https://github.com/cgcl-codes/graphinstruct
The benchmark proposed in paper: GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
https://github.com/cgcl-codes/ohminer
Open-source release for OHMiner(EuroSys'25)
https://github.com/cgcl-codes/gecc
gECC: A GPU-based high-throughput framework for Elliptic Curve Cryptography
https://github.com/cgcl-codes/nifa
[NeurIPS 2024] Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections.
https://github.com/cgcl-codes/synergychain
A multichain-based data sharing framework with hierarchical access control
https://github.com/cgcl-codes/unlearnablepc
Official code for the NeurIPS 2024 paper "Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need"
https://github.com/cgcl-codes/ldcf
LDCF is a novel efficient approximate set representation structure for large-scale dynamic data sets. LDCF uses a novel multi-level tree structure and reduces the worst insertion and membership testing times from O(N) to O(1).
https://github.com/cgcl-codes/pstream
PStream is a popularity-aware differentiated distributed stream processing system, which identifies the popularity of keys in the stream data and uses a differentiated partitioning scheme. PStream greatly outperforms Storm on skew distributed data in terms of throughput and processing latency.
https://github.com/cgcl-codes/pensieve
Pensieve is a skewness-aware multi-version graph processing system that exploits the time locality of graph version access and leverages a differentiated graph storage strategy.