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Networks, Deep Learning (and COVID-19)

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Networks, Deep Learning (and COVID-19)

  1. 1. Networks, Deep Learning (and COVID-19) Tsuyoshi Murata Department of Computer Science School of Computing Tokyo Institute of Technology murata@c.titech.ac.jp http://www.net.c.titech.ac.jp/ The Tenth Workshop on Computation: Theory and Practice (WCTP 2020) Nov. 21(Sat) 2020
  2. 2. Table of contents • Networks (graphs) • Networks + Deep Learning • Networks + Deep Learning + COVID-19 2
  3. 3. Table of contents • Networks (graphs) • Networks + Deep Learning • Networks + Deep Learning + COVID-19 3
  4. 4. Networks (or graphs) • a set of vertices and edges • many objects in physical, biological, and social sciences can be thought of as networks 4 social networks metabolic networks food web “graph” and “network” are often used interchangeably
  5. 5. Understanding/analyzing networks metrics models processes algorithms
  6. 6. Understanding/analyzing networks metrics models processes algorithms path length, density, diameter, degree distribution, clustering coefficient, … Dijkstra's algorithm, graph partitioning, centrality computation, … random network, scale- free network, small-world network, power law, configuration model, … rumor/disease diffusion, influence maximization / minimization, SI model, SIR model, …
  7. 7. Topics • Community detection • Link prediction • Centrality (ranking) • Influence maximization • … 7 https://link.springer.com/article/10.1007/s11042-020-08700-4 https://www.nature.com/articles/s41598-019-57304-y https://www2.slideshare.net/tom.zimmermann/changes-and- bugs-mining-and-predicting-development-activities/19- CentralityDegree_Closeness_BetweennessBlue_binary_has https://link.springer.com/referenceworkent ry/10.1007%2F978-1-4939-7131-2_110197 Networks can be huge, incomplete, noisy, directed, weighted, signed, temporal, …
  8. 8. Community detection in signed networks 8 • two types of edges: friendship and hostility • Detection of nested communities (which often appears in real social networks) Tsuyoshi Murata, Takahiko Sugihara, and Talel Abdessalem, "Community Detection in Signed Networks Based on Extended Signed Modularity", Proceedings of the 8th Conference on Complex Networks (CompleNet 2017), Springer, 2017.
  9. 9. Transductive classification on heterogeneous networks • the labels of some vertices are given -> classify the labels of the remaining vertices 9 Phiradet Bangcharoensap, Tsuyoshi Murata, Hayato Kobayashi, Nobuyuki Shimizu, “Transductive Classification on Heterogeneous Information Networks with Edge Betweenness-based Normalization”, Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM2016), pp.437-446, 2016.
  10. 10. Influence maximization in dynamic networks • finding a set of nodes that will propagate information most in given social networks 10 Tsuyoshi Murata and Hokuto Koga, "Approximation Methods for Influence Maximization in Temporal Networks", Chapter 18, In: Petter Holme and Jari Saramaki (eds.), "Temporal Network Theory", pp.345- 368, Springer, 2019.
