary information was shown to improve completion accuracy especially when observations are sparse.…”

Section: Similarity Based Approachesmentioning

confidence: 99%

“…Information di usion refers to a process by which a piece of information or knowledge is spread initiated by a sender and reaches receivers through medium interactions [218]. Ge et al [59] linked the problem of modeling and predicting the spatiotemporal dynamics of online memes with tensor completion. e meme data is constructed as a third-order tensor (meme × locations × time).…”

Section: Social Computingmentioning

confidence: 99%

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Tensor Completion Algorithms in Big Data Analytics

Song

Caverlee

et al. 2019

ACM Trans. Knowl. Discov. Data

Self Cite

173

118

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Tensor completion is a problem of lling the missing or unobserved entries of partially observed tensors. Due to the multidimensional character of tensors in describing complex datasets, tensor completion algorithms and their applications have received wide a ention and achievement in areas like data mining, computer vision, signal processing, and neuroscience. In this survey, we provide a modern overview of recent advances in tensor completion algorithms from the perspective of big data analytics characterized by diverse variety, large volume, and high velocity. We characterize these advances from four perspectives: general tensor completion algorithms, tensor completion with auxiliary information (variety), scalable tensor completion algorithms (volume), and dynamic tensor completion algorithms (velocity). Further, we identify several tensor completion applications on real-world data-driven problems and present some common experimental frameworks popularized in the literature. Our goal is to summarize these popular methods and introduce them to researchers and practitioners for promoting future research and applications. We conclude with a discussion of key challenges and promising research directions in this community for future exploration.

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Section: Similarity Based Approachesmentioning

confidence: 99%

Section: Social Computingmentioning

confidence: 99%

Tensor Completion Algorithms in Big Data Analytics

Song

Caverlee

et al. 2019

ACM Trans. Knowl. Discov. Data

Self Cite

173

118

View full text Add to dashboard Cite

show abstract

“…To run in batch setting, we set K = 1 in Algorithm 1 and run for multiple passes over the data. In this setting, AirCP [8] is the current state-of-the-art algorithm which is also capable of handling side information. We Figure 5: Runtime comparison between TeCPSGD, OLSTEC and SIITA.…”

Section: Batch Settingmentioning

confidence: 99%

“…For instance, movie-genre information in the movie recommendation etc. There has been considerable amount of work in incorporating side information into tensor completion [22,8]. However, the previous works on incorporating side information deal with the static setting.…”

Section: Introductionmentioning

confidence: 99%

Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information

Nimishakavi

Mishra

Gupta

et al. 2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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Low rank tensor completion is a well studied problem and has applications in various fields. However, in many real world applications the data is dynamic, i.e., new data arrives at different time intervals. As a result, the tensors used to represent the data grow in size. Besides the tensors, in many real world scenarios, side information is also available in the form of matrices which also grow in size with time. The problem of predicting missing values in the dynamically growing tensor is called dynamic tensor completion. Most of the previous work in dynamic tensor completion make an assumption that the tensor grows only in one mode. To the best of our Knowledge, there is no previous work which incorporates side information with dynamic tensor completion. We bridge this gap in this paper by proposing a dynamic tensor completion framework called Side Information infused Incremental Tensor Analysis (SIITA), which incorporates side information and works for general incremental tensors. We also show how non-negative constraints can be incorporated with SIITA, which is essential for mining interpretable latent clusters. We carry out extensive experiments on multiple real world datasets to demonstrate the effectiveness of SIITA in various different settings.

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“…Zhang and Aeron (2016) proposed tensor Singular Value Decomposition (t‐SVD), which generalized the matrix SVD to recover a low‐rank tensor under tensor incoherence conditions. Ge, Caverlee, Zhang, and Squicciarini (2016) integrated auxiliary information to facilitate tensor completion (AirCP) with the alternative direction method of multipliers. However, the Gaussian assumption does not always hold in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Exponential family tensor completion with auxiliary information

Yang

Zhang

2020

Stat

Self Cite

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Tensor completion is among the most important tasks in tensor data analysis, which aims to fill the missing entries of a partially observed tensor. In many real applications, non‐Gaussian data such as binary or count data are frequently collected. Thus, it is inappropriate to assume that observations are normally distributed and formulate tensor completion with least squares based approaches. In this article, we develop a new tensor completion method called ExpCP for non‐Gaussian data from the exponential family distributions. Under the framework of penalized likelihood estimation, we assume the tensor of latent natural parameters admits a low‐rank structure and further induce regularization by integrating auxiliary information. Moreover, we propose an efficient algorithm based on the alternating direction method of multipliers to solve the optimization problem. We establish the algorithmic convergence results and demonstrate the efficacy of our proposal with comprehensive simulations and real data analysis on GDELT social events study. The empirical results show that our method offers improved modelling strategies as well as efficient computation.

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Uncovering the Spatio-Temporal Dynamics of Memes in the Presence of Incomplete Information

Cited by 28 publications

References 26 publications

Tensor Completion Algorithms in Big Data Analytics

Tensor Completion Algorithms in Big Data Analytics

Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information

Exponential family tensor completion with auxiliary information

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