A Practical Tutorial on Graph Neural Networks

Ward, Isaac Ronald; Joyner, Jack; Lickfold, Casey; Guo, Yinglu; Bennamoun, Mohammed

doi:10.48550/arxiv.2010.05234

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Types of encoder-decoder architectures, combined with an attention mechanism [604], are furthermore naturally suited for modelling time series [605] and sequential data, offering state of the art performance in the space of natural-language processing. Finally, the development of graph neural networks further accelerated progress in developing general AI architectures for handling unstructured and non-Euclidean data [606].…”

Section: Deep Learningmentioning

confidence: 99%

Social physics

Jusup,

Holme,

Kanazawa

et al. 2021

Preprint

View full text Add to dashboard Cite

Recent decades have seen a rise in the use of physics-inspired or physicslike methods in attempts to resolve diverse societal problems. Such a rise is driven both by physicists venturing outside of their traditional domain of interest, but also by scientists from other domains who wish to mimic the enormous success of physics throughout the 19 th and 20 th century. Here, we dub the physics-inspired and physics-like work on societal problems "social physics", and pay our respect to intellectual mavericks who nurtured the field to its maturity. We do so by comprehensively (but not exhaustively) reviewing the current state of the art. Starting with a set of topics that pertain to the modern way of living and factors that enable humankind's prosperous existence, we discuss urban development and traffic, the functioning of financial markets, cooperation as a basis for civilised life, the structure of (social) networks, and the integration of intelligent machines in such networks. We then shift focus to a set of topics that explore potential threats to humanity. These include criminal behaviour, massive migrations, contagions, environmental problems, and finally climate change. The coverage of each topic is ended with ideas for future progress. Based on the number of ideas laid out, but also on the fact that the field is already too big for an exhaustive review despite our best efforts, we are forced to conclude that the future for social physics is bright. Physicists tackling societal problems are no longer a curiosity, but rather a force to be reckoned with, yet for reckoning to be truly productive, it is necessary to build dialog and mutual understanding with social scientists, environmental scientists, philosophers, and more.

show abstract

Section: Deep Learningmentioning

confidence: 99%