  11. 11. Detecting Communities of Distant Members 11 Xin Liu, Tsuyoshi Murata, Ken Wakita, "Detecting network communities beyond assortativity-related attributes", Physical Review E 90, 012806, 2014. Paulo Shakarian, Patrick Roos, Devon Callahan, Cory Kirk, "Mining for Geographically Disperse Communities in Social Networks by Leveraging Distance Modularity", KDD2013. • Our method was used for detecting terrorist networks by the researchers of U.S. Military Academy
  12. 12. Reference (Networks) • Networks (second edition), Mark Newman, Oxford University Press, 2018. https://global.oup.com/academic/product/net works-9780198805090
  13. 13. Table of contents • Networks (graphs) • Networks + Deep Learning • Networks + Deep Learning + COVID-19 13
  14. 14. Deep Learning Image recognition Voice recognition Natural Language Processing 14
  15. 15. Convolutional neural networks • Recognizing local features -> global features https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53 15
  16. 16. Convolution works for images, sentences, and networks • Images: grid of pixels • Sentences: sequences of words • Networks: – Number of neighbors is not fixed – Topologically complex – Vertices are not ordered 16
  17. 17. Graph Neural Networks • Learning features of vertices using their neighbors GNN Classification 17 gender, age, job, income, … https://edition.cnn.com/style/article/why-democrats- are-donkeys-republicans-are-elephants-artsy/index.html ? ? or
  18. 18. Machine learning tasks • classification • regression • clustering • dimensionality reduction regression f(x)=ax3+… clustering group 1 group 2 dimensionality reduction classification 18
  19. 19. Machine learning tasks for graphs • Node classification • Graph classification • Link prediction • Graph generation model for generating graphs 19
  20. 20. Applications of Graph Neural Networks • Computer vision – scene graph generation (input : images, output: objects and semantic relations) – realistic image generation (input: scene graph, output: images) • Recommender systems – recommendation as link prediction (input: items & users, output: missing links) • Traffic – Forecast of traffic speed (input: sensors on roads and the distances, output: traffic speed and volume) • Chemistry – classification of molecular graphs (atoms = nodes, bonds = edges) Graph Neural Networks: A Review of Methods and Applications https://arxiv.org/abs/1812.08434 20
  21. 21. DeepMind article (Sept. 2020) • Traffic prediction with advanced Graph Neural Networks – https://deepmind.com/blog/article/traffic- prediction-with-advanced-graph-neural-networks 21
  22. 22. GNN for traffic prediction • Segmenting roads as graphs 22
  23. 23. Learning Community Structure with Variational Autoencoder • Variational autoencoder (VAE) : generative models for the classification of similar synthetic entities • Variational graph autoencoder (VGAE) : the extension of VAE to graph structures • Variational Graph Autoencoder for Community Detection (VGAECD) : encodes graph structures with multiple Gaussian distributions corresponding to each of the communities 23 Jun Jin Choong, Xin Liu, Tsuyoshi Murata, "Learning Community Structure with Variational Autoencoder", Proceedings of IEEE ICDM 2018 (IEEE International Conference on Data Mining), pp.69-78, November, 2018.
  24. 24. Fast Approximations of Betweenness Centrality using Graph Neural Networks • A novel GNN for approximating centrality – aggregation is done separately for incoming and outgoing paths – Node’s own features are not aggregated – Nodes with no shortest paths are identified and corresponding rows in A and AT are set to zero 24 Sunil Kumar Maurya, Liu Xin, Tsuyoshi Murata, "Fast approximations of betweenness centrality with Graph Neural Networks", Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM 2019), pp.2149-2152, 2019.
  25. 25. Linear Graph Convolutional Model for Diagnosing Brain Disorders 25 • fMRI data -> brain network -> population graph of similar patients Zarina Rakhimberdina, Liu Xin, Tsuyoshi Murata, "Population Graph-based Multi-Model Ensemble Method for Diagnosing Autism Spectrum Disorder“, Sensors, Vol.20, No.21, 18 pages, 2020.
