# Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs

arXiv: https://arxiv.org/abs/2207.08098 · HTML: https://mlsanity.com/paper/2207.08098/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 60 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 12 of 12)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [GNNExplainer: Generating Explanations for Graph Neural Networks](https://mlsanity.com/paper/1903.03894/) (2019)
- [Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations](https://mlsanity.com/paper/1905.07697/) (2019)
- [AliGraph: A Comprehensive Graph Neural Network Platform](https://mlsanity.com/paper/1902.08730/) (2019)
- [The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasons](https://mlsanity.com/paper/1912.04930/) (2019)
- [Dynamic Network Embedding Survey](https://mlsanity.com/paper/2103.15447/) (2021)
- [XFake: Explainable Fake News Detector with Visualizations](https://mlsanity.com/paper/1907.07757/) (2019)
- [Counterfactual Explanations for Neural Recommenders](https://mlsanity.com/paper/2105.05008/) (2021)
- [Bi-CLKT: Bi-Graph Contrastive Learning based Knowledge Tracing](https://mlsanity.com/paper/2201.09020/) (2022)
- [Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis](https://mlsanity.com/paper/2203.16141/) (2022)
- [Heterogeneous Graph Attention Networks for Early Detection of Rumors on Twitter](https://mlsanity.com/paper/2006.05866/) (2020)
- [Oblique Predictive Clustering Trees](https://mlsanity.com/paper/2007.13617/) (2020)

## Built on by (most cited first, 9 of 9)

- [Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction](https://mlsanity.com/paper/2211.07400/) (2022)
- [Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience](https://mlsanity.com/paper/2405.18040/) (2024)
- [A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era](https://mlsanity.com/paper/2301.09362/) (2023)
- [Heterogeneous Hypergraph Embedding for Recommendation Systems](https://mlsanity.com/paper/2407.03665/) (2024)
- [Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion](https://mlsanity.com/paper/2501.14399/) (2025)
- [Hypergraph Diffusion for High-Order Recommender Systems](https://mlsanity.com/paper/2501.16722/) (2025)
- [Model-Free Counterfactual Subset Selection at Scale](https://mlsanity.com/paper/2502.08326/) (2025)
- [Empowering Contrastive Federated Sequential Recommendation with LLMs](https://mlsanity.com/paper/2602.09306/) (2026)
- [FINER-SQL: Boosting Small Language Models for Text-to-SQL](https://mlsanity.com/paper/2605.03465/) (2026)

## Abstract (arXiv)

The propagation of rumours on social media poses an important threat to societies, so that various techniques for rumour detection have been proposed recently. Yet, existing work focuses on what entities constitute a rumour, but provides little support to understand why the entities have been classified as such. This prevents an effective evaluation of the detected rumours as well as the design of countermeasures. In this work, we argue that explanations for detected rumours may be given in terms of examples of related rumours detected in the past. A diverse set of similar rumours helps users to generalize, i.e., to understand the properties that govern the detection of rumours. Since the spread of rumours in social media is commonly modelled using feature-annotated graphs, we propose a query-by-example approach that, given a rumour graph, extracts the k most similar and diverse subgraphs from past rumours. The challenge is that all of the computations require fast assessment of similarities between graphs. To achieve an efficient and adaptive realization of the approach in a streaming setting, we present a novel graph representation learning technique and report on implementation considerations. Our evaluation experiments show that our approach outperforms baseline techniques in delivering meaningful explanations for various rumour propagation behaviours.