P3: Distributed deep graph learning at scale
Graph Neural Networks (GNNs) have gained significant attention in the recent past, and
become one of the fastest growing subareas in deep learning. While several new GNN …
become one of the fastest growing subareas in deep learning. While several new GNN …
DynaGraph: dynamic graph neural networks at scale
In this paper, we present DynaGraph, a system that supports dynamic Graph Neural
Networks (GNNs) efficiently. Based on the observation that existing proposals for dynamic …
Networks (GNNs) efficiently. Based on the observation that existing proposals for dynamic …
Commongraph: Graph analytics on evolving data
We consider the problem of graph analytics on evolving graphs (ie, graphs that change over
time). In this scenario, a query typically needs to be applied to different snapshots of the …
time). In this scenario, a query typically needs to be applied to different snapshots of the …
Practice of streaming processing of dynamic graphs: Concepts, models, and systems
Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …
machine learning, medical applications, social network analysis, computational sciences …
Practice of streaming processing of dynamic graphs: Concepts, models, and systems
Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …
machine learning, medical applications, social network analysis, computational sciences …
Mega evolving graph accelerator
Graph Processing is an emerging workload for applications working with unstructured data,
such as social network analysis, transportation networks, bioinformatics and operations …
such as social network analysis, transportation networks, bioinformatics and operations …
Affinity Alloc: Taming Not-So Near-Data Computing
To mitigate the data movement bottleneck on large multicore systems, the near-data
computing paradigm (NDC) offloads computation to where the data resides on-chip. The …
computing paradigm (NDC) offloads computation to where the data resides on-chip. The …
Kairos: Enabling prompt monitoring of information diffusion over temporal networks
Analyses of temporal graphs provide valuable insights into temporal data through the use of
two analytical approaches: temporal evolution and temporal information diffusion. The …
two analytical approaches: temporal evolution and temporal information diffusion. The …
A survey on dynamic graph processing on GPUs: concepts, terminologies and systems
H Gao, X Liao, Z Shao, K Li, J Chen, H Jin - Frontiers of Computer Science, 2024 - Springer
Graphs that are used to model real-world entities with vertices and relationships among
entities with edges, have proven to be a powerful tool for describing real-world problems in …
entities with edges, have proven to be a powerful tool for describing real-world problems in …
GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs
Graph Neural Networks (GNNs) play a crucial role in various fields. However, most existing
deep graph learning frameworks assume pre-stored static graphs and do not support …
deep graph learning frameworks assume pre-stored static graphs and do not support …