  26. 26. Reference (Graph Neural Networks) • “Graph Neural Networks: Models and Applications” (tutorial of AAAI 2020) • https://cse.msu.edu/~mayao4/tutorials/aaai2 020/ 26
  27. 27. Table of contents • Networks (graphs) • Networks + Deep Learning • Networks + Deep Learning + COVID-19 27
  28. 28. Table of contents • Networks (graphs) • Networks + Deep Learning • Networks + Deep Learning + COVID-19 – Networks + COVID-19 – Networks + Deep Learning + COVID-19 28
  29. 29. Table of contents • Networks (graphs) • Networks + Deep Learning • Networks + Deep Learning + COVID-19 – Networks + COVID-19 – Networks + Deep Learning + COVID-19 29
  30. 30. Network modeling for epidemics • disease spread 30
  31. 31. Network modeling for epidemics • disease spread 31 network # of infected people prob. of infection prob. of recovery
  32. 32. SIR model • S : susceptible • I : infected • R: recovered (or removed) 32 S I R β γ 𝛾𝛾𝛾𝛾𝛾𝛾 1-𝛾𝛾𝛾𝛾𝛾𝛾 S I R NDlib - Network Diffusion Library https://ndlib.readthedocs.io/en/latest/index.html
  33. 33. SIR model • three states – Susceptible (S) : not infected – Infected (I) – Recovered (removed) (R) • It makes little difference to the disease whether a person is immune or dead • 𝜏𝜏 : the length of time that infected individual is likely to remain infected before they recover • 𝛾𝛾𝛾𝛾𝜏𝜏 : probability of recovering in time interval 𝛿𝛿𝛿𝛿 • 1 − 𝛾𝛾𝛾𝛾𝛾𝛾 : probability of not doing so • Probability that the individual is still infected after time 𝜏𝜏 : lim 𝛿𝛿𝑡𝑡→0 1 − 𝛾𝛾𝛾𝛾𝛾𝛾 ⁄𝜏𝜏 𝛿𝛿𝛿𝛿 = 𝑒𝑒−𝛾𝛾𝛾𝛾 • Probability 𝑝𝑝 𝜏𝜏 𝑑𝑑𝜏𝜏 that the individual remains infected for and then recovers between 𝜏𝜏 and 𝜏𝜏 + 𝑑𝑑𝜏𝜏 : 𝑝𝑝 𝜏𝜏 𝑑𝑑𝜏𝜏 = 𝛾𝛾𝑒𝑒−𝛾𝛾𝛾𝛾 𝑑𝑑𝜏𝜏 S I R recovery and death β γ 𝛾𝛾𝛾𝛾𝛾𝛾 1-𝛾𝛾𝛾𝛾𝛾𝛾 Exponential distribution: some might remain in I state for a long time not realistic for most real disease
  34. 34. Equations for the SIR model • 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 = −𝛽𝛽𝑠𝑠𝑠𝑠 • 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 = 𝛽𝛽𝑠𝑠𝑠𝑠 − 𝛾𝛾𝑥𝑥 • 𝑑𝑑𝑟𝑟 𝑑𝑑𝑑𝑑 = 𝛾𝛾𝑥𝑥 • 𝑠𝑠 + 𝑥𝑥 + 𝑟𝑟 = 1 • Eliminate x : 1 𝑠𝑠 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 = − 𝛽𝛽 𝛾𝛾 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 • Integrate both sides with respect to t : 𝑠𝑠 = 𝑠𝑠0 𝑒𝑒 ⁄−𝛽𝛽𝑟𝑟 𝛾𝛾 • Put this equation and x = 1 − 𝑠𝑠 − 𝑟𝑟 : 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 = 𝛾𝛾� � 1 − 𝑟𝑟 − 𝑠𝑠0 𝑒𝑒 ⁄−𝛽𝛽𝑟𝑟 𝛾𝛾 • 𝑡𝑡 = 1 𝛾𝛾 ∫0 𝑟𝑟 𝑑𝑑𝑑𝑑 1−𝑟𝑟−𝑠𝑠0 𝑒𝑒 ⁄−𝛽𝛽𝑟𝑟 𝛾𝛾 S I R 𝑠𝑠 β γ 𝑥𝑥 𝑟𝑟 S R I Time evolution of the SIR model 𝛽𝛽 = 1, 𝛾𝛾 = 0.4, 𝑠𝑠0 = 0.99, 𝑥𝑥0 = 0.01, 𝑟𝑟0 = 0
  35. 35. Time evolution of the SIR model • S decreases / R increases monotonically • S does not go to zero (because no I left as 𝑡𝑡 → ∞) • R: total size of the outbreak • 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑 = 𝛾𝛾 1 − 𝑟𝑟 − 𝑠𝑠0 𝑒𝑒 ⁄−𝛽𝛽𝑟𝑟 𝛾𝛾 = 0 • 𝑟𝑟 = 1 − 𝑠𝑠0 𝑒𝑒 ⁄−𝛽𝛽𝑟𝑟 𝛾𝛾 • Initial condition: – c infected and n-c susceptible – 𝑠𝑠0 = 1 − ⁄𝑐𝑐 𝑛𝑛 , 𝑥𝑥0 = ⁄𝑐𝑐 𝑛𝑛 , 𝑟𝑟0 = 0 – When 𝑛𝑛 → ∞, 𝑠𝑠0 ≅ 1 • 𝑟𝑟 = 1 − 𝑒𝑒 ⁄−𝛽𝛽𝑟𝑟 𝛾𝛾 S R I Time evolution of the SIR model 𝛽𝛽 = 1, 𝛾𝛾 = 0.4, 𝑠𝑠0 = 0.99, 𝑥𝑥0 = 0.01, 𝑟𝑟0 = 0 Size of the giant component of a Poisson random graph 𝑐𝑐 = ⁄𝛽𝛽 𝛾𝛾 cS eS − −=1 size
  36. 36. Size of epidemics • If 𝛽𝛽 ≤ 𝛾𝛾 there is no epidemic – 𝐼𝐼 → 𝑅𝑅 is faster than 𝑆𝑆 → 𝐼𝐼 Sy = cS ey − −=1cS eS − −=1 no giant component 0=S 0>S transition between two regimes 1)1( =− −cS e dS d 1=−cS ce 10 =→= cS cS eS − −=1 γβ=c S I R β γ Epidemic transition 𝛽𝛽 = 𝛾𝛾
  37. 37. Basic reproduction number • The average number of additional I people – If each I person passes disease to two others on average, then 𝑅𝑅0 = 2 → disease will grow exponentially – If 𝑅𝑅0 = ⁄1 2 → disease will die exponentially – If 𝑅𝑅0 = 1 → epidemic threshold (𝛽𝛽 = 𝛾𝛾) S I R 𝑠𝑠 β γ 𝑥𝑥 𝑟𝑟
  38. 38. Modelling COVID-19 epidemic in Italy • SIDARTHE model 38 Giordano, G., Blanchini, F., Bruno, R. et al. "Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy", Nature Medicine Vol.26, pp.855–860 (2020). https://doi.org/10.1038/s41591-020-0883-7 diagnosednot diagnosed severe mild
  39. 39. Growth of COVID-19 patients • Power-law curves between countries are highly correlated 39 Cesar Manchein, Eduardo L. Brugnago, Rafael M. da Silva, Carlos F. O. Mendes, and Marcus W. Beims, "Strong Correlations Between Power-law Growth of COVID-19 in Four Continents and the Inefficiency of Soft Quarantine Strategies", Chaos Vol.30, No.041102 pp.1-7, 2020. https://doi.org/10.1063/5.0009454 theoretically: exponential 𝑦𝑦 = 𝑥𝑥 𝑘𝑘 http://maps.unomaha.edu/maher/ GEOL2300/week10/exp.html actually : power law 𝑦𝑦 = 𝑘𝑘 𝑥𝑥
  40. 40. Effect of travel restrictions • Evaluating travel ban by computer simulation • Wuhan travel ban was effective for preventing COVID-19 outside of China, although it was not effective inside of China (already diffused) 40 Matteo Chinazzi, Jessica T. Davis, Marco Ajelli, Corrado Gioannini, Maria Litvinova, Stefano Merler, Ana Pastore y Piontti, Kunpeng Mu, Luca Rossi, Kaiyuan Sun, Cécile Viboud, Xinyue Xiong, Hongjie Yu, M. Elizabeth Halloran, Ira M. Longini Jr., Alessandro Vespignani, "The Effect of Travel Restrictions on the Spread of the 2019 Novel Coronavirus (COVID-19) Outbreak“, Science 24 Apr 2020, Vol. 368, Issue 6489, pp. 395-400, 2020. https://doi.org/10.1126/science.aba9757
  41. 41. Network analysis of genomes 41 Peter Forstera, Lucy Forster, Colin Renfrew, and Michael Forster, "Phylogenetic Network Analysis of SARS-CoV-2 Genomes“, PNAS, Vol.117, No.17, pp.9241-9243, 2020 https://doi.org/10.1073/pnas.2004999117 A BC Bat Europe and America East Asia • Phylogenetics: for the inference of the evolutionary history and relationships among groups of organisms • Three variants • Virus mutation emerges in two different hosts
  42. 42. Table of contents • Networks (graphs) • Networks + Deep Learning • Networks + Deep Learning + COVID-19 – Networks + COVID-19 – Networks + Deep Learning + COVID-19 42
  43. 43. Graph Representation Learning and Beyond (GRL+) • A workshop collocated with International Conference on Machine Learning (ICML 2020) – https://grlplus.github.io/covid19/ • “Graph Methods for COVID-19 Response” William L. Hamilton (McGill University/Mila) – https://grlplus.github.io/files/graphs- against-covid.pdf 43
