10 papers
SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation
Haoyu Liu, Ningyi Liao, Siqiang Luo
Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their…
Are Joins over LSM-Trees Ready? Take RocksDB as an Example
Weiping Yu, Fan Wang, Xuwei Zhang +1
LSM-tree-based data stores are widely adopted in industries for their excellent performance. As data scales increase, disk-based join operations become indispensable yet costly for…
[Extended Version] ArceKV: Towards Workload-driven LSM-compactions for Key-Value Store Under Dynamic Workloads
Junfeng Liu, Haoxuan Xie, Siqiang Luo
Key-value stores underpin a wide range of applications due to their simplicity and efficiency. Log-Structured Merge Trees (LSM-trees) dominate as their underlying structure, excell…
RadixGraph: A Fast, Space-Optimized Data Structure for Dynamic Graph Storage (Extended Version)
Haoxuan Xie, Junfeng Liu, Siqiang Luo +1
Dynamic graphs model many real-world applications, and as their sizes grow, efficiently storing and updating them becomes critical. We present RadixGraph, a fast and memory-efficie…
When Deepfake Detection Meets Graph Neural Network:a Unified and Lightweight Learning Framework
Haoyu Liu, Chaoyu Gong, Mengke He +3
The proliferation of generative video models has made detecting AI-generated and manipulated videos an urgent challenge. Existing detection approaches often fail to generalize acro…
A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness
Ningyi Liao, Haoyu Liu, Zulun Zhu +2
With recent advancements in graph neural networks (GNNs), spectral GNNs have received increasing popularity by virtue of their ability to retrieve graph signals in the spectral dom…