# Modeling and Mining Multi-Aspect Graphs With Scalable Streaming Tensor Decomposition

arXiv: https://arxiv.org/abs/2210.04404 · HTML: https://mlsanity.com/paper/2210.04404/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 285 references that are in the map.
- First posted on arXiv 10 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Explainable AI for Trees: From Local Explanations to Global Understanding](https://mlsanity.com/paper/1905.04610/) (2019)
- [COPA: Constrained PARAFAC2 for Sparse & Large Datasets](https://mlsanity.com/paper/1803.04572/) (2018)
- [An explainable XGBoost-based approach towards assessing the risk of cardiovascular disease in patients with Type 2 Diabetes Mellitus](https://mlsanity.com/paper/2009.06629/) (2020)
- [A generalized Bayes framework for probabilistic clustering](https://mlsanity.com/paper/2006.05451/) (2020)
- [t-PINE: Tensor-based Predictable and Interpretable Node Embeddings](https://mlsanity.com/paper/1805.01889/) (2018)
- [OCTen: Online Compression-based Tensor Decomposition](https://mlsanity.com/paper/1807.01350/) (2018)
- [Scoup-SMT: Scalable Coupled Sparse Matrix-Tensor Factorization](https://mlsanity.com/paper/1302.7043/) (2013)
- [Higher-Order Block Term Decomposition for Spatially Folded fMRI Data](https://mlsanity.com/paper/1607.05073/) (2016)
- [Some Advances in Role Discovery in Graphs](https://mlsanity.com/paper/1609.02646/) (2016)

## Abstract (arXiv)

Graphs emerge in almost every real-world application domain, ranging from online social networks all the way to health data and movie viewership patterns. Typically, such real-world graphs are big and dynamic, in the sense that they evolve over time. Furthermore, graphs usually contain multi-aspect information i.e. in a social network, we can have the "means of communication" between nodes, such as who messages whom, who calls whom, and who comments on whose timeline and so on. How can we model and mine useful patterns, such as communities of nodes in that graph, from such multi-aspect graphs? How can we identify dynamic patterns in those graphs, and how can we deal with streaming data, when the volume of data to be processed is very large? In order to answer those questions, in this thesis, we propose novel tensor-based methods for mining static and dynamic multi-aspect graphs. In general, a tensor is a higher-order generalization of a matrix that can represent high-dimensional multi-aspect data such as time-evolving networks, collaboration networks, and spatio-temporal data like Electroencephalography (EEG) brain measurements. The thesis is organized in two synergistic thrusts: First, we focus on static multi-aspect graphs, where the goal is to identify coherent communities and patterns between nodes by leveraging the tensor structure in the data. Second, as our graphs evolve dynamically, we focus on handling such streaming updates in the data without having to re-compute the decomposition, but incrementally update the existing results.