Improving Network Digital Twins through Data-centric AI

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In recent years, the networking community has produced robust Graph Neural Network (GNN) architectures that can accurately mimic complex network environments. Modern GNN-based architectures enable building lightweight and accurate Network Digital Twins that can operate in real time. However, the quality of Machine Learning-based models depends on two main components: the model architecture, and the training dataset. In this context, very little research has been done on the impact of training data on the performance of these network models.

This talk will introduce the Graph Neural Networking challenge 2022, entitled “Improving Network Digital Twins through Data-centric AI”. In this problem statement, participants will be given a state-of-the-art GNN model for network performance evaluation, and an accurate packet-level network simulator to generate training datasets. They will be tasked with producing a training dataset that results in better performance for the target GNN model.

This live event includes a 30-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.

Panelists
Resources

Graph Neural Networking challenge 2022: Improving Network Digital Twins through Data-centric AI

For important information regarding the classification, please go to the Division’s website and review the last two questions in the Q&A page. Please be advised that the utilization of this list by AI for Good is exclusively for the purpose of ticketing for the 2024 AI for Good Global Summit, unless otherwise specified

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Libya	LY	LBY	Developing
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Western Sahara	EH	ESH	Developing
British Indian Ocean Territory	IO	IOT	Developing
Burundi	BI	BDI	Developing
Comoros	KM	COM	Developing
Djibouti	DJ	DJI	Developing
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Ethiopia	ET	ETH	Developing
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Kyrgyzstan	KG	KGZ	Developing
Tajikistan	TJ	TJK	Developing
Turkmenistan	TM	TKM	Developing
Uzbekistan	UZ	UZB	Developing
China	CN	CHN	Developing
China, Hong Kong Special Administrative Region	HK	HKG	Developing
China, Macao Special Administrative Region	MO	MAC	Developing
Democratic People’s Republic of Korea	KP	PRK	Developing
Mongolia	MN	MNG	Developing
Brunei Darussalam	BN	BRN	Developing
Cambodia	KH	KHM	Developing
Indonesia	ID	IDN	Developing
Lao People’s Democratic Republic	LA	LAO	Developing
Malaysia	MY	MYS	Developing
Myanmar	MM	MMR	Developing
Philippines	PH	PHL	Developing
Singapore	SG	SGP	Developing
Thailand	TH	THA	Developing
Timor-Leste	TL	TLS	Developing
Viet Nam	VN	VNM	Developing
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Bangladesh	BD	BGD	Developing
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India	IN	IND	Developing
Iran (Islamic Republic of)	IR	IRN	Developing
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Pakistan	PK	PAK	Developing
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Azerbaijan	AZ	AZE	Developing
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Iraq	IQ	IRQ	Developing
Jordan	JO	JOR	Developing
Kuwait	KW	KWT	Developing
Lebanon	LB	LBN	Developing
Oman	OM	OMN	Developing
Qatar	QA	QAT	Developing
Saudi Arabia	SA	SAU	Developing
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Syrian Arab Republic	SY	SYR	Developing
Turkey	TR	TUR	Developing
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Yemen	YE	YEM	Developing
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Micronesia (Federated States of)	FM	FSM	Developing
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Niue	NU	NIU	Developing
Pitcairn	PN	PCN	Developing
Samoa	WS	WSM	Developing
Tokelau	TK	TKL	Developing
Tonga	TO	TON	Developing
Tuvalu	TV	TUV	Developing
Wallis and Futuna Islands	WF	WLF	Developing

Improving Network Digital Twins through Data-centric AI