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Found 24 papers, 10 papers with code
Distributed Graph Neural Network Inference With Just-In-Time Compilation For Industry-Scale Graphs
Graph neural networks (GNNs) have delivered remarkable results in various fields.
GraphGen+: Advancing Distributed Subgraph Generation and Graph Learning On Industrial Graphs
Graph-based computations are crucial in a wide range of applications, where graphs can scale to trillions of edges.
Exploring the Potential of Large Language Models as Predictors in Dynamic Text-Attributed Graphs
In this work, we pioneer using LLMs for predictive tasks on dynamic graphs.
Multi-Grained Preference Enhanced Transformer for Multi-Behavior Sequential Recommendation
Secondly, a novel multi-scale transformer architecture equipped with multi-grained user preference extraction is proposed to encode the interaction-aware sequential pattern enhanced by capturing temporal behavior-aware multi-grained preference .
GraphRPM: Risk Pattern Mining on Industrial Large Attributed Graphs
Graph-based patterns are extensively employed and favored by practitioners within industrial companies due to their capacity to represent the behavioral attributes and topological relationships among users, thereby offering enhanced interpretability in comparison to black-box models commonly utilized for classification and recognition tasks.
Subgraph Retrieval Enhanced by Graph-Text Alignment for Commonsense Question Answering
Firstly, we transform the knowledge graph into a database of subgraph vectors and propose a BFS-style subgraph sampling strategy to avoid information loss, leveraging the analogy between BFS and the message-passing mechanism.
GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs
In this work, we propose the GraphCLIP framework to address these challenges by learning graph foundation models with strong cross-domain zero/few-shot transferability through a self-supervised contrastive graph-summary pretraining method.
Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents
Recent research has explored the use of Large Language Models (LLMs) for tackling complex graph reasoning tasks.
CL4KGE: A Curriculum Learning Method for Knowledge Graph Embedding
Knowledge graph embedding (KGE) constitutes a foundational task, directed towards learning representations for entities and relations within knowledge graphs (KGs), with the objective of crafting representations comprehensive enough to approximate the logical and symbolic interconnections among entities.
Graph Retrieval-Augmented Generation: A Survey
Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining.
Graph Triple Attention Network: A Decoupled Perspective
To address these challenges, we propose a high-level decoupled perspective of GTs, breaking them down into three components and two interaction levels: positional attention, structural attention, and attribute attention, alongside local and global interaction.
UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification Tasks
In this study, we introduce UniGAP, a universal and adaptive graph upsampling technique for graph data.
Ranked #19 on
Node Classification
on Actor
AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference for Preconditioning Matrix
Additionally, we introduce an auto-switching function that enables the preconditioning matrix to switch dynamically between Stochastic Gradient Descent (SGD) and the adaptive optimizer.
Efficient LDPC Decoding using Physical Computation
Due to 5G deployment, there is significant interest in LDPC decoding.
Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term
Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization.
Adaptive Optimizers with Sparse Group Lasso for Neural Networks in CTR Prediction
We develop a novel framework that adds the regularizers of the sparse group lasso to a family of adaptive optimizers in deep learning, such as Momentum, Adagrad, Adam, AMSGrad, AdaHessian, and create a new class of optimizers, which are named Group Momentum, Group Adagrad, Group Adam, Group AMSGrad and Group AdaHessian, etc., accordingly.
Path-based Deep Network for Candidate Item Matching in Recommenders
PDN utilizes Trigger Net to capture the user's interest in each of his/her interacted item, and Similarity Net to evaluate the similarity between each interacted item and the target item based on these items' profile and CF information.
GIPA: General Information Propagation Algorithm for Graph Learning
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation.
Ranked #1 on
Node Property Prediction
on ogbn-proteins
GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy
Herein, we present GraphTheta, the first distributed and scalable graph learning system built upon vertex-centric distributed graph processing with neural network operators implemented as user-defined functions.
Adaptive Optimizers with Sparse Group Lasso
We develop a novel framework that adds the regularizers to a family of adaptive optimizers in deep learning, such as MOMENTUM, ADAGRAD, ADAM, AMSGRAD, ADAHESSIAN, and create a new class of optimizers, which are named GROUP MOMENTUM, GROUP ADAGRAD, GROUP ADAM, GROUP AMSGRAD and GROUP ADAHESSIAN, etc., accordingly.
Woodpecker-DL: Accelerating Deep Neural Networks via Hardware-Aware Multifaceted Optimizations
Accelerating deep model training and inference is crucial in practice.
A Generalized and Robust Method Towards Practical Gaze Estimation on Smart Phone
For the second issue, we define a new metric to measure the robustness of gaze estimator, and propose an adversarial training based Disturbance with Ordinal loss (DwO) method to improve it.
Parallelized Kendall's Tau Coefficient Computation via SIMD Vectorized Sorting On Many-Integrated-Core Processors
Pairwise association measure is an important operation in data analytics.
