Search Results for author:
Found 13 papers, 4 papers with code
Beyond the Known: Novel Class Discovery for Open-world Graph Learning
Inter-class correlations are subsequently eliminated by the prototypical attention network, leading to distinctive representations for different classes.
GOODAT: Towards Test-time Graph Out-of-Distribution Detection
To identify and reject OOD samples with GNNs, recent studies have explored graph OOD detection, often focusing on training a specific model or modifying the data on top of a well-trained GNN.
T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation
A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i. e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors.
RAW-GNN: RAndom Walk Aggregation based Graph Neural Network
Due to the homophily assumption of Graph Convolutional Networks (GCNs) that these methods use, they are not suitable for heterophily graphs where nodes with different labels or dissimilar attributes tend to be adjacent.
TrustGNN: Graph Neural Network based Trust Evaluation via Learnable Propagative and Composable Nature
In this work, we propose a new GNN based trust evaluation method named TrustGNN, which integrates smartly the propagative and composable nature of trust graphs into a GNN framework for better trust evaluation.
Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning
However, the existing contrastive learning methods are inadequate for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e. g., meta-path) in graph data while ignore the noises that may exist in both node attributes and graph topologies.
Powerful Graph Convolutioal Networks with Adaptive Propagation Mechanism for Homophily and Heterophily
To address this problem, in this paper we design a novel propagation mechanism, which can automatically change the propagation and aggregation process according to homophily or heterophily between node pairs.
Block Modeling-Guided Graph Convolutional Neural Networks
Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation.
Universal Graph Convolutional Networks
So can we reasonably utilize these segmentation rules to design a universal propagation mechanism independent of the network structural assumption?
HTCInfoMax: A Global Model for Hierarchical Text Classification via Information Maximization
The second one encourages the structure encoder to learn better representations with desired characteristics for all labels which can better handle label imbalance in hierarchical text classification.
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning
We conclude with discussions of the challenges of the field and suggestions of possible directions for future research.
GCN for HIN via Implicit Utilization of Attention and Meta-paths
Graph neural networks for HIN embeddings typically adopt a hierarchical attention (including node-level and meta-path-level attentions) to capture the information from meta-path-based neighbors.
Using deep learning for community discovery in social networks
Thanks to spectral clustering which is one of the best community detection methods, the proposed new method is also good at community discovery task.
