MIS 644 Social Network Analysis Chapter 1 Introduction 2017/2018 Spring

1. MIS 644 Social Network Analysis Chapter 1 Introduction 2017/2018 Spring

2. Outline Networks Why to study networks Examples of networks Properties of networks Outline of the course

3. Networks • Collection of objects in which some pairs are connected by links • Verticies or nodes actors in SNA • Links adges

4. A Network

5. Why to study networks • Individual parts or components linked by some way • Examples • Internet: collection of computers linked by communication links • social networks: people by friendship relations • Individual components • How a computer works • How a humen being feals or acts • Connections or interactions • Communication protocoles TCP/IP • Dynamics of human friendship – social psychology

6. Third approach (1) • Patterns of connections • Network - simplified reprsentation abstraction • components - nodes • connections - links • Patterns of interactions • affects behavior of systems • e.g.: connection patterns – Internet • affect – routers, efficiency of tranfering data • e.g.:: in social networks • how people learn, form opinions or gether news • spread of disease • Structure - behavior

7. Third approach (2) • Verious disiplines • Mathematics, computer science, statistics, sociology, economics, ... • analysing, modeling and understanding networks • Many tools – simple network representation • verticies and edges • e.g.: • Which is the best connected vertex • Length of path from one vertex to another • Network models – how links are formed • Processes on networks - Predictions about: • The way trafic flow over Internet • The way diseases spread through a community

8. Third approach (3) • Not always works • Depens on • What the system is and does • Specific questions to be answered • Networks are • general and powerful • way of representing • patterns of connection and interraction of components

9. Examples • A classification by N-N • Technological networks • Social and economic networks: • Information networks • Biological netwoks

10. Technological Networks • Internet • Electrical power grids • Phone networks • Transport networks: • Roads, airline connection between airports, rail roads and subway networks

11. The Internet • Vertecies – computers, rooters • Edges – physical data connections • optical fibers or telephone lines • Man made, carefully engineered • Its structiure is not known exactly • Many different groups, little centrilized control • See Figure 1.1 of N-N • Why study the network structure of the Internet? • How to choose a route? • Is the shortest route the fastest? • How to avoid buttlenecks in the trafic flow? • What happens when a vertex or edge fails? • Where to add new capacity? • Development of new standards or protocoles

12. Network Structure of the Internet

13. Information Networks • Logical connections • www • E-mail • Phone calls • Nodes: telephone number • Directed or weihted links: number of calls from A to B • Trust network • Link: one accepts other public key • Citations • Social media

14. Web • Network of information stored in Web pages • Vertcies – web pages • Edges – hyperlinks - software constract • Content structure – link structure • Useful page gets many links • Web – directed network • Run in a specific direction: from one page to another • Dificulty: many pages are dynamically generated

15. Information Networks • www: links among Web pages • How pages are related • How groups of communities • Which pages are more prominent or important • Search engines - Google • Network structure - evaluate • quality and relevance of web pages • Not number of links it receives • A page is prominent if receives link from prominent pages

18. Social Networks • Collection of social ties among friends • A network of people or group of people such as firms • Vertecies - people or groups (organizations) • Edges – connections of some kind • friendship or acquaintances , working relations, sexual • business relations • Sociology • Increase in complexity due to • travel, technological advences, global communication, digital interaction • Exampe: Actor network • Wayne Zachary’s karate club - small 34 members • Figure 1.2 of N-N • Facebook or MS Instant messaging – large data

20. the pattern of emailcommunication among 436 employees of Hewlett Packard Research Lab

21. Economic Netwoks • Special types of social networks • Vertecies- firms, countries, industries • Edges – trading relationships between companies or countries- • Global manifacturing operations – network of suppliers • Media companies – network of advertizers • Spread of local breakdown to • Cascading failures or financial crisis

22. The network of loans among fnancial institutions

23. a network representing international trade

24. Biological Networks • Logical abstract • Interractions between • genes and/or proteins • molecules in the cells metabolic pathways • Interractions physical but links not • Possibility of inerraction molecules • Food web: Ecological, predetor-pray networks • Vertecies – species • Directed Links – predation of one by onother • Physical: • nervous system, neurons in the brain • blood vessels

27. How do we reason about networks? • Empirical: Study network data to find organizational principles • How do we measure and quantify networks? • Mathematical models: graph theory and statistical models • Models allow us to understand behaviors and distinguish surprising from expected phenomena • Algorithms for analyzing graphs • Hard computational challenges

28. Properties of Networks • Structure • Behavior • Dynamics • On networks – processes on .- given structure. • Of networks – evolution/coevolution

29. Structure • How to investigate? • Visualization – small or medium size networks with relatively sparse links • Measures or metrics • e.g.: centrality: • Quantifies how important vertices are • Degree of a node: number of edges attached to a node • Hubs

30. Some Topics • Structure of netowks • Small World Phenomena • Community Detection

31. Structure • A language for structural features of networks • Complxities • Parts that are more or less densely interconnected • Central cores • Participants can be more central or peripherial

32. Hubs: vertices with unusually high degree • Observed in many networks • Algorithms for • analyzing and understaning • network data • Measurements of network properties

33. Small world effect • Milgram experiment • 6 degrees of seperation • Internet • Computer are few hops away

34. Clusters or Communities in Networks • Social networks – break down into subcommunities • Knit friends or acquantions • Business relationships of companies • Clustered sets • Community detection techniques

35. the network structure of political blogs prior to the 2004 U.S. Presidentialelection

36. Behavior and Dynamics • Structure – starting point • Connectedness at the level of structure • Who is linked to whom • At the level of behavior • Individual’s actions – consequenses • Outcomes of anyone

37. Processes on Networks • Failure and resilience • Diffusion epidemics • Search

38. Dynamics on and of networks • coevolution of networks • structure – proceses on networks

39. Network Dynamics – Structural Effects • How people influence each other • Taking into acount structure of the network • The underlaying mechanisms • İnformation and • population level • Local level - network • İn the structure links friends • Aligning behavior with immediate friends rather then population as a whole

40. Search • The way people explore social contact for information • Effectiveness of performing these tasks – structure of networks

41. Outline of the course • Prerequisites for MIS 644 • Work load

42. Prerequisits for MIS 644 • elementary knowledge of linear algebra • matrices, tranpose, inverse of a matrix, eigen values and vectors • calculus • elementary probability and statistics • basic knowledge of algorithms • preferentially experinece with a programming language • some experience in data processing and manipulation

43. