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Found 12 papers, 8 papers with code
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 effectiveness in modeling complex data relations represented by graph structural data.
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy
To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels.
Domain-adaptive Message Passing Graph Neural Network
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently.
Comparison of Update and Genetic Training Algorithms in a Memristor Crossbar Perceptron
Memristor-based computer architectures are becoming more attractive as a possible choice of hardware for the implementation of neural networks.
Against Adversarial Learning: Naturally Distinguish Known and Unknown in Open Set Domain Adaptation
Open set domain adaptation refers to the scenario that the target domain contains categories that do not exist in the source domain.
Deep Adversarial Domain Adaptation Based on Multi-layer Joint Kernelized Distance
In this paper, a deep adversarial domain adaptation model based on a multi-layer joint kernelized distance metric is proposed.
Network Together: Node Classification via Cross-Network Deep Network Embedding
On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.
Adversarial Deep Network Embedding for Cross-network Node Classification
This motivates us to propose an adversarial cross-network deep network embedding (ACDNE) model to integrate adversarial domain adaptation with deep network embedding so as to learn network-invariant node representations that can also well preserve the network structural information.
Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
Existing methods for single network learning cannot solve this problem due to the domain shift across networks.
Adversarial Training Methods for Network Embedding
To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain.
Network Together: Node Classification via Cross network Deep Network Embedding
On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations.
Social and Information Networks
Deep Network Embedding for Graph Representation Learning in Signed Networks
As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector representation for each node of a given network.
