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Found 20 papers, 13 papers with code
ID-MixGCL: Identity Mixup for Graph Contrastive Learning
Recently developed graph contrastive learning (GCL) approaches compare two different "views" of the same graph in order to learn node/graph representations.
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs
Therefore, in this paper, we propose a novel automated graph neural network on heterophilic graphs, namely Auto-HeG, to automatically build heterophilic GNN models with expressive learning abilities.
State of the Art and Potentialities of Graph-level Learning
Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures.
DAGAD: Data Augmentation for Graph Anomaly Detection
To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes.
Geometry Contrastive Learning on Heterogeneous Graphs
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data.
Ultrahyperbolic Knowledge Graph Embeddings
Recent knowledge graph (KG) embeddings have been advanced by hyperbolic geometry due to its superior capability for representing hierarchies.
Graph-level Neural Networks: Current Progress and Future Directions
To frame this survey, we propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling.
Projective Ranking-based GNN Evasion Attacks
By projecting the strategy, our method dramatically minimizes the cost of learning a new attack strategy when the attack budget changes.
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning
In this survey, we aim to provide a systematic and comprehensive review of the contemporary deep learning techniques for graph anomaly detection.
Pseudo-Riemannian Graph Convolutional Networks
Empirical results demonstrate that our method outperforms Riemannian counterparts when embedding graphs of complex topologies.
A Comprehensive Survey on Community Detection with Deep Learning
A community reveals the features and connections of its members that are different from those in other communities in a network.
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
To overcome this problem, inspired by the recent success of graph contrastive learning and Siamese networks in visual representation learning, we propose a novel self-supervised approach in this paper to learn node representations by enhancing Siamese self-distillation with multi-scale contrastive learning.
Graph Self-Supervised Learning: A Survey
Deep learning on graphs has attracted significant interests recently.
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way.
Task-adaptive Neural Process for User Cold-Start Recommendation
In this paper, we develop a novel meta-learning recommender called task-adaptive neural process (TaNP).
Graph Stochastic Neural Networks for Semi-supervised Learning
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification.
Graph Geometry Interaction Learning
To utilize the strength of both Euclidean and hyperbolic geometries, we develop a novel Geometry Interaction Learning (GIL) method for graphs, a well-suited and efficient alternative for learning abundant geometric properties in graph.
Deep Learning for Community Detection: Progress, Challenges and Opportunities
As communities represent similar opinions, similar functions, similar purposes, etc., community detection is an important and extremely useful tool in both scientific inquiry and data analytics.
Deep Active Learning for Anchor User Prediction
Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction.
GraphNAS: Graph Neural Architecture Search with Reinforcement Learning
On node classification tasks, GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy.
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