# Online Learning of Independent Cascade Models with Node-level Feedback

arXiv: https://arxiv.org/abs/2109.02519 · HTML: https://mlsanity.com/paper/2109.02519/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 41 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 16 of 16)

- [An Introduction to Matrix Concentration Inequalities](https://mlsanity.com/paper/1501.01571/) (2015)
- [Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms](https://mlsanity.com/paper/1407.8339/) (2014)
- [Cascading Bandits: Learning to Rank in the Cascade Model](https://mlsanity.com/paper/1502.02763/) (2015)
- [MNL-Bandit: A Dynamic Learning Approach to Assortment Selection](https://mlsanity.com/paper/1706.03880/) (2017)
- [Cascading Bandits for Large-Scale Recommendation Problems](https://mlsanity.com/paper/1603.05359/) (2016)
- [Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications](https://mlsanity.com/paper/1703.01610/) (2017)
- [Combinatorial Cascading Bandits](https://mlsanity.com/paper/1507.04208/) (2015)
- [Thompson Sampling for the MNL-Bandit](https://mlsanity.com/paper/1706.00977/) (2017)
- [Multinomial Logit Contextual Bandits: Provable Optimality and Practicality](https://mlsanity.com/paper/2103.13929/) (2021)
- [Online Influence Maximization (Extended Version)](https://mlsanity.com/paper/1506.01188/) (2015)
- [DCM Bandits: Learning to Rank with Multiple Clicks](https://mlsanity.com/paper/1602.03146/) (2016)
- [Online Influence Maximization under Linear Threshold Model](https://mlsanity.com/paper/2011.06378/) (2020)
- [Influence Maximization with Bandits](https://mlsanity.com/paper/1503.00024/) (2015)
- [Robust Influence Maximization](https://mlsanity.com/paper/1601.06551/) (2016)
- [Revenue Maximization and Learning in Products Ranking](https://mlsanity.com/paper/2012.03800/) (2020)
- [Online Learning and Optimization Under a New Linear-Threshold Model with Negative Influence](https://mlsanity.com/paper/1911.03276/) (2019)

## Built on by (most cited first, 1 of 1)

- [Online Influence Maximization: Concept and Algorithm](https://mlsanity.com/paper/2312.00099/) (2023)

## Abstract (arXiv)

We propose a detailed analysis of the online-learning problem for Independent Cascade (IC) models under node-level feedback. These models have widespread applications in modern social networks. Existing works for IC models have only shed light on edge-level feedback models, where the agent knows the explicit outcome of every observed edge. Little is known about node-level feedback models, where only combined outcomes for sets of edges are observed; in other words, the realization of each edge is censored. This censored information, together with the nonlinear form of the aggregated influence probability, make both parameter estimation and algorithm design challenging. We establish the first confidence-region result under this setting. We also develop an online algorithm achieving a cumulative regret of O( √T), matching the theoretical regret bound for IC models with edge-level feedback.