research-article

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models

Authors:

Benedek Rozemberczki,

George Panagopoulos,

Alexander Riedel,

Maria Astefanoaei,

Guzmán López,

Nicolas Collignon, and

Rik SarkarAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

Pages 4564 - 4573

https://doi.org/10.1145/3459637.3482014

Published: 30 October 2021 Publication History

Abstract

We present PyTorch Geometric Temporal, a deep learning framework combining state-of-the-art machine learning algorithms for neural spatiotemporal signal processing. The main goal of the library is to make temporal geometric deep learning available for researchers and machine learning practitioners in a unified easy-to-use framework. PyTorch Geometric Temporal was created with foundations on existing libraries in the PyTorch eco-system, streamlined neural network layer definitions, temporal snapshot generators for batching, and integrated benchmark datasets. These features are illustrated with a tutorial-like case study. Experiments demonstrate the predictive performance of the models implemented in the library on real-world problems such as epidemiological forecasting, ride-hail demand prediction, and web traffic management. Our sensitivity analysis of runtime shows that the framework can potentially operate on web-scale datasets with rich temporal features and spatial structure.

References

[1]

Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016. Tensorflow: A System for Large-Scale Machine Learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16). 265--283.

Digital Library

[2]

Sean Anderson, Eric Ward, Lewis Barnett, and Philippina English. 2018. sdmTMB: Spatial and spatiotemporal GLMMs with TMB. https://github.com/pbs-assess/sdmTMB.

[3]

Dzmitry Bahdanau, Kyung Hyun Cho, and Yoshua Bengio. 2015. Neural Machine Translation by Jointly Learning to Align and Translate. In 3rd International Conference on Learning Representations, ICLR 2015.

[4]

Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang. 2020. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting. Advances in Neural Information Processing Systems, Vol. 33 (2020).

[5]

Ferenc Béres, Domokos M. Kelen, Róbert Pálovics, and András A. Benczúr. 2019. Node Embeddings in Dynamic Graphs. Applied Network Science, Vol. 4, 64 (2019), 25.

[6]

Ferenc Béres, Róbert Pálovics, Anna Oláh, and András A. Benczúr. 2018. Temporal Walk Based Centrality Metric for Graph Streams. Applied Network Science, Vol. 3, 32 (2018), 26.

[7]

Aleksandar Bojchevski, Johannes Klicpera, Bryan Perozzi, Amol Kapoor, Martin Blais, Benedek Rózemberczki, Michal Lukasik, and Stephan Günnemann. 2020. Scaling Graph Neural Networks with Approximate Pagerank. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2464--2473.

Digital Library

[8]

Georg Brandl. 2010. Sphinx Documentation. URL http://sphinx-doc.org/sphinx. pdf (2010).

[9]

Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Peng Zhang, Guohao Dai, et al. 2021. CogDL: An Extensive Toolkit for Deep Learning on Graphs. arXiv preprint arXiv:2103.00959 (2021).

[10]

Jinyin Chen, Xuanheng Xu, Yangyang Wu, and Haibin Zheng. 2018. GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction. arXiv preprint arXiv:1812.04206 (2018).

[11]

Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. 2015. MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems. arXiv preprint arXiv:1512.01274 (2015).

[12]

Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning Phrase Representations Using RNN Encoder--Decoder for Statistical Machine Translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 1724--1734.

[13]

CSIRO's Data61. 2018. StellarGraph Machine Learning Library. https://github.com/stellargraph/stellargraph.

[14]

Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems. 3844--3852.

Digital Library

[15]

Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.

[16]

Roy Frostig, Matthew James Johnson, and Chris Leary. 2018. Compiling Machine Learning Programs via High-Level Tracing. Systems for Machine Learning (2018).

[17]

Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural Message Passing for Quantum Chemistry. In International Conference on Machine Learning. PMLR, 1263--1272.

Digital Library

[18]

Jonathan Godwin, Thomas Keck, Peter Battaglia, Victor Bapst, Thomas Kipf, Yujia Li, Kimberly Stachenfeld, Petar Velivcković, and Alvaro Sanchez-Gonzalez. 2020. Jraph: A Library for Graph Neural Networks in Jax. http://github.com/deepmind/jraph

[19]

Palash Goyal, Sujit Rokka Chhetri, Ninareh Mehrabi, Emilio Ferrara, and Arquimedes Canedo. 2018. DynamicGEM: A Library for Dynamic Graph Embedding Methods. arXiv preprint arXiv:1811.10734 (2018).

[20]

Palash Goyal and Emilio Ferrara. [n.d.]. GEM: A Python Package for Graph Embedding Methods. Journal of Open Source Software, Vol. 3, 29 ( [n.,d.]), 876.

[21]

Daniele Grattarola and Cesare Alippi. 2020. Graph Neural Networks in TensorFlow and Keras with Spektral. arXiv preprint arXiv:2006.12138 (2020).

