The Large-Scale Graph Data Science (LAGAS) Group is a team of researchers focused on exploring the vast and complex world of graph data science. With a growing interest in graph analytics and machine learning, the group is dedicated to designing and implementing scalable algorithms and tools to analyze large-scale graphs. Their research encompasses a broad range of applications, including social network analysis, recommendation systems, and bioinformatics. Through their work, the group aims to advance the field of graph data science and help solve real-world problems using cutting-edge technology.
LAGAS Group, HKBU
Large dAta alGorithm, Ai, and System
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TPC
TPC PublicThe official implementation of the KDD 2024 paper "Effective Clustering on Large Attributed Bipartite Graphs"
Python
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awesome-anomaly-detection
awesome-anomaly-detection Public archiveForked from hoya012/awesome-anomaly-detection
A curated list of awesome anomaly detection resources
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ann-benchmarks
ann-benchmarks Public archiveForked from erikbern/ann-benchmarks
Benchmarks of approximate nearest neighbor libraries in Python
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- awesome-cpp Public archive Forked from fffaraz/awesome-cpp
A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.
HKBU-LAGAS/awesome-cpp’s past year of commit activity - ann-benchmarks Public archive Forked from erikbern/ann-benchmarks
Benchmarks of approximate nearest neighbor libraries in Python
HKBU-LAGAS/ann-benchmarks’s past year of commit activity - awesome-graph-transformer Public archive Forked from wehos/awesome-graph-transformer
Papers about graph transformers.
HKBU-LAGAS/awesome-graph-transformer’s past year of commit activity - awesome-anomaly-detection Public archive Forked from hoya012/awesome-anomaly-detection
A curated list of awesome anomaly detection resources
HKBU-LAGAS/awesome-anomaly-detection’s past year of commit activity
