Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning
Graph learning plays a pivotal role and has gained significant attention in various
application scenarios, from social network analysis to recommendation systems, for its …
application scenarios, from social network analysis to recommendation systems, for its …
Open-World Semi-Supervised Learning for Node Classification
Open-world semi-supervised learning (Open-world SSL) for node classification, that
classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but …
classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but …
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning
Open-set graph learning is a practical task that aims to classify the known class nodes and
to identify unknown class samples as unknowns. Conventional node classification methods …
to identify unknown class samples as unknowns. Conventional node classification methods …
Open-world structured sequence learning via dense target encoding
Structured sequences are popularly used to describe graph data with time-evolving node
features and edges. A typical real-world scenario of structured sequences is that unknown …
features and edges. A typical real-world scenario of structured sequences is that unknown …
[PDF][PDF] CONC: Complex-noise-resistant Open-set Node Classification with Adaptive Noise Detection
As a popular task in graph learning, node classification seeks to assign labels to nodes,
taking into account both their features and connections. However, an important challenge for …
taking into account both their features and connections. However, an important challenge for …