[22]

Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan. 2019. Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 922--929.

Digital Library

[23]

Matthew Hanson. 2019. The Open-Source Software Ecosystem for Leveraging Public Datasets in Spatio-Temporal Asset Catalogs (STAC). In AGU Fall Meeting Abstracts, Vol. 2019. IN23B--07.

[24]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long Short-Term Memory. Neural computation, Vol. 9, 8 (1997), 1735--1780.

Digital Library

[25]

Petter Holme. 2015. Modern Temporal Network Theory: A Colloquium. The European Physical Journal B, Vol. 88, 9 (2015), 1--30.

[26]

Petter Holme and Jari Saram"aki. 2012. Temporal Networks. Physics reports, Vol. 519, 3 (2012), 97--125.

[27]

Jun Hu, Shengsheng Qian, Quan Fang, Youze Wang, Quan Zhao, Huaiwen Zhang, and Changsheng Xu. 2021. Efficient Graph Deep Learning in TensorFlow with TF Geometric. arXiv preprint arXiv:2101.11552 (2021).

[28]

James Bradbury and Roy Frostig and Peter Hawkins and Matthew James Johnson and Chris Leary and Dougal Maclaurin and George Necula and Adam Paszke and Jake VanderPlas and Skye Wanderman-Milne and Qiao Zhang. 2018. JAX: Composable Transformations of Python+NumPy Programs. http://github.com/google/jax

[29]

Diederik Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In Proceedings of the 3rd International Conference on Learning Representations.

[30]

Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR).

[31]

Jia Li, Zhichao Han, Hong Cheng, Jiao Su, Pengyun Wang, Jianfeng Zhang, and Lujia Pan. 2019. Predicting Path Failure in Time-Evolving Graphs. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1279--1289.

Digital Library

[32]

Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In International Conference on Learning Representations.

[33]

Yujia Li, Richard Zemel, Marc Brockschmidt, and Daniel Tarlow. 2016. Gated Graph Sequence Neural Networks. In International Conference on Learning Representations (ICLR).

[34]

Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Zhao Xu, Haiyang Yu, Jingtun Zhang, Yi Liu, Keqiang Yan, Bora Oztekin, Haoran Liu, Xuan Zhang, Cong Fu, and Shuiwang Ji. 2021. DIG: A Turnkey Library for Diving into Graph Deep Learning Research. arXiv preprint arXiv:2103.12608 (2021).

[35]

Minh-Thang Luong, Hieu Pham, and Christopher D Manning. 2015. Effective Approaches to Attention-based Neural Machine Translation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1412--1421.

[36]

Harold B Maynard, G J Stegemerten, and John L Schwab. 1948. Methods-Time Measurement. McGraw-Hill.

[37]

Vinod Nair and Geoffrey E Hinton. 2010. Rectified Linear Units Improve Restricted Boltzmann Machines. In Proceedings of the 27th International Conference on International Conference on Machine Learning. 807--814.

Digital Library

[38]

George Panagopoulos, Giannis Nikolentzos, and Michalis Vazirgiannis. 2021. Transfer Graph Neural Networks for Pandemic Forecasting. In Proceedings of the 35th AAAI Conference on Artificial Intelligence.

[39]

Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B Schardl, and Charles E Leiserson. 2020. EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs. In AAAI. 5363--5370.

[40]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 a. PyTorch: An Imperative Style, High-Performance Deep Learning Library. In Advances in Neural Information Processing Systems. 8024--8035.

Digital Library

[41]

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 b. PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems, Vol. 32 (2019), 8026--8037.

Digital Library

[42]

Edzer Pebesma. 2017. staRs: Spatiotemporal Arrays: Raster and Vector Datacubes. https://github.com/r-spatial/stars.

[43]

Sergio J Rey and Luc Anselin. 2010. PySAL: A Python Library of Spatial Analytical Methods. In Handbook of Applied Spatial Analysis. Springer, 175--193.

[44]

Emanuele Rob. 2020. PySTAC: Python library for working with any SpatioTemporal Asset Catalog (STAC). https://github.com/stac-utils/pystac. GitHub repository.

[45]

Benedek Rozemberczki, Peter Englert, Amol Kapoor, Martin Blais, and Bryan Perozzi. 2020 a. Pathfinder Discovery Networks for Neural Message Passing. arXiv preprint arXiv:2010.12878 (2020).

[46]

Benedek Rozemberczki, Oliver Kiss, and Rik Sarkar. 2020 b. Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 3125--3132.

Digital Library

[47]

Benedek Rozemberczki, Paul Scherer, Oliver Kiss, Rik Sarkar, and Tamas Ferenci. 2021. Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks .arxiv: 2102.08100 [cs.LG]

[48]

Jason Sanders and Edward Kandrot. 2010. CUDA by Example: An Introduction to General-Purpose GPU Programming. Addison-Wesley Professional.

