POWN: Prototypical Open-World Node Classification
- URL: http://arxiv.org/abs/2406.09926v1
- Date: Fri, 14 Jun 2024 11:14:01 GMT
- Title: POWN: Prototypical Open-World Node Classification
- Authors: Marcel Hoffmann, Lukas Galke, Ansgar Scherp,
- Abstract summary: We consider the problem of textittrue open-world semi-supervised node classification.
Existing methods detect and reject new classes but fail to distinguish between different new classes.
We introduce a novel end-to-end approach for classification into known classes and new classes based on class prototypes.
- Score: 6.704529554100875
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We consider the problem of \textit{true} open-world semi-supervised node classification, in which nodes in a graph either belong to known or new classes, with the latter not present during training. Existing methods detect and reject new classes but fail to distinguish between different new classes. We adapt existing methods and show they do not solve the problem sufficiently. We introduce a novel end-to-end approach for classification into known classes and new classes based on class prototypes, which we call Prototypical Open-World Learning for Node Classification (POWN). Our method combines graph semi-supervised learning, self-supervised learning, and pseudo-labeling to learn prototype representations of new classes in a zero-shot way. In contrast to existing solutions from the vision domain, POWN does not require data augmentation techniques for node classification. Experiments on benchmark datasets demonstrate the effectiveness of POWN, where it outperforms baselines by up to $20\%$ accuracy on the small and up to $30\%$ on the large datasets. Source code is available at https://github.com/Bobowner/POWN.
Related papers
- Inductive Graph Few-shot Class Incremental Learning [34.19083477893245]
We introduce inductive GFSCIL that continually learns novel classes with newly emerging nodes.
Compared to the transductive GFSCIL, the inductive setting exacerbates catastrophic forgetting due to inaccessible previous data.
We propose a novel method, called Topology-based class Augmentation and Prototype calibration.
arXiv Detail & Related papers (2024-11-11T00:06:20Z) - Open-World Semi-Supervised Learning for Node Classification [53.07866559269709]
Open-world semi-supervised learning (Open-world SSL) for node classification is a practical but under-explored problem in the graph community.
We propose an IMbalance-Aware method named OpenIMA for Open-world semi-supervised node classification.
arXiv Detail & Related papers (2024-03-18T05:12:54Z) - $\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs
with Proxy Unknowns [35.976426549671075]
We propose a novel generative open-set node classification method, i.e. $mathcalG2Pxy$.
It follows a stricter inductive learning setting where no information about unknown classes is available during training and validation.
$mathcalG2Pxy$ achieves superior effectiveness for unknown class detection and known class classification.
arXiv Detail & Related papers (2023-08-10T09:42:20Z) - Class Incremental Learning with Self-Supervised Pre-Training and
Prototype Learning [21.901331484173944]
We analyze the causes of catastrophic forgetting in class incremental learning.
We propose a two-stage learning framework with a fixed encoder and an incrementally updated prototype classifier.
Our method does not rely on preserved samples of old classes, is thus a non-exemplar based CIL method.
arXiv Detail & Related papers (2023-08-04T14:20:42Z) - Contrastive Meta-Learning for Few-shot Node Classification [54.36506013228169]
Few-shot node classification aims to predict labels for nodes on graphs with only limited labeled nodes as references.
We create a novel contrastive meta-learning framework on graphs, named COSMIC, with two key designs.
arXiv Detail & Related papers (2023-06-27T02:22:45Z) - Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class
Incremental Learning [120.53458753007851]
Few-shot class-incremental learning (FSCIL) has been a challenging problem as only a few training samples are accessible for each novel class in the new sessions.
We deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse.
We propose a neural collapse inspired framework for FSCIL. Experiments on the miniImageNet, CUB-200, and CIFAR-100 datasets demonstrate that our proposed framework outperforms the state-of-the-art performances.
arXiv Detail & Related papers (2023-02-06T18:39:40Z) - Geometer: Graph Few-Shot Class-Incremental Learning via Prototype
Representation [50.772432242082914]
Existing graph neural network based methods mainly focus on classifying unlabeled nodes within fixed classes with abundant labeling.
In this paper, we focus on this challenging but practical graph few-shot class-incremental learning (GFSCIL) problem and propose a novel method called Geometer.
Instead of replacing and retraining the fully connected neural network classifer, Geometer predicts the label of a node by finding the nearest class prototype.
arXiv Detail & Related papers (2022-05-27T13:02:07Z) - Zero-shot Node Classification with Decomposed Graph Prototype Network [33.24920910739568]
We study a zero-shot node classification (ZNC) problem which has a two-stage nature.
For the first stage, we give a novel quantitative CSDs evaluation strategy based on estimating the real class relationships.
For the second stage, we propose a novel Decomposed Graph Prototype Network (DGPN) method.
arXiv Detail & Related papers (2021-06-15T10:13:20Z) - Graph Prototypical Networks for Few-shot Learning on Attributed Networks [72.31180045017835]
We propose a graph meta-learning framework -- Graph Prototypical Networks (GPN)
GPN is able to perform textitmeta-learning on an attributed network and derive a highly generalizable model for handling the target classification task.
arXiv Detail & Related papers (2020-06-23T04:13:23Z) - SCAN: Learning to Classify Images without Labels [73.69513783788622]
We advocate a two-step approach where feature learning and clustering are decoupled.
A self-supervised task from representation learning is employed to obtain semantically meaningful features.
We obtain promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime.
arXiv Detail & Related papers (2020-05-25T18:12:33Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.