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Allok: a machine learning approach for efficient graph execution on CPU–GPU clusters
The unprecedented increase in interconnected data has driven the development of efficient graph analytics for extensive data analysis, resulting in...
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Jittor: a novel deep learning framework with meta-operators and unified graph execution
This paper introduces Jittor, a fully just-in-time (JIT) compiled deep learning framework. With JIT compilation, we can achieve higher performance...
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DYGL: A Unified Benchmark and Library for Dynamic Graph
Difficulty in reproducing the code and inconsistent experimental methods hinder the development of the dynamic network field. We present DYGL, a...
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A Unified Stream and Batch Graph Computing Model for Community Detection
An essential challenge in graph data analysis and mining is to simply and effectively deal with large-scale network data that is expanding...
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Unified platform for storing, retrieving, and analysing biomechanical applications data using graph database
Sensors and smart equipment are frequently used in biomechanical systems and applications in sports and rehabilitation to measure various physical...
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Unified Models and Framework for Querying Distributed Data Across Polystores
Combining data sources from NoSQL and SQL systems leads to data distribution and complexifies user queries: data is distributed among different...
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Enhancing fault localization in microservices systems through span-level using graph convolutional networks
In the domain of cloud computing and distributed systems, microservices architecture has become preeminent due to its scalability and flexibility....
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A faster deep graph clustering network based on dynamic graph weight update mechanism
Deep graph clustering has attracted considerable attention for its potential in handling complex graph-structured data. However, existing approaches...
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PandaDB: An AI-Native Graph Database for Unified Managing Structured and Unstructured Data
In many applications, data are organized as graphs (e.g., social network and smart city). There could be unstructured data on such a graph, for...
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Unified Programming Models for Heterogeneous High-Performance Computers
Unified programming models can effectively improve program portability on various heterogeneous high-performance computers. Existing unified...
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OneGraph: a cross-architecture framework for large-scale graph computing on GPUs based on oneAPI
The explosive growth of graph data sets has led to an increase in the computing power and storage resources required for graph computing. To handle...
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Multi-perspective enriched instance graphs for next activity prediction through graph neural network
Today’s organizations store lots of data tracking the execution of their business processes. These data often contain valuable information that can...
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Estimation of execution time for computing tasks
This work aims to estimate the execution time of data processing tasks (specific executions of a program or an algorithm) before their execution. The...
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Learning to solve graph metric dimension problem based on graph contrastive learning
Deep learning has been widely used to solve graph and combinatorial optimization problems. However, proper model deployment is critical for training...
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Large-scale knowledge graph representation learning
The knowledge graph emerges as powerful data structures that provide a deep representation and understanding of the knowledge presented in networks....
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BejaGNN: behavior-based Java malware detection via graph neural network
As a popular platform-independent language, Java is widely used in enterprise applications. In the past few years, language vulnerabilities exploited...
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Query-driven graph models in e-commerce
Graph model has been widely used in e-commerce applications to speed up query processing. The graph model’s flexibility has led to the designing of...
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Self-supervised graph clustering via attention auto-encoder with distribution specificity
Graph clustering, an essential unsupervised learning task in data mining, has garnered significant attention in recent years. With the advent of deep...
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Unified-memory-based hybrid processing for partition-oriented subgraph matching on GPU
Subgraph isomorphism is a well known NP-hard problem that is to find all the matched subgraphs of a query graph in a large target graph. The...
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A Unification of Heterogeneous Data Sources into a Graph Model in E-commerce
The incorporation of heterogeneous data models into large-scale e-commerce applications incurs various complexities and overheads, such as redundancy...
