Megha Khosla

TMLR Journal 2025 Journal Article

Disentangled and Self-Explainable Node Representation Learning

• Simone Piaggesi
• André Panisson
• Megha Khosla

Node embeddings are low-dimensional vectors that capture node properties, typically learned through unsupervised structural similarity objectives or supervised tasks. While recent efforts have focused on post-hoc explanations for graph models, intrinsic interpretability in unsupervised node embeddings remains largely underexplored. To bridge this gap, we introduce DiSeNE (Disentangled and Self-Explainable Node Embedding), a framework that learns self-explainable node representations in an unsupervised fashion. By leveraging disentangled representation learning, DiSeNE ensures that each embedding dimension corresponds to a distinct topological substructure of the graph, thus offering clear, dimension-wise interpretability. We introduce new objective functions grounded in principled desiderata, jointly optimizing for structural fidelity, disentanglement, and human interpretability. Additionally, we propose several new metrics to evaluate representation quality and human interpretability. Extensive experiments on multiple benchmark datasets demonstrate that DiSeNE not only preserves the underlying graph structure but also provides transparent, human-understandable explanations for each embedding dimension.

TMLR Journal 2024 Journal Article

AGALE: A Graph-Aware Continual Learning Evaluation Framework

• Tianqi Zhao
• Alan Hanjalic
• Megha Khosla

In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly in the realm of euclidean data. To foster fair evaluation and recognize challenges in CL settings, several evaluation frameworks have been proposed, focusing mainly on the single- and multi-label classification task on euclidean data. However, these evaluation frameworks are not trivially applicable when the input data is graph-structured, as they do not consider the topological structure inherent in graphs. Existing continual graph learning (CGL) evaluation frameworks have predominantly focussed on single-label scenarios in the node classification (NC) task. This focus has overlooked the complexities of multi-label scenarios, where nodes may exhibit affiliations with multiple labels, simultaneously participating in multiple tasks. We develop a graph-aware evaluation (AGALE) framework that accommodates both single-labeled and multi-labeled nodes, addressing the limitations of previous evaluation frameworks. In particular, we define new incremental settings and devise data partitioning algorithms tailored to CGL datasets. We perform extensive experiments comparing methods from the domains of continual learning, continual graph learning, and dynamic graph learning (DGL). We theoretically analyze \agale and provide new insights about the role of homophily in the performance of compared methods. We release our framework at https://github.com/Tianqi-py/AGALE.

TMLR Journal 2023 Journal Article

Multi-label Node Classification On Graph-Structured Data

• Tianqi Zhao
• Thi Ngan Dong
• Alan Hanjalic
• Megha Khosla

Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. While these improvements have been largely demonstrated in a multi-class classification scenario, a more general and realistic scenario in which each node could have multiple labels has so far received little attention. The first challenge in conducting focused studies on multi-label node classification is the limited number of publicly available multi-label graph datasets. Therefore, as our first contribution, we collect and release three real-world biological datasets and develop a multi-label graph generator to generate datasets with tunable properties. While high label similarity (high homophily) is usually attributed to the success of GNNs, we argue that a multi-label scenario does not follow the usual semantics of homophily and heterophily so far defined for a multi-class scenario. As our second contribution, we define homophily and Cross-Class Neighborhood Similarity for the multi-label scenario and provide a thorough analyses of the collected $9$ multi-label datasets. Finally, we perform a large-scale comparative study with $8$ methods and $9$ datasets and analyse the performances of the methods to assess the progress made by current state of the art in the multi-label node classification scenario. We release our benchmark at https://github.com/Tianqi-py/MLGNC.

TMLR Journal 2023 Journal Article

Releasing Graph Neural Networks with Differential Privacy Guarantees

• Iyiola Emmanuel Olatunji
• Thorben Funke
• Megha Khosla

With the increasing popularity of graph neural networks (GNNs) in several sensitive applications like healthcare and medicine, concerns have been raised over the privacy aspects of trained GNNs. More notably, GNNs are vulnerable to privacy attacks, such as membership inference attacks, even if only black-box access to the trained model is granted. We propose PRIVGNN, a privacy-preserving framework for releasing GNN models in a centralized setting. Assuming an access to a public unlabeled graph, PRIVGNN provides a framework to release GNN models trained explicitly on public data along with knowledge obtained from the private data in a privacy preserving manner. PRIVGNN combines the knowledge-distillation framework with the two noise mechanisms, random subsampling, and noisy labeling, to ensure rigorous privacy guarantees. We theoretically analyze our approach in the Rènyi differential privacy framework. Besides, we show the solid experimental performance of our method compared to several baselines adapted for graph-structured data. Our code is available at https://github.com/iyempissy/privGnn.

SODA Conference 2011 Conference Paper

The Multiple-Orientability Thresholds for Random Hypergraphs

• Nikolaos Fountoulakis
• Megha Khosla
• Konstantinos Panagiotou