P3: Distributed deep graph learning at scale

S Gandhi, AP Iyer - 15th {USENIX} Symposium on Operating Systems …, 2021 - usenix.org
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 …

DynaGraph: dynamic graph neural networks at scale

M Guan, AP Iyer, T Kim - Proceedings of the 5th ACM SIGMOD Joint …, 2022 - dl.acm.org
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 …

Commongraph: Graph analytics on evolving data

M Afarin, C Gao, S Rahman, N Abu-Ghazaleh… - Proceedings of the 28th …, 2023 - dl.acm.org
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 …

Practice of streaming processing of dynamic graphs: Concepts, models, and systems

M Besta, M Fischer, V Kalavri… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …

Practice of streaming processing of dynamic graphs: Concepts, models, and systems

M Besta, M Fischer, V Kalavri, M Kapralov… - arXiv preprint arXiv …, 2019 - arxiv.org
Graph processing has become an important part of various areas of computing, including
machine learning, medical applications, social network analysis, computational sciences …

Mega evolving graph accelerator

C Gao, M Afarin, S Rahman, N Abu-Ghazaleh… - Proceedings of the 56th …, 2023 - dl.acm.org
Graph Processing is an emerging workload for applications working with unstructured data,
such as social network analysis, transportation networks, bioinformatics and operations …

Affinity Alloc: Taming Not-So Near-Data Computing

Z Wang, C Liu, N Beckmann, T Nowatzki - … of the 56th Annual IEEE/ACM …, 2023 - dl.acm.org
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 …

Kairos: Enabling prompt monitoring of information diffusion over temporal networks

H Gaza, J Byun - IEEE Transactions on Knowledge and Data …, 2023 - ieeexplore.ieee.org
Analyses of temporal graphs provide valuable insights into temporal data through the use of
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 …

GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs

Y Zhong, G Sheng, T Qin, M Wang, Q Gan… - arXiv preprint arXiv …, 2023 - arxiv.org
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 …