257 citations · 1.2k across the 74 of their papers we have counts for
6 papers · 1 filter
Leave Graphs Alone: Addressing Over-Squashing without Rewiring
Domenico Tortorella, Alessio Micheli
Recent works have investigated the role of graph bottlenecks in preventing long-range information propagation in message-passing graph neural networks, causing the so-called `over-…
CAPER: Coarsen, Align, Project, Refine - A General Multilevel Framework for Network Alignment
Jing Zhu, Danai Koutra, Mark Heimann
Network alignment, or the task of finding corresponding nodes in different networks, is an important problem formulation in many application domains. We propose CAPER, a multilevel…
Analyzing Data-Centric Properties for Graph Contrastive Learning
Puja Trivedi, Ekdeep Singh Lubana, Mark Heimann +2
Recent analyses of self-supervised learning (SSL) find the following data-centric properties to be critical for learning good representations: invariance to task-irrelevant semanti…
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety
Puja Trivedi, Danai Koutra, Jayaraman J. Thiagarajan
While directly fine-tuning (FT) large-scale, pretrained models on task-specific data is well-known to induce strong in-distribution task performance, recent works have demonstrated…
Learning node embeddings via summary graphs: a brief theoretical analysis
Houquan Zhou, Shenghua Liu, Danai Koutra +2
Graph representation learning plays an important role in many graph mining applications, but learning embeddings of large-scale graphs remains a problem. Recent works try to improv…
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods
Donald Loveland, Jiong Zhu, Mark Heimann +3
We study the task of node classification for graph neural networks (GNNs) and establish a connection between group fairness, as measured by statistical parity and equal opportunity…