# Learning the Influence Graph of a High-Dimensional Markov Process with Memory

arXiv: https://arxiv.org/abs/2406.09338 · HTML: https://mlsanity.com/paper/2406.09338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 31 references that are in the map.
- First posted on arXiv 13 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Learning and Forecasting Opinion Dynamics in Social Networks](https://mlsanity.com/paper/1506.05474/) (2015)
- [Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications](https://mlsanity.com/paper/1705.11107/) (2017)
- [Active Learning Algorithms for Graphical Model Selection](https://mlsanity.com/paper/1602.00354/) (2016)
- [On the Difficulty of Selecting Ising Models with Approximate Recovery](https://mlsanity.com/paper/1602.03647/) (2016)
- [Learning Temporal Dependence from Time-Series Data with Latent Variables](https://mlsanity.com/paper/1608.07636/) (2016)
- [Mixing Times and Structural Inference for Bernoulli Autoregressive Processes](https://mlsanity.com/paper/1612.06061/) (2016)

## Abstract (arXiv)

Motivated by multiple applications in social networks, nervous systems, and financial risk analysis, we consider the problem of learning the underlying (directed) influence graph or causal graph of a high-dimensional multivariate discrete-time Markov process with memory. At any discrete time instant, each observed variable of the multivariate process is a binary string of random length, which is parameterized by an unobservable or hidden [0,1]-valued scalar. The hidden scalars corresponding to the variables evolve according to discrete-time linear stochastic dynamics dictated by the underlying influence graph whose nodes are the variables. We extend an existing algorithm for learning i.i.d. graphical models to this Markovian setting with memory and prove that it can learn the influence graph based on the binary observations using logarithmic (in number of variables or nodes) samples when the degree of the influence graph is bounded. The crucial analytical contribution of this work is the derivation of the sample complexity result by upper and lower bounding the rate of convergence of the observed Markov process with memory to its stationary distribution in terms of the parameters of the influence graph.