Graph Prototypical Networks for Few-shot Learning on Attributed Networks

Ding, Kaize; Wang, Jianling; Li, Jundong; Shu, Kai; Liu, Chenghao; Liu, Huan

doi:10.1145/3340531.3411922

Cited by 103 publications

(87 citation statements)

References 23 publications

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“…Furthermore, RALE [21] uses GNNs to encode graph path-based hubs and capture the task-level dependency, to achieve knowledge transfer. GPN [6] adopts Prototypical Networks [33] to make the classification based on the distance between the node feature and the prototypes. AMM-GNN [43] leverages an attribute-level attention mechanism to characterize the feature distribution differences between different tasks and learns more meaningful transferable knowledge across tasks.…”

Section: Graph Few-shot Learningmentioning

confidence: 99%

Graph Few-shot Class-incremental Learning

Tan

Ding

Guo

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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The ability to incrementally learn new classes is vital to all realworld artificial intelligence systems. A large portion of high-impact applications like social media, recommendation systems, E-commerce platforms, etc. can be represented by graph models. In this paper, we investigate the challenging yet practical problem, Graph Few-shot Class-incremental (Graph FCL) problem, where the graph model is tasked to classify both newly encountered classes and previously learned classes. Towards that purpose, we put forward a Graph Pseudo Incremental Learning paradigm by sampling tasks recurrently from the base classes, so as to produce an arbitrary number of training episodes for our model to practice the incremental learning skill. Furthermore, we design a Hierarchical-Attention-based Graph Meta-learning framework, HAG-Meta from an optimization perspective. We present a task-sensitive regularizer calculated from task-level attention and node class prototypes to mitigate overfitting onto either novel or base classes. To employ the topological knowledge, we add a node-level attention module to adjust the prototype representation. Our model not only achieves greater stability of old knowledge consolidation, but also acquires advantageous adaptability to new knowledge with very limited data samples. Extensive experiments on three real-world datasets, including Amazon-clothing, Reddit, and DBLP, show that our framework demonstrates remarkable advantages in comparison with the baseline and other related state-of-the-art methods. CCS CONCEPTS• Computing methodologies → Lifelong machine learning.

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Section: Graph Few-shot Learningmentioning

confidence: 99%

Graph Few-shot Class-incremental Learning

Tan

Ding

Guo

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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“…To achieve the goal of "learning-to-learn", there are three types of different approaches. Metric-based methods are based on a similar idea to the nearest neighbors algorithm with a well-designed metric or distance function [28], prototypical networks [3,23] or Siamese Neural Network [13]. Model-based methods usually perform a rapid parameter update with an internal architecture or are controlled by another meta-learner model [22].…”

Section: Related Workmentioning

confidence: 99%

Sequential Recommendation for Cold-start Users with Meta Transitional Learning

Wang

Ding

Caverlee

2021

Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

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A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items. In many practical scenarios, however, there are a great number of cold-start users with only minimal logged interactions. As a result, existing sequential recommendation models will lose their predictive power due to the difficulties in learning sequential patterns over users with only limited interactions. In this work, we aim to improve sequential recommendation for cold-start users with a novel framework named MetaTL, which learns to model the transition patterns of users through meta-learning. Specifically, the proposed MetaTL: (i) formulates sequential recommendation for cold-start users as a few-shot learning problem; (ii) extracts the dynamic transition patterns among users with a translation-based architecture; and (iii) adopts meta transitional learning to enable fast learning for cold-start users with only limited interactions, leading to accurate inference of sequential interactions.

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“…Furthermore, [11] presents three fundamentally meta-learning problems for both node classification and link prediction. [4] proposes a framework Graph Prototypical Networks (GPN) for few-shot node classification on attributed networks. However, these works all focus on homogeneous graph while we consider FSL in HIN.…”

Section: Graph Few-shot Learningmentioning

confidence: 99%

“…Research on FSL has been carried out in many tasks, such as image classification [7,22,23,26], object detection [6] and text classification [30]. Recently, more and more attention has been paid to graph FSL tasks including graph classification [2], link prediction [1] and node classification [4,11,15,29,33]. Although these existing works have achieved great success, they take only homogeneous graph into consideration while few works study the FSL in Heterogeneous Information Network (HIN).…”

Section: Introductionmentioning

confidence: 99%

HINFShot: A Challenge Dataset for Few-Shot Node Classification in Heterogeneous Information Network

Zhuang

Xiang

Huang

et al. 2021

Proceedings of the 2021 International Conference on Multimedia Retrieval

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Few-shot learning aims to generalize to novel classes. It has achieved great success in image and text classification tasks. Inspired by such success, few-shot node classification in homogeneous graph has attracted much attention but few works have begun to study this problem in Heterogeneous Information Network (HIN) so far. We consider few-shot learning in HIN and study a pioneering problem HIN Few-Shot Node Classification (HIN-FSNC) that aims to generalize the node types with sufficient labeled samples to unseen node types with only few-labeled samples. However, existing HIN datasets contain just one labeled node type, which means they cannot meet the setting of unseen node types. To facilitate the investigation of HIN-FSNC, we propose a large-scale academic HIN dataset called HINFShot. It contains 1,235,031 nodes with four node types (author, paper, venue, institution) and all the nodes regardless of node type are divided into 80 classes. Finally, we conduct extensive experiments on HINFShot and the result indicates a significant challenge of identifying novel classes of unseen node types in HIN-FSNC. CCS CONCEPTS• Information systems → Data mining; • Computing methodologies → Supervised learning by classification.

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Graph Prototypical Networks for Few-shot Learning on Attributed Networks

Cited by 103 publications

References 23 publications

Graph Few-shot Class-incremental Learning

Graph Few-shot Class-incremental Learning

Sequential Recommendation for Cold-start Users with Meta Transitional Learning

HINFShot: A Challenge Dataset for Few-Shot Node Classification in Heterogeneous Information Network

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