# Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation

arXiv: https://arxiv.org/abs/1812.01661 · HTML: https://mlsanity.com/paper/1812.01661/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 14 of 14)

- [FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information](https://mlsanity.com/paper/2210.10936/) (2022)
- [Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications](https://mlsanity.com/paper/2110.08760/) (2021)
- [Data Poisoning Attacks to Deep Learning Based Recommender Systems](https://mlsanity.com/paper/2101.02644/) (2021)
- [CLNode: Curriculum Learning for Node Classification](https://mlsanity.com/paper/2206.07258/) (2022)
- [A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems](https://mlsanity.com/paper/2206.04149/) (2022)
- [TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support](https://mlsanity.com/paper/2306.13339/) (2023)
- [Understanding the Security of Deepfake Detection](https://mlsanity.com/paper/2107.02045/) (2021)
- [Backdoor Graph Condensation](https://mlsanity.com/paper/2407.11025/) (2024)
- [Finding MNEMON: Reviving Memories of Node Embeddings](https://mlsanity.com/paper/2204.06963/) (2022)
- [Detect Professional Malicious User with Metric Learning in Recommender Systems](https://mlsanity.com/paper/2205.09673/) (2022)
- [CAT: Can Trust be Predicted with Context-Awareness in Dynamic Heterogeneous Networks?](https://mlsanity.com/paper/2512.11352/) (2025)
- [Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks](https://mlsanity.com/paper/2503.09049/) (2025)
- [CleanBase: Detecting Malicious Documents in RAG Knowledge Databases](https://mlsanity.com/paper/2605.00460/) (2026)
- [LLM4Trust: Exploring the Capabilities of Large Language Models for Trust Evaluation](https://mlsanity.com/paper/2609.33521/) (2026)

## Abstract (arXiv)

Many security and privacy problems can be modeled as a graph classification problem, where nodes in the graph are classified by collective classification simultaneously. State-of-the-art collective classification methods for such graph-based security and privacy analytics follow the following paradigm: assign weights to edges of the graph, iteratively propagate reputation scores of nodes among the weighted graph, and use the final reputation scores to classify nodes in the graph. The key challenge is to assign edge weights such that an edge has a large weight if the two corresponding nodes have the same label, and a small weight otherwise. Although collective classification has been studied and applied for security and privacy problems for more than a decade, how to address this challenge is still an open question. In this work, we propose a novel collective classification framework to address this long-standing challenge. We first formulate learning edge weights as an optimization problem, which quantifies the goals about the final reputation scores that we aim to achieve. However, it is computationally hard to solve the optimization problem because the final reputation scores depend on the edge weights in a very complex way. To address the computational challenge, we propose to jointly learn the edge weights and propagate the reputation scores, which is essentially an approximate solution to the optimization problem. We compare our framework with state-of-the-art methods for graph-based security and privacy analytics using four large-scale real-world datasets from various application scenarios such as Sybil detection in social networks, fake review detection in Yelp, and attribute inference attacks. Our results demonstrate that our framework achieves higher accuracies than state-of-the-art methods with an acceptable computational overhead.