xiaolunzhou's starred repositories
agentscope
Start building LLM-empowered multi-agent applications in an easier way.
Data-Copilot
Data-Copilot: Bridging Billions of Data and Humans with Autonomous Workflow
SheetCopilot
We release a general framework for prompting LLMs to manipulate software in a closed-loop manner.
AAAI-2024-Papers
AAAI 2024 Papers: Explore a comprehensive collection of innovative research papers presented at one of the premier artificial intelligence conferences. Seamlessly integrate code implementations for better understanding. ⭐ experience the forefront of progress in artificial intelligence with this repository!
LLMAgentPapers
Must-read Papers on LLM Agents.
community-graphs
Collection of graphs with communities and ground truth partition
handy_graph
A collection of frequently-used functions based on networkx
Awesome-Deep-Community-Detection
Deep and conventional community detection related papers, implementations, datasets, and tools.
Awesome-Graph-LLM
A collection of AWESOME things about Graph-Related LLMs.
awesome-graph-transformer
Papers about graph transformers.
SubgraphMatching
In-Memory Subgraph Matching: An In-depth Study by Dr. Shixuan Sun and Prof. Qiong Luo
A-Unified-Framework-for-Deep-Attribute-Graph-Clustering
This project is a scalable unified framework for deep graph clustering.
GraMi
GraMi is a novel framework for frequent subgraph mining in a single large graph, GraMi outperforms existing techniques by 2 orders of magnitudes. GraMi supports finding frequent subgraphs as well as frequent patterns, Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Also, GraMi supports user-defined structural and semantic constraints over the results, as well as approximate results. For more details, check our paper: Mohammed Elseidy, Ehab Abdelhamid, Spiros Skiadopoulos, and Panos Kalnis. GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph. PVLDB, 7(7):517-528, 2014.
graph-based-deep-learning-literature
links to conference publications in graph-based deep learning
CipherChat
A framework to evaluate the generalization capability of safety alignment for LLMs
densest-subgraph
Epasto et al. Efficient Densest Subgraph Computation in Evolving Graphs, WWW, 2015
Awesome-Deep-Graph-Clustering
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).