Privacy-Preserving Representation Learning for Text-Attributed Networks with Simplicial Complexes

by   Huixin Zhan, et al.

Although recent network representation learning (NRL) works in text-attributed networks demonstrated superior performance for various graph inference tasks, learning network representations could always raise privacy concerns when nodes represent people or human-related variables. Moreover, standard NRLs that leverage structural information from a graph proceed by first encoding pairwise relationships into learned representations and then analysing its properties. This approach is fundamentally misaligned with problems where the relationships involve multiple points, and topological structure must be encoded beyond pairwise interactions. Fortunately, the machinery of topological data analysis (TDA) and, in particular, simplicial neural networks (SNNs) offer a mathematically rigorous framework to learn higher-order interactions between nodes. It is critical to investigate if the representation outputs from SNNs are more vulnerable compared to regular representation outputs from graph neural networks (GNNs) via pairwise interactions. In my dissertation, I will first study learning the representations with text attributes for simplicial complexes (RT4SC) via SNNs. Then, I will conduct research on two potential attacks on the representation outputs from SNNs: (1) membership inference attack, which infers whether a certain node of a graph is inside the training data of the GNN model; and (2) graph reconstruction attacks, which infer the confidential edges of a text-attributed network. Finally, I will study a privacy-preserving deterministic differentially private alternating direction method of multiplier to learn secure representation outputs from SNNs that capture multi-scale relationships and facilitate the passage from local structure to global invariant features on text-attributed networks.


page 1

page 2


When Differential Privacy Meets Graph Neural Networks

Graph Neural Networks have demonstrated superior performance in learning...

Node-Level Differentially Private Graph Neural Networks

Graph Neural Networks (GNNs) are a popular technique for modelling graph...

Motif-based Graph Representation Learning with Application to Chemical Molecules

This work considers the task of representation learning on the attribute...

Topological Relational Learning on Graphs

Graph neural networks (GNNs) have emerged as a powerful tool for graph c...

Privacy-Preserving Graph Neural Network Training and Inference as a Cloud Service

Graphs are widely used to model the complex relationships among entities...

Simplicial Neural Networks

We present simplicial neural networks (SNNs), a generalization of graph ...

PhD dissertation to infer multiple networks from microbial data

The interactions among the constituent members of a microbial community ...

Please sign up or login with your details

Forgot password? Click here to reset