38 citations · 163 across the 25 of their papers we have counts for
25 papers
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…
[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…
How to Grow an LSM-tree? Towards Bridging the Gap Between Theory and Practice
Dingheng Mo, Siqiang Luo, Stratos Idreos
LSM-tree based key-value stores are widely adopted as the data storage backend in modern big data applications. The LSM-tree grows with data ingestion, by either adding levels with…
Finding Near-Optimal Maximum Set of Disjoint -Cliques in Real-World Social Networks
Wenqing Lin, Xin Chen, Haoxuan Xie +2
A -clique is a dense graph, consisting of fully-connected nodes, that finds numerous applications, such as community detection and network analysis. In this paper, we study…
Aster: Enhancing LSM-structures for Scalable Graph Database
Dingheng Mo, Junfeng Liu, Fan Wang +1
There is a proliferation of applications requiring the management of large-scale, evolving graphs under workloads with intensive graph updates and lookups. Driven by this challenge…
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…
DHIL-GT: Scalable Graph Transformer with Decoupled Hierarchy Labeling
Ningyi Liao, Zihao Yu, Siqiang Luo
Graph Transformer (GT) has recently emerged as a promising neural network architecture for learning graph-structured data. However, its global attention mechanism with quadratic co…
CAMAL: Optimizing LSM-trees via Active Learning
Weiping Yu, Siqiang Luo, Zihao Yu +1
We use machine learning to optimize LSM-tree structure, aiming to reduce the cost of processing various read/write operations. We introduce a new approach Camal, which boasts the f…
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…
LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs
Kai Wang, Yuwei Xu, Zhiyong Wu +1
Knowledge Graph (KG) inductive reasoning, which aims to infer missing facts from new KGs that are not seen during training, has been widely adopted in various applications. One cri…
Resurrecting Label Propagation for Graphs with Heterophily and Label Noise
Yao Cheng, Caihua Shan, Yifei Shen +3
Label noise is a common challenge in large datasets, as it can significantly degrade the generalization ability of deep neural networks. Most existing studies focus on noisy labels…
Learning to Optimize LSM-trees: Towards A Reinforcement Learning based Key-Value Store for Dynamic Workloads
Dingheng Mo, Fanchao Chen, Siqiang Luo +1
LSM-trees are widely adopted as the storage backend of key-value stores. However, optimizing the system performance under dynamic workloads has not been sufficiently studied or eva…
River of No Return: Graph Percolation Embeddings for Efficient Knowledge Graph Reasoning
Kai Wang, Siqiang Luo, Dan Lin
We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge graph (KG) reasoning. For the first time, we link the path redundancy issue in the state-of-the-art K…
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…
Massively Parallel Single-Source SimRanks in Rounds
Siqiang Luo, Zulun Zhu
SimRank is one of the most fundamental measures that evaluate the structural similarity between two nodes in a graph and has been applied in a plethora of data management tasks. Th…
Less is More: Towards Lightweight Cost Estimator for Database Systems
Weiping Yu, Siqiang Luo
We present FasCo, a simple yet effective learning-based estimator for the cost of executing a database query plan. FasCo uses significantly shorter training time and a lower infere…
Distributed Graph Embedding with Information-Oriented Random Walks
Peng Fang, Arijit Khan, Siqiang Luo +5
Graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. The increasing availability of billion-edge graphs underscores the imp…
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
Ningyi Liao, Dingheng Mo, Siqiang Luo +2
Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in sol…
Proteus: A Self-Designing Range Filter
Eric R. Knorr, Baptiste Lemaire, Andrew Lim +4
We introduce Proteus, a novel self-designing approximate range filter, which configures itself based on sampled data in order to optimize its false positive rate (FPR) for a given…
Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
Xiang Li, Renyu Zhu, Yao Cheng +4
We investigate graph neural networks on graphs with heterophily. Some existing methods amplify a node's neighborhood with multi-hop neighbors to include more nodes with homophily.…
Spiking Graph Convolutional Networks
Zulun Zhu, Jiaying Peng, Jintang Li +3
Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemente…
DMCS : Density Modularity based Community Search
Junghoon Kim, Siqiang Luo, Gao Cong +1
Community Search, or finding a connected subgraph (known as a community) containing the given query nodes in a social network, is a fundamental problem. Most of the existing commun…
A Survey on Machine Learning Solutions for Graph Pattern Extraction
Kai Siong Yow, Ningyi Liao, Siqiang Luo +3
A subgraph is constructed by using a subset of vertices and edges of a given graph. There exist many graph properties that are hereditary for subgraphs. Hence, researchers from dif…