  44. 44. “Graph Methods for COVID-19 Response” • Three key types of data – Biomedical treatment data – Epidemiological network data – Supply chain networks • heterogeneous and relational structures – Computational drug design – Computational treatment design – Epidemiological forecasting – Demand forecasting and supply chain optimization – Outbreak tracking and tracing 44 William L. Hamilton, "Graph Methods for COVID-19 Response", https://grlplus.github.io/files/graphs- against-covid.pdf
  45. 45. Computational drug design • Can we design better antivirals to target COVID-19? • Sub-problem 1: Molecule representation and property prediction • Sub-problem 2: Molecule generation and search – How can we generate molecules that have particular properties? How can we effectively search over the space of possible molecules? 45 possibility of application of GNNs (still open challenge) William L. Hamilton, "Graph Methods for COVID-19 Response", https://grlplus.github.io/files/graphs- against-covid.pdf
  46. 46. Computational treatment design • Can we design better treatment strategies using existing drugs? • Approach 1: structure-based – similar to computational drug design • Approach 2: network-based – Leverage knowledge of biological interactions between drugs, diseases, and proteins 46 William L. Hamilton, "Graph Methods for COVID-19 Response", https://grlplus.github.io/files/graphs- against-covid.pdf
  47. 47. Epidemiological forecasting • Can we better predict how and where infection rate will change over time? 47 William L. Hamilton, "Graph Methods for COVID-19 Response", https://grlplus.github.io/files/graphs- against-covid.pdf
  48. 48. Demand forecasting and supply chain optimization • Can we forecast COVID-19 related demands to optimize supply chains? 1. Heterogeneous relational data 2. Temporal information and changes 3. Node-level predictions -> spatio-temporal GNNs are useful 48 William L. Hamilton, "Graph Methods for COVID-19 Response", https://grlplus.github.io/files/graphs- against-covid.pdf
  49. 49. Outbreak tracking and tracing • Can we model and predict infection risk at the individual level? 49 William L. Hamilton, "Graph Methods for COVID-19 Response", https://grlplus.github.io/files/graphs- against-covid.pdf
  50. 50. “COVID-19 and Networks” • an article in Journal of the Japanese Society for Artificial Intelligence (written in Japanese) Tsuyoshi Murata, “COVID-19 and Networks”, Journal of JSAI, Vol.35, No.5, pp.654-660, 2020 http://id.nii.ac.jp/1004/00010709/ 50
  51. 51. Table of contents • Networks (graphs) • Networks + Deep Learning • Networks + Deep Learning + COVID-19 51
  52. 52. Networks, Deep Learning (and COVID-19) Tsuyoshi Murata Department of Computer Science School of Computing Tokyo Institute of Technology murata@c.titech.ac.jp http://www.net.c.titech.ac.jp/ The Tenth Workshop on Computation: Theory and Practice (WCTP 2020) Nov. 21(Sat) 2020 slides available from here

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