Digital Library

[49]

Paul Scherer and Pietro Lio. 2020. Learning Distributed Representations of Graphs with Geo2DR. In ICML Workshop on Graph Representation Learning and Beyond.

[50]

Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling Relational Data with Graph Convolutional Networks. In European Semantic Web Conference. Springer, 593--607.

[51]

Youngjoo Seo, Michaël Defferrard, Pierre Vandergheynst, and Xavier Bresson. 2018. Structured Sequence Modeling with Graph Convolutional Recurrent Networks. In International Conference on Neural Information Processing. Springer, 362--373.

[52]

Lei Shi, Yifan Zhang, Jian Cheng, and Hanqing Lu. 2019. Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12026--12035.

[53]

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. The Journal of Machine Learning Research, Vol. 15, 1 (2014), 1929--1958.

Digital Library

[54]

Aynaz Taheri and Tanya Berger-Wolf. 2019. Predictive Temporal Embedding of Dynamic Graphs. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. 57--64.

Digital Library

[55]

Aynaz Taheri, Kevin Gimpel, and Tanya Berger-Wolf. 2019. Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models. In Companion Proceedings of The 2019 World Wide Web Conference (WWW '19). 301--307.

Digital Library

[56]

Paul Taylor, Christopher Harris, Thompson Comer, and Mark Harris. 2019. CUDA-Accelerated GIS and Spatiotemporal Algorithms. https://github.com/rapidsai/cuspatial.

[57]

Cunchao Tu, Yuan Yao, Zhengyan Zhang, Ganqu Cui, Hao Wang, Changxin Tian, Jie Zhou, and Cheng Yang. 2018. OpenNE: An Open Source Toolkit for Network Embedding. https://github.com/thunlp/OpenNE.

[58]

Stefan Van Der Walt, S. Chris Colbert, and Gael Varoquaux. 2011. The NumPy Array: a Structure for Efficient Numerical Computation. Computing in science & engineering, Vol. 13, 2 (2011), 22--30.

Digital Library

[59]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 6000--6010.

Digital Library

[60]

Michael A Whitby, Rich Fecher, and Chris Bennight. 2017. GeoWave: Utilizing Distributed Key-Value Stores for Multidimensional Data. In International Symposium on Spatial and Temporal Databases. Springer, 105--122.

[61]

Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. 2020. Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 753--763.

Digital Library

[62]

Hongxia Yang. 2019. AliGraph: A Comprehensive Graph Neural Network Platform. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 3165--3166.

Digital Library

[63]

Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2018. Spatio-Temporal Graph Convolutional Networks: a Deep Learning Framework for Traffic Forecasting. In Proceedings of the 27th International Joint Conference on Artificial Intelligence. 3634--3640.

Digital Library

[64]

Le Yu, Leilei Sun, Bowen Du, Chuanren Liu, Hui Xiong, and Weifeng Lv. 2020. Predicting Temporal Sets with Deep Neural Networks. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1083--1091.

Digital Library

[65]

Fan Zhang, Valentin Bazarevsky, Andrey Vakunov, Andrei Tkachenka, George Sung, Chuo-Ling Chang, and Matthias Grundmann. 2020. MediaPipe Hands: On-device Real-time Hand Tracking .arxiv: 2006.10214 [cs.CV]

[66]

Ling Zhao, Yujiao Song, Chao Zhang, Yu Liu, Pu Wang, Tao Lin, Min Deng, and Haifeng Li. 2019. T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems, Vol. 21, 9 (2019), 3848--3858.

[67]

Chuanpan Zheng, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. 2020a. GMAN: A Graph Multi-Attention Network for Traffic Prediction. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 1234--1241.

[68]

Da Zheng, Minjie Wang, Quan Gan, Zheng Zhang, and George Karypis. 202 b. Learning Graph Neural Networks with Deep Graph Library. In Companion Proceedings of the Web Conference 2020 (WWW '20). 305--306.

Digital Library

[69]

Jiawei Zhu, Yujiao Song, Ling Zhao, and Haifeng Li. 2020. A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting. arXiv preprint arXiv:2006.11583 (2020).

[70]

Esteban Zimányi, Mahmoud Sakr, and Arthur Lesuisse. 2020. MobilityDB: A Mobility Database Based on PostgreSQL and PostGIS. ACM Transactions on Database Systems (TODS), Vol. 45, 4 (2020), 1--42.

