A Cognac shot to forget bad memories: Corrective Unlearning in GNNs

Graph Neural Networks (GNNs) are increasingly being used for a variety of ML applications on graph data. As graph data does not follow the independently and identically distributed (i.i.d) assumption, adversarial manipulations or incorrect data can propagate to other data points through message passing, deteriorating the model's performance. To allow model developers to remove the adverse effects of manipulated entities from a trained GNN, we study the recently formulated problem of Corrective Unlearning. We find that current graph unlearning methods fail to unlearn the effect of manipulations even when the whole manipulated set is known. We introduce a new graph unlearning method, Cognac, which can unlearn the effect of the manipulation set even when only 5% of it is identified. It recovers most of the performance of a strong oracle with fully corrected training data, even beating retraining from scratch without the deletion set while being 8x more efficient. We hope our work guides GNN developers in fixing harmful effects due to issues in real-world data post-training.

Ensemble of Task-Specific Language Models for Brain Encoding

Language models have been shown to be rich enough to encode fMRI activations of certain Regions of Interest in our Brains. Previous works have explored transfer learning from representations learned for popular natural language processing tasks for predicting brain responses. In our work, we improve the performance of such encoders by creating an ensemble model out of 10 popular Language Models (2 syntactic and 8 semantic). We beat the current baselines by 10% on average across all ROIs through our ensembling methods.

CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on Graphs

Unsupervised representation learning on (large) graphs has received significant attention in the research community due to the compactness and richness of the learned embeddings and the abundance of unlabelled graph data. When deployed, these node representations must be generated with appropriate fairness constraints to minimize bias induced by them on downstream tasks. Consequently, group and individual fairness notions for graph learning algorithms have been investigated for specific downstream tasks. One major limitation of these fairness notions is that they do not consider the connectivity patterns in the graph leading to varied node influence (or centrality power). In this paper, we design a centrality-aware fairness framework for inductive graph representation learning algorithms. We propose CAFIN (Centrality Aware Fairness inducing IN-processing), an in-processing technique that leverages graph structure to improve GraphSAGE's representations - a popular framework in the unsupervised inductive setting. We demonstrate the efficacy of CAFIN in the inductive setting on two popular downstream tasks - Link prediction and Node Classification. Empirically, they consistently minimize the disparity in fairness between groups across datasets (varying from 18 to 80% reduction in imparity, a measure of group fairness) from different domains while incurring only a minimal performance cost.