Graph meta-learning
WebSep 11, 2024 · We study “graph meta-learning” for few-shot learning, in which every learning task’s prediction space is defined by a subset of nodes from a given graph, e.g., 1) a subset of classes from a hierarchy of classes for classification tasks; 2) a subset of variables from a graphical model as prediction targets for regression tasks; or 3) a ... WebApr 11, 2024 · To address this difficulty, we propose a multi-graph neural group recommendation model with meta-learning and multi-teacher distillation, consisting of …
Graph meta-learning
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WebOct 19, 2024 · To answer these questions, in this paper, we propose a graph meta-learning framework -- Graph Prototypical Networks (GPN). By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform meta-learning on an attributed network and derive a highly generalizable model … WebFeb 22, 2024 · Few-shot Network Anomaly Detection via Cross-network Meta-learning. Network anomaly detection aims to find network elements (e.g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority. It has a profound impact in a variety of applications ranging from finance, healthcare to social network analysis.
WebApr 20, 2024 · To this end, we propose to tackle few-shot learning on HG and develop a novel model for H eterogeneous G raph Meta -learning (a.k.a. HG-Meta ). Regarding … WebDec 20, 2024 · Meta-Graph: Few shot Link Prediction via Meta Learning. Fast adaptation to new data is one key facet of human intelligence and is an unexplored problem on graph-structured data. Few-Shot Link Prediction is a challenging task representative of real world data with evolving sub-graphs or entirely new graphs with shared structure.
Weband language, e.g., [39, 51, 27]. However, meta learning on graphs has received considerably less research attention and has remained a problem beyond the reach of prevailing GNN models. Meta learning on graphs generally refers to a scenario in which a model learns at two levels. In the first level, rapid learning occurs within a task. WebOct 26, 2024 · As one of the most famous methods, MAML [20] treats the meta-learner as parameter initialization by bi-level optimization, we use MAML as the basic framework in this paper. Besides, [21] raised ...
WebOct 19, 2024 · To tackle the aforementioned problem, we propose a novel graph meta-learning framework--Attribute Matching Meta-learning Graph Neural Networks (AMM-GNN). Specifically, the proposed AMM-GNN leverages an attribute-level attention mechanism to capture the distinct information of each task and thus learns more …
WebMoreover, we propose a task-adaptive meta-learning algorithm to provide meta knowledge customization for different tasks in few-shot scenarios. Experiments on multiple real-life benchmark datasets show that HSL-RG is superior to existing state-of-the-art approaches. ... Keywords: Few-shot learning; Graph neural networks; Meta learning ... diab neuropathy cksWeblem of weakly-supervised graph meta-learning for improving the model robustness in terms of knowledge transfer. To achieve this goal, we propose a new graph meta-learning … diabnext creation compteWebFeb 22, 2024 · Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve … diabolical box walkthroughWebEngineering manager in AI. PhD of statistics, MS of computer sciences. Built industry solutions with SoTA graph learning, video understanding, NLP … diabl startech ledWebmeta-learning has been applied to different few-shot graph learning problems, most existing efforts predominately assume that all the data from those seen classes is gold-labeled, while those methods cinergy seatingWebJan 11, 2024 · The objective of the graph augmenter is to promote our feature extraction network to learn a more discriminative feature representation, which motivates us to propose a meta-learning paradigm. Empirically, the experiments across multiple benchmark datasets demonstrate that MEGA outperforms the state-of-the-art methods in graph self … dia boc thien tinhWebApr 10, 2024 · Results show that learners had an inadequate graphical frame as they drew a graph that had elements of a value bar graph, distribution bar graph and a histogram all representing the same data set. cine ritz shopping sul