SIAM International Conference on Data Mining (SDM) 2021 Tutorial
Kevin S. Xu (University of Toledo) and James R. Foulds (University of Maryland, Baltimore County)
Saturday, May 1st 3:30-5:30pm Eastern time: happening virtually on vFairs
5/1/2021: Added tutorial slides and code for demos.
4/23/2021: The tutorial is scheduled for Saturday, May 1st from 3:30-5:30pm Eastern time. We will post the tutorial materials and additional information over the next few days.
1/21/2021: Added more detailed 2-page description PDF.
1/8/2021: This tutorial has been re-scheduled for SDM 2021, which is being held virtually.
3/29/2020: This tutorial will no longer be offered at SDM 2020 since it has been canceled! See https://sinews.siam.org/Details-Page/covid19 for more information regarding the status of SIAM conferences for 2020. We hope to offer this tutorial at a later time.
Traditional data mining algorithms for networks typically assume that the network is in the form of a single static graph, which either represents a single time snapshot or an aggregate view over time. However, this is often an oversimplification of the underlying network phenomenon. For example, in the age of social media, many of our social interactions are recorded digitally with timestamps, and the temporal dynamics can yield as much insight as the graph structure. Thus, it has become increasingly clear that network mining algorithms need to go beyond a single graph to include time information. Generative probabilistic models are well-suited for such analyzing such rich network data, as they provide a natural framework for reasoning collectively over structured data.
This tutorial presents recent advances in generative models for dynamic networks, focusing on models that encode network phenomena with latent (i.e. hidden) attributes, which are subsequently recovered from data. We consider dynamic networks in two different forms: discrete-time networks, where the network evolves through a set of snapshots observed at discrete time steps, and continuous-time networks, where the network is observed through relational events at arbitrary, irregularly-spaced timestamps. Our focus will be on continuous-time networks, which differ most from static networks due to the absence of graph snapshots. For such networks, model-based analysis using point process models can reveal significant insights about the frequency, burstiness, and ordering of edges in networks that are not possible to uncover using snapshot-based analyses.
More detailed description (PDF)
There will be two demos in this tutorial:
- Fitting a stochastic block model to a static Facebook wall post network: facebook_sbm_demo.ipynb
- Fitting the Community Hawkes Independent Pairs (CHIP) model to a timestamped Facebook wall post network: facebook_chip_demo.ipynb
These demos are in the form of Jupyter notebooks. To run these demos, first clone this repository. Then clone the CHIP repository into a folder called CHIP-Network-Model with the same parent folder as this one.
We suggest installing a Python 3.x version of Anaconda, which includes all the necessary Python packages used, as well as Jupyter notebook. You will also need to download the Facebook wall post data: see instructions in data_download_instructions.md
Kevin S. Xu (University of Toledo): Personal website
James R. Foulds (Jimmy) (University of Maryland, Baltimore County): Personal website
We thank Makan Arastuie for his contributions to the tutorial materials.