bore3601's repositories
anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
awesome-community-detection
A curated list of community detection research papers with implementations.
awesome-public-datasets
An awesome list of (large-scale) public datasets on the Internet. (On-going collection)
bipartite-graph-learning
bipartite-graph-learning
CCF-2018-TeleCOM
CCF2018 数据挖掘 机器学习 智能匹配 特征工程
ChinaUnicom_BigData_Competition
2018年**联通大数据创新大赛：高端用户离网预测/用户换机时间预测全套代码
ComE
Learning Community Embedding with Community Detection and Node Embedding on Graphs
Data-Competition-TopSolution
Data competition Top Solution 数据竞赛top解决方案开源整理
DecryptPrompt
总结Prompt&LLM论文，开源数据&模型，AIGC应用
GNN-Communication-Networks
This is the repository for the collection of Graph-based Deep Learning for Communication Networks.
go-common
听说这是来自 https://github.com/openbilibili/go-common/ 的 “哔哩哔哩 bilibili 网站后台工程 源码”，不过咱也不知道这是啥。 据说 是他干的
Graph-Anomaly-Loss
TNNLS: A Synergistic Approach for Graph Anomaly Detection with Pattern Mining and Feature Learning; CIKM'20: Error-bounded Graph Anomaly Loss for GNNs.
graph-fraud-detection-papers
A curated list of fraud detection papers using graph information or graph neural networks
graph-neural-network-pyg
PyG (a geometric extension library for PyTorch) implementation of several Graph Neural Networks (GNNs): GCN, GAT, GraphSAGE, etc.
karateclub
A general purpose community detection and network embedding library for research built on NetworkX.
llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
NRLPapers
Must-read papers on network representation learning (NRL) / network embedding (NE)
QASystemOnMedicalKG
A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建，自动问答，基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱，并以该知识图谱完成自动问答与分析服务。
snad
Social Network Anomaly Detection
Social-Networks-Anomaly-Detetion
A list of social networks anomaly detection tutorials
Terminal-Changes-Prediction
基于某城市移动终端用户的运营商数据预测未来三月内用户是否会终端变迁(用户从当前使用的手机品牌更换为其他手机品牌)。应用xgboost算法和随机森林算法组合成多学习器预测模型。