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Found 34 papers, 11 papers with code
Boosting Domain Incremental Learning: Selecting the Optimal Parameters is All You Need
Deep neural networks (DNNs) often underperform in real-world, dynamic settings where data distributions change over time.
DualCP: Rehearsal-Free Domain-Incremental Learning via Dual-Level Concept Prototype
Inspired by the incremental cognitive process of the human brain, we design Dual-level Concept Prototypes (DualCP) for each class to address the conflict between learning new knowledge and retaining old knowledge in RFDIL.
Deal: Distributed End-to-End GNN Inference for All Nodes
Second, we introduce memory-saving and communication-efficient distributed primitives for lightweight 1-D graph and feature tensor collaborative partitioning-based distributed inference.
Space Rotation with Basis Transformation for Training-free Test-Time Adaptation
With the development of visual-language models (VLM) in downstream task applications, test-time adaptation methods based on VLM have attracted increasing attention for their ability to address changes distribution in test-time.
Spectro-Riemannian Graph Neural Networks
Can integrating spectral and curvature signals unlock new potential in graph representation learning?
AutoG: Towards automatic graph construction from tabular data
The absence of dedicated datasets to formalize and evaluate the effectiveness of graph construction methods, and 2.
Learning Endogenous Attention for Incremental Object Detection
In this paper, we focus on a challenging Incremental Object Detection (IOD) problem.
Prompt-Agnostic Adversarial Perturbation for Customized Diffusion Models
In this paper, we introduce a Prompt-Agnostic Adversarial Perturbation (PAP) method for customized diffusion models.
Hierarchical Compression of Text-Rich Graphs via Large Language Models
Large Language Models (LLMs), noted for their superior text understanding abilities, offer a solution for processing the text in graphs but face integration challenges due to their limitation for encoding graph structures and their computational complexities when dealing with extensive text in large neighborhoods of interconnected nodes.
GraphStorm: all-in-one graph machine learning framework for industry applications
GraphStorm has the following desirable properties: (a) Easy to use: it can perform graph construction and model training and inference with just a single command; (b) Expert-friendly: GraphStorm contains many advanced GML modeling techniques to handle complex graph data and improve model performance; (c) Scalable: every component in GraphStorm can operate on graphs with billions of nodes and can scale model training and inference to different hardware without changing any code.
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning
Inspired by this, we introduce Graph-aware Parameter-Efficient Fine-Tuning - GPEFT, a novel approach for efficient graph representation learning with LLMs on text-rich graphs.
KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web.
NetInfoF Framework: Measuring and Exploiting Network Usable Information
Given a node-attributed graph, and a graph task (link prediction or node classification), can we tell if a graph neural network (GNN) will perform well?
On the Initialization of Graph Neural Networks
In this paper, we analyze the variance of forward and backward propagation across GNN layers and show that the variance instability of GNN initializations comes from the combined effect of the activation function, hidden dimension, graph structure and message passing.
TouchUp-G: Improving Feature Representation through Graph-Centric Finetuning
How can we enhance the node features acquired from Pretrained Models (PMs) to better suit downstream graph learning tasks?
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
Evenworse, the tremendous overhead to synchronize the node memory make it impractical to be deployed to distributed GPU clusters.
Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications
Model pre-training on large text corpora has been demonstrated effective for various downstream applications in the NLP domain.
Pitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices
While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including the edges being predicted in the graph at training and/or test have outsized impact on the performance of low-degree nodes.
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning Research
Most existing public datasets for GNNs are relatively small, which limits the ability of GNNs to generalize to unseen data.
PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Prediction
However, GNN explanation for link prediction (LP) is lacking in the literature.
FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training
A key performance bottleneck when training graph neural network (GNN) models on large, real-world graphs is loading node features onto a GPU.
Hector: An Efficient Programming and Compilation Framework for Implementing Relational Graph Neural Networks in GPU Architectures
Relational graph neural networks (RGNNs) are graph neural networks with dedicated structures for modeling the different types of nodes and edges in heterogeneous graphs.
Refined Edge Usage of Graph Neural Networks for Edge Prediction
However, existing methods lack elaborate design regarding the distinctions between two tasks that have been frequently overlooked: (i) edges only constitute the topology in the node classification task but can be used as both the topology and the supervisions (i. e., labels) in the edge prediction task; (ii) the node classification makes prediction over each individual node, while the edge prediction is determinated by each pair of nodes.
Efficient and effective training of language and graph neural network models
The effectiveness in our framework is achieved by applying stage-wise fine-tuning of the BERT model first with heterogenous graph information and then with a GNN model.
ColdGuess: A General and Effective Relational Graph Convolutional Network to Tackle Cold Start Cases
It outperforms the lightgbm2 by up to 34 pcp ROC-AUC in a cold start case when a new seller sells a new product .
TGL: A General Framework for Temporal GNN Training on Billion-Scale Graphs
Our temporal parallel sampler achieves an average of 173x speedup on a multi-core CPU compared with the baselines.
Network In Graph Neural Network
However, wider hidden layers can easily lead to overfitting, and incrementally adding more GNN layers can potentially result in over-smoothing. In this paper, we present a model-agnostic methodology, namely Network In Graph Neural Network (NGNN ), that allows arbitrary GNN models to increase their model capacity by making the model deeper.
Ranked #1 on
Link Property Prediction
on ogbl-citation2
Graph Neural Network Training with Data Tiering
With our data tiering method, we additionally provide a new data placement and access strategy to further minimize the CPU-GPU communication overhead.
DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
To minimize the overheads associated with distributed computations, DistDGL uses a high-quality and light-weight min-cut graph partitioning algorithm along with multiple balancing constraints.
COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature
Finally, we propose a document similarity engine that leverages low-dimensional graph embeddings from the CKG with semantic embeddings for similar article retrieval.
Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning
While this study, by no means recommends specific drugs, it demonstrates a powerful deep learning methodology to prioritize existing drugs for further investigation, which holds the potential of accelerating therapeutic development for COVID-19.
DGL-KE: Training Knowledge Graph Embeddings at Scale
Experiments on knowledge graphs consisting of over 86M nodes and 338M edges show that DGL-KE can compute embeddings in 100 minutes on an EC2 instance with 8 GPUs and 30 minutes on an EC2 cluster with 4 machines with 48 cores/machine.
Distributed, Parallel, and Cluster Computing
A Novel Graph based Trajectory Predictor with Pseudo Oracle
Recent studies focus on modeling pedestrians' motion patterns with recurrent neural networks, capturing social interactions with pooling-based or graph-based methods, and handling future uncertainties by using random Gaussian noise as the latent variable.
Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.
Ranked #34 on
Node Classification
on Cora