Digital Library

Cited By

Ahmad MAli MHasan MMobo FRai S(2024)Geospatial Machine Learning and the Power of Python ProgrammingEthics, Machine Learning, and Python in Geospatial Analysis10.4018/979-8-3693-6381-2.ch010(223-253)Online publication date: 10-May-2024
https://doi.org/10.4018/979-8-3693-6381-2.ch010
Rösch MNolde MUllmann TRiedlinger T(2024)Data-Driven Wildfire Spread Modeling of European Wildfires Using a Spatiotemporal Graph Neural NetworkFire10.3390/fire70602077:6(207)Online publication date: 19-Jun-2024
https://doi.org/10.3390/fire7060207
Laidi RDjenouri DDjenouri YLin J(2024)TG-SPRED: Temporal Graph for Sensorial Data PREDictionACM Transactions on Sensor Networks10.1145/364989220:3(1-20)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3649892
Show More Cited By

Index Terms

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

Recommendations

t-ConvESN: Temporal Convolution-Readout for Random Recurrent Neural Networks
Artificial Neural Networks and Machine Learning – ICANN 2023
Abstract
While deep neural networks have excelled at static data such as images, temporal data - the data that these networks will need to process in the real world - remains an open challenge. Handling temporal data with neural networks requires one of ...
Read More
The nature of unsupervised learning in deep neural networks: A new understanding and novel approach

Over the last decade, the deep neural networks are a hot topic in machine learning. It is breakthrough technology in processing images, video, speech, text and audio. Deep neural network permits us to overcome some limitations of a shallow neural ...
Read More
Survey of Deep Learning Paradigms for Speech Processing
Abstract
Over the past decades, a particular focus is given to research on machine learning techniques for speech processing applications. However, in the past few years, research has focused on using deep learning for speech processing applications. This ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Badges

Best Paper

Author Tags

Qualifiers

Research-article

Conference

CIKM '21

Sponsor:

CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

60
Total Citations
View Citations
1,234
Total Downloads

Downloads (Last 12 months)386
Downloads (Last 6 weeks)36

Other Metrics

View Author Metrics

Citations

Cited By

Ahmad MAli MHasan MMobo FRai S(2024)Geospatial Machine Learning and the Power of Python ProgrammingEthics, Machine Learning, and Python in Geospatial Analysis10.4018/979-8-3693-6381-2.ch010(223-253)Online publication date: 10-May-2024
https://doi.org/10.4018/979-8-3693-6381-2.ch010
Rösch MNolde MUllmann TRiedlinger T(2024)Data-Driven Wildfire Spread Modeling of European Wildfires Using a Spatiotemporal Graph Neural NetworkFire10.3390/fire70602077:6(207)Online publication date: 19-Jun-2024
https://doi.org/10.3390/fire7060207
Laidi RDjenouri DDjenouri YLin J(2024)TG-SPRED: Temporal Graph for Sensorial Data PREDictionACM Transactions on Sensor Networks10.1145/364989220:3(1-20)Online publication date: 13-Apr-2024
https://dl.acm.org/doi/10.1145/3649892
Liu MTu ZSu TWang XXu XWang Z(2024)BehaviorNet: A Fine-grained Behavior-aware Network for Dynamic Link PredictionACM Transactions on the Web10.1145/358051418:2(1-26)Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1145/3580514
Nagarajan SAsha Jerlin MDevarajan GSivakami RTiwari R(2024)Efficiency Improvisation of Large-Scale Knowledge Systems in Feature Determination using Proposed HVGAN ArchitectureJournal of Information & Knowledge Management10.1142/S021964922450006023:02Online publication date: 23-Jan-2024
https://doi.org/10.1142/S0219649224500060
Hart DMorse B(2024)Improving Graph Networks through Selection-based Convolution2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)10.1109/WACV57701.2024.00181(1783-1793)Online publication date: 3-Jan-2024
https://doi.org/10.1109/WACV57701.2024.00181
Skarding JGabrys BMusial K(2024)On the Effectiveness of Heterogeneous Ensembles Combining Graph Neural Networks and Heuristics for Dynamic Link PredictionIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.334392711:4(3250-3259)Online publication date: Jul-2024
https://doi.org/10.1109/TNSE.2023.3343927
Yin CJiang JWang QMao ZJing N(2024)DeltaGNN: Accelerating Graph Neural Networks on Dynamic Graphs With Delta UpdatingIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2023.333515343:4(1163-1176)Online publication date: Apr-2024
https://doi.org/10.1109/TCAD.2023.3335153
Baker JPasini MHauck C(2024)Invariant Features for Accurate Predictions of Quantum Chemical UV-vis Spectra of Organic MoleculesSoutheastCon 202410.1109/SoutheastCon52093.2024.10500060(311-320)Online publication date: 15-Mar-2024
https://doi.org/10.1109/SoutheastCon52093.2024.10500060
Ntekouli MSpanakis GWaldorp LRoefs A(2024)Exploiting Individual Graph Structures to Enhance Ecological Momentary Assessment (EMA) Forecasting2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW61823.2024.00027(158-166)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDEW61823.2024.00027
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents