1. COT 6398: Network Science Spring 2016 Mainak Chatterjee mainak@eecs.ucf.edu http://www.eecs.ucf.edu/~mainak/ http://www.eecs.ucf.edu/~mainak/COURSES/spr16/6938

2. The slides that I will be using for this class have been copied from open sources from the Web. I do not claim any intellectual property for the following material. I thank all other instructors for making the slides available and making my life easy. Disclaimers

3. Some real networks – Social Networks Networks of acquaintances Collaboration networks – actor networks, co-authorship networks, director networks Phone-call networks, e-mail networks, IM networks, Bluetooth networks Sexual networks Home page/blog networks – Information Networks Citation network (directed acyclic) The Web (directed) Peer-to-Peer networks Software graphs – Distribution Networks The Internet (router level, AS level) Power Grids, Telephone Networks Airline networks Transportation Networks (roads, railways, pedestrian traffic)

4. Some real networks…. – Biological Networks Protein-Protein Interaction Networks Gene regulation networks Gene co-expression networks Metabolic pathways The Food Web Neural Networks – Economic Networks Bank networks Credit card – Natural Networks River networks Leaf networks Epidemic

5. How do networks “look” like? Let’s look at some pictures… These will give you some idea of what we are dealing with. A word of caution – When was the data collected – How was the data collected (methodology) – Credibility of the experiments Nevertheless, the pictures tell a story

6. Terrorist Network

7. Actors (https://oracleofbacon.org/)https://oracleofbacon.org

8. Links among blogs (2004 presidential) election)

9. Product recommendations

10. Facebook The “Social Graph” behind Facebook

11. Technological networks Networks built for distribution of commodity – The Internet router level, AS level – Power Grids – Airline networks – Telephone networks – Transportation Networks roads, railways, pedestrian traffic

12. PoP-level Internet2 network

13. The Internet at AS level

14. Internet as measured by Hal Burch and Bill Cheswick's Internet Mapping Project.Internet Mapping Project

15. Routers

16. Power networks

17. transportation networks: airlines Source: Northwest Airlines WorldTraveler Magazine

18. transportation networks: railway maps Source: TRTA, March 2003 - Tokyo rail map

19. Biological networks Biological systems represented as networks – Protein-Protein Interaction Networks – Gene regulation networks – Gene co-expression networks – Metabolic pathways – The Food Web – Neural Networks

20. Citric acid cycle Metabolites participate in chemical reactions metabolic networks

21. Biochemical pathways (Roche) Source: Roche Applied Science, http://www.expasy.org/cgi-bin/show_thumbnails.pl

22. Gene regulatory networks GRN is a collection of DNA segments in a cell which interact with other indirectly (through their RNA and protein expression products) and with other substances in the cell, thereby governing the expressions levels of Messenger RNA (mRMA) and proteins. humans have 30,000 genes the complexity is in the interaction of genes can we predict what result of the inhibition of one gene will be? Source: http://www.zaik.uni-koeln.de/bioinformatik/regulatorynets.html.en

23. protein-gene interactions protein-protein interactions PROTEOME GENOME Citrate Cycle METABOLISM Bio-chemical reactions Bio map by L-A Barabasi - -- - _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _

24. Protein binding networks Baker’s yeast S. cerevisiae (only nuclear proteins shown) Nematode worm C. elegans

25. Transcription regulatory networks Bacterium: E. coli Single-celled eukaryote: S. cerevisiae

26. The Protein Network of Drosophila (a small fly) CuraGen Corporation Science, 2003

27. KEGG database: http://www.genome.ad.jp/kegg/kegg2.html Metabolic networks

28. C. elegans neurons

29. Network of Interacting Pathways (NIP) A.Mazurie D.Bonchev G.A. Buck, 2007 381 organisms

30. Freshwater food web by Neo Martinez and Richard Williams

31. Protein interaction networks

32. Ecological networks

33. Brain networks

34. Structural brain connectivity

35. Economic networks

36. World-trade networks

37. Email exchanges in a company

38. Phone calls in a country

39. Socio-epidemic networks

40. Epidemic networks

41. River networks

42. Leaf networks

43. Nodes: Links: http://ecclectic.ss.uci.edu/~drwhite/Movie Companies Investment Pharma Research Labs Public Biotechnology Collaborations Financial R&D Network Science: Introduction 2012 Business ties in bio-tech industry

44. Network Science: Introduction 2012 What message did the pictures convey? Enough of pictures

45. Network Science: Introduction 2012 We are surrounded by complex systems, from the society (a collection of 7 billion individuals, to communications systems, to neurons in the brain. All these systems work together in a seamless fashion. These systems, random looking at first, upon close inspection display endless signatures of order and self-organization whose quantification, understanding, prediction and eventually control is the major intellectual challenge. Bottomline….it’s the complexity

46. THE ROLE OF NETWORKS Behind each complex system there is a network (a wiring diagram), that defines the interactions between the component. We will never understand complex system unless we map out and understand the networks behind them. Network Science: Introduction 2012

47. “Complex” networks Complex networks are large (in node number) Complex networks are sparse (low edge to node ratio) Complex networks are usually dynamic and evolving Complex networks can be social, economic, natural, informational, abstract,... Isn’t this graph theory? – Yes, but emphasis is on data and mechanistic explanations...

48. Networks in complex systems Complex systems – Large number of components interacting with each other – All components and/or interactions are different from each other – Paradigms: 10 4 types of proteins in an organism, 10 6 routers in the Internet 10 9 web pages in the WWW 10 11 neurons in a human brain The simplest property: – who interacts with whom? can be visualized as a network Complex networks are just a backbone for complex dynamical systems

49. What is Network Science? The study of complex systems that can be represented as (typically dynamic) networks –Society, Economy –Various biological networks (e.g., metabolism) –Brain –WWW, Internet, Transportation nets –Natural networks (global climate system) –Many others Relies on: –Network data (strong empirical basis) –Network models –Network algorithms –Statistics of network data

50. Main components of Network Science Structural properties of complex networks –E.g., scale-free, small-world Dynamics of networks Dynamics on networks Dynamics of & on networks (co-evolutionary networks) How does structure affect dynamics? And how do dynamics affect structure?

51. The roots of Network Science Graph Theory Statistical Mechanics Nonlinear Dynamics Games and Learning Data mining (“graph mining”) and machine learning Algorithms Complexity theory

52. Applications of Network Science Social networks and social media Economic networks Biology Ecology Network medicine Climate science Brain Science and Neuroscience Web Internet and computer networks.. many others

53. Why study Network Science now? Network Science: Introduction 2012 The list of chemical reactions that take place in a cell were discovered over a 150 year period by biochemists and biologists. In the 1990s they were collected in central databases, offering the first chance to assemble the networks behind a cell. The list of actors that play in each movie were traditionally scattered in books and encyclopedias. With the advent of the Internet, these disparate data were assembled into a central database by imdb.com, mainly to feed the curiosity of movie aficionados. The database offered the first chance for network scientists to explore the structure of the affiliation network behind Hollywood. The detailed list of authors of millions of research papers were traditionally scattered in the table of content of thousands of journals, but recently the Web of Science, Google Scholar, and other sites assembled them into comprehensive databases, easing the search for scientific information. These databases turned into the first science collaboration maps.

54. Data Availability: Universality: The (urgent) need to understand complexity: THE EMERGENCE OF NETWORK SCIENCE Movie Actor Network, 1998; World Wide Web, 1999. C elegans neural wiring diagram 1990 Citation Network, 1998 Metabolic Network, 2000; The architecture of networks emerging in various domains of science, nature, and technology are more similar to each other than one would have expected. During the past decade some of the most important advances towards understanding complexity were provided in context of network theory. Network Science: Introduction 2012

55. Interdisciplinary: Cell biologists/Computer Scientists/others needs the wiring diagram behind their system, extracting information from incomplete and noisy data sets. Empirical, Data driven: Focus is on data and its utility. Not just mathematical models, but tools to test real data. The value addition will be judged by the insights it offers. Quantitative and Mathematical Nature: Graph theory; organizing principles from statistical physics, control and information theory, statistics and data mining. Computational Intensive: Size of networks giving rise to Big Data. Needs algorithms, database management, data mining, analytics, software tools. THE CHARACTERISTICS OF NETWORK SCIENCE Network Science: Introduction 2012

