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Found 15 papers, 3 papers with code
Characterizing the Influence of Topology on Graph Learning Tasks
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations.
Sculpting Molecules in 3D: A Flexible Substructure Aware Framework for Text-Oriented Molecular Optimization
This work not only holds general significance for the advancement of deep learning methodologies but also paves the way for a transformative shift in molecular design strategies.
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction
Monocular SLAM has received a lot of attention due to its simple RGB inputs and the lifting of complex sensor constraints.
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs
The latest advancements in large language models (LLMs) have revolutionized the field of natural language processing (NLP).
Empower Text-Attributed Graphs Learning with Large Language Models (LLMs)
In order to tackle this challenge, we propose a lightweight paradigm called ENG, which adopts a plug-and-play approach to empower text-attributed graphs through node generation using LLMs.
Measuring and Sampling: A Metric-guided Subgraph Learning Framework for Graph Neural Network
We demonstrate the effectiveness and efficiency of MeGuide in training various GNNs on multiple datasets.
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data Augmentations
In this paper, we show the potential hazards of inappropriate augmentations and then propose a novel Collaborative Graph Contrastive Learning framework (CGCL).
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection
AA-HGNN utilizes an active learning framework to enhance learning performance, especially when facing the paucity of labeled data.
Label Contrastive Coding based Graph Neural Network for Graph Classification
Graph classification is a critical research problem in many applications from different domains.
Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Network
To deal with these problems, in this paper, we propose a general subgraph-based training framework, namely Ripple Walk Training (RWT), for deep and large graph neural networks.
Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention
In addition, the experiment proved the expandability and generalizability of our for graph representation learning and other node classification related applications in heterogeneous graphs.
EnsemFDet: An Ensemble Approach to Fraud Detection based on Bipartite Graph
Existing fraud detection methods try to identify unexpected dense subgraphs and treat related nodes as suspicious.
Heterogeneous Deep Graph Infomax
The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.
Ranked #7 on
Heterogeneous Node Classification
on DBLP (PACT) 14k
BGADAM: Boosting based Genetic-Evolutionary ADAM for Neural Network Optimization
To resolve this problem and further maximize the advantages of genetic algorithm with base learners, we propose to implement the boosting strategy for input model training, which can subsequently improve the effectiveness of genetic algorithm.
DEAM: Adaptive Momentum with Discriminative Weight for Stochastic Optimization
Optimization algorithms with momentum, e. g., (ADAM), have been widely used for building deep learning models due to the faster convergence rates compared with stochastic gradient descent (SGD).
