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Official implementation for NeurIPS'24 paper "Generative Semi-supervised Graph Anomaly Detection"
[NeurIPS 2024 🔥] TEG-DB: A Comprehensive Dataset and Benchmark of Textual-Edge Graphs
Code for "Zero-shot Generalist Graph Anomaly Detection with Unified Neighborhood Prompts"
Official implementation for "UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction" (KDD 2024)
Survey: A collection of AWESOME papers and resources on the large language model (LLM) related recommender system topics.
A professional list on Multi-modal Data Fusion Models and Key Datasets for Urban Computing.
A professional list of Deep Learning and Large (Language) Models (LM, LLM, FM) for Trajectory Data Management and Mining.
An Awesome Collection of Urban Foundation Models (UFMs).
List the AI for Science papers accepted by top conferences
AI for Science 论文解读合集（持续更新ing），论文/数据集/教程下载：hyper.ai
Implementation of ICLR23 paper "Molecule Generation for Target Protein Binding with Structural Motifs"
This repository collects papers for "A Survey on Knowledge Distillation of Large Language Models". We break down KD into Knowledge Elicitation and Distillation Algorithms, and explore the Skill & V…
[WSDM'2024 Oral] "SSLRec: A Self-Supervised Learning Framework for Recommendation"
[CVPR'24 Highlight] SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection
[ICML'2024] "FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction"
Awesome-LLM-RAG: a curated list of advanced retrieval augmented generation (RAG) in Large Language Models
The official source code for "X-Ray: A Sequential 3D Representation for Generation".
PSICHIC (pronounced Psychic) - PhySIcoCHemICal graph neural network for learning protein-ligand interaction fingerprints from sequence data
What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks
Large Language Model-enhanced Recommender System Papers
Awesome Protein Representation Learning
clone/download repositories from https://anonymous.4open.science/
A framework to empover LLMs on graph reasoning and generation. Refer to our paper: https://arxiv.org/pdf/2402.08785.pdf