56. Impact of Network Science

57. Network Science: Introduction January 10, 2011 Network Science: Introduction 2012

58. The 2003 blackout is a typical example of a cascading failure. When a network acts as a transportation system, a local failure shifts loads or responsibilities to other nodes. If the extra load is negligible, the rest of the system can seamlessly absorb it, and the failure remains effectively unnoticed. If the extra load is too much for the neighboring nodes to carry, they will either tip or again redistribute the load to their neighbors. Either way, we are faced with a cascading event, the magnitude and reach of which depend on the centrality and capacity of the nodes that have been removed in the first round. Case in point is electricity: As it cannot be stored, when a line goes down, its power must be shifted to other lines. Most of the time the neighboring lines have no difficulty carrying the extra load. If they do, however, they will also tip and redistribute their increased load to their neighbors. Cascading failures are common in most complex networks. They take place on the Internet, when traffic is rerouted to bypass malfunctioning routers, occasionally creating denial of service attacks on routers that do not have the capacity to handle extra traffic. We witnessed one in 1997, when the International Monetary Fund pressured the central banks of several Pacific nations to limit their credit. And they are behind the 2009-2011 financial meltdown, when the US credit crisis paralyzed the economy of the globe, leaving behind scores of failed banks, corporations and even bankrupt states, like Greece. Example: Cascading Failures

59. Network Science: Introduction 2012 American effort to dry up the money supply of terrorist organizations is aimed at crippling terrorist networks. Doctors and researchers hope to induce cascading failures to kill cancer cells. Cascading can also help

60. How come we see relevant ads when we browse? All rely on networks – connections that we have with products/ people/things/etc. ECONOMIC IMPACT – Business exploiting user data Network Science: Introduction 2012

61. In September 2010 the National Institutes of Health awarded $40 million to researchers at Harvard, Washington University in St. Louis, the University of Minnesota and UCLA, to develop the technologies that could systematically map out brain circuits. The Human Connectome Project (HCP) with the ambitious goal to construct a map of the complete structural and functional neural connections in vivo within and across individuals. http://www.humanconnectomeproject.org/overview/ BRAIN RESEARCH Network Science: Introduction 2012

62. If you were to understand the spread of diseases, can you do it without networks? If you were to understand the WWW structure, searchability, etc, hopeless without invoking the Web’s topology. If you want to understand human diseases, it is hopeless without considering the wiring diagram of the cell. MOST IMPORTANT Networks Really Matter Network Science: Introduction 2012

63. Course Logistics Second time Network Science is being offered at UCF –First time in Fall 2014 Relatively new: Very few universities offer Network Science –Course website lists a few

64. Course Website The course website: http://eecs.ucf.edu/~mainak/COURSES/spr16/6938/ http://eecs.ucf.edu/~mainak/COURSES/spr16/6938/ Linked from my “Teaching” page Instructor: Mainak Chatterjee Office: HEC 305 Office hours: Tue/Thu 3:00 – 4:30 PM or by appointments TA: None Email: mainak@eecs.ucf.edu (Do NOT use the webcourse email)mainak@eecs.ucf.edu

65. Books A.Barabasi, Network Science http://barabasilab.neu.edu/networksciencebook/ M.E.J. Newman, Networks: An Introduction, Oxford University Press, 2010. Not available online. D. Easley and J. Kleinberg, Networks, Crowds and Markets, Cambridge Univ Press, 2010. http://www.cs.cornell.edu/home/kleinber/networks-book/

66. Topics to be covered…this is what I intend to teach Graph definitions, paths, components, degree distribution, clustering, degree correlations, centrality metrics, small-world property, scale-free property, heavy-tailed degree distributions, network motifs, Poisson networks, Watts-Strogatz model, preferential attachment and its variants, applications in communications and social networks, community identification and detection algorithms, percolation, vulnerabilities, resilience to random and targeted attacks, epidemics, immunization strategies, influence identification, games on networks, strategic network formation, evolution due to cooperation and non-cooperation on social networks.

67. Grading 3 Assignments (3 X 15) = 45 Points These will be programming assignments and analytical questions. 1 Mid-term exam = 30 points Sometime in Mid April Topic review = 25 points Requires 2 presentations (2 and 10 minutes)

68. Helps if you know Graph Theory –Basic definitions and properties Probability theory –Distribution functions (Binomial, Poisson) Linear Algebra –Eigenvalues and eigenvectors A bit of differential equations Programming knowledge (any language)

69. Project Review You will read at least 3-4 papers or book chapters on a particular topic. You will make a 2-min presentation where you let the class know the topic that you would be reviewing and the set of papers that you would be reading. Final Presentation: What is the topic/problem that you have reviewed? How has it been studied? What are the technical approaches? You will have to present the details of these approaches so make sure you understand them well. What kind of data sets were used? Was data collected via experiments? If so, what was the methodology? Again, you will have to provide details. What insights do the analysis/results provide? If you were to investigate this problem, how would you go about doing so? Can this line of research be extended beyond the current understanding? If so, how?

70. http://eecs.ucf.edu/~mainak/COURSES/spr16/6938/ Email: mainak@eecs.ucf.edu