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Found 21 papers, 11 papers with code
MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding
In addition, microenvironments defined in previous work are largely based on experimentally assayed physicochemical properties, for which the "vocabulary" is usually extremely small.
Can we Soft Prompt LLMs for Graph Learning Tasks?
Graph plays an important role in representing complex relationships in real-world applications such as social networks, biological data and citation networks.
Towards Safer Large Language Models through Machine Unlearning
To address this gap, we introduce Selective Knowledge negation Unlearning (SKU), a novel unlearning framework for LLMs, designed to eliminate harmful knowledge while preserving utility on normal prompts.
G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface.
UGMAE: A Unified Framework for Graph Masked Autoencoders
In light of this, we propose UGMAE, a unified framework for graph masked autoencoders to address these issues from the perspectives of adaptivity, integrity, complementarity, and consistency.
TinyLLM: Learning a Small Student from Multiple Large Language Models
To solve the problems and facilitate the learning of compact language models, we propose TinyLLM, a new knowledge distillation paradigm to learn a small student LLM from multiple large teacher LLMs.
Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning
OPPU integrates parametric user knowledge in the personal PEFT parameters with the non-parametric knowledge acquired through retrieval and profile.
Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies
There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology.
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Exploring the full spectrum of trade-offs between privacy, model utility, and runtime efficiency is critical for practical unlearning scenarios.
Graph Neural Prompting with Large Language Models
While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost.
Class-Imbalanced Learning on Graphs: A Survey
Concerning the latter, we critically analyze recent work in CILG and discuss urgent lines of inquiry within the topic.
Knowledge Distillation on Graphs: A Survey
Therefore, to improve the applicability of GNNs and fully encode the complicated topological information, knowledge distillation on graphs (KDG) has been introduced to build a smaller yet effective model and exploit more knowledge from data, leading to model compression and performance improvement.
FakeEdge: Alleviate Dataset Shift in Link Prediction
In this paper, we first identify the dataset shift problem in the link prediction task and provide theoretical analyses on how existing link prediction methods are vulnerable to it.
Boosting Graph Neural Networks via Adaptive Knowledge Distillation
In this paper, we propose a novel adaptive KD framework, called BGNN, which sequentially transfers knowledge from multiple GNNs into a student GNN.
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning
The effectiveness is further guaranteed and proved by the gradients' distance between the subset and the full set; (ii) empirically, we discover that during the learning process of a GNN, some samples in the training dataset are informative for providing gradients to update model parameters.
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs
Existing methods attempt to address this scalability issue by training multi-layer perceptrons (MLPs) exclusively on node content features using labels derived from trained GNNs.
Heterogeneous Graph Masked Autoencoders
In light of this, we study the problem of generative SSL on heterogeneous graphs and propose HGMAE, a novel heterogeneous graph masked autoencoder model to address these challenges.
Graph-based Molecular Representation Learning
Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning.
RecipeRec: A Heterogeneous Graph Learning Model for Recipe Recommendation
We then propose RecipeRec, a novel heterogeneous graph learning model for recipe recommendation.
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural Networks
Learning effective recipe representations is essential in food studies.
TUNet: Incorporating segmentation maps to improve classification
Determining the localization of specific protein in human cells is important for understanding cellular functions and biological processes of underlying diseases.
