Search Results for author:
Found 21 papers, 10 papers with code
PyGOD: A Python Library for Graph Outlier Detection
PyGOD is an open-source Python library for detecting outliers on graph data.
A Simple Yet Effective Pretraining Strategy for Graph Few-shot Learning
Recently, increasing attention has been devoted to the graph few-shot learning problem, where the target novel classes only contain a few labeled nodes.
Few-Shot Learning on Graphs: A Survey
In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.
Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning
Graph Contrastive Learning (GCL) recently has drawn much research interest for learning generalizable, transferable, and robust node representations in a self-supervised fashion.
Data Augmentation for Deep Graph Learning: A Survey
To counter the data noise and data scarcity issues in deep graph learning (DGL), increasing graph data augmentation research has been conducted lately.
Session-based Recommendation with Hypergraph Attention Networks
Session-based recommender systems aim to improve recommendations in short-term sessions that can be found across many platforms.
Graph Few-shot Class-incremental Learning
The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems.
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning
Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance in various graph learning tasks.
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation
In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data.
Sequential Recommendation for Cold-start Users with Meta Transitional Learning
A fundamental challenge for sequential recommenders is to capture the sequential patterns of users toward modeling how users transit among items.
Weakly-supervised Graph Meta-learning for Few-shot Node Classification
Graphs are widely used to model the relational structure of data, and the research of graph machine learning (ML) has a wide spectrum of applications ranging from drug design in molecular graphs to friendship recommendation in social networks.
FBAdTracker: An Interactive Data Collection and Analysis Tool for Facebook Advertisements
The growing use of social media has led to drastic changes in our decision-making.
AdaGNN: Graph Neural Networks with Adaptive Frequency Response Filter
Existing efforts can be mainly categorized as spectral-based and spatial-based methods.
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.
Fact-Enhanced Synthetic News Generation
The existing text generation methods either afford limited supplementary information or lose consistency between the input and output which makes the synthetic news less trustworthy.
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Text classification is a critical research topic with broad applications in natural language processing.
Combating Disinformation in a Social Media Age
The creation, dissemination, and consumption of disinformation and fabricated content on social media is a growing concern, especially with the ease of access to such sources, and the lack of awareness of the existence of such false information.
GLOW : Global Weighted Self-Attention Network for Web Search
Deep matching models aim to facilitate search engines retrieving more relevant documents by mapping queries and documents into semantic vectors in the first-stage retrieval.
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.
Feature Interaction-aware Graph Neural Networks
Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on different graph learning tasks.
Deep Anomaly Detection on Attributed Networks
In particular, our proposed deep model: (1) explicitly models the topological structure and nodal attributes seamlessly for node embedding learning with the prevalent graph convolutional network (GCN); and (2) is customized to address the anomaly detection problem by virtue of deep autoencoder that leverages the learned embeddings to reconstruct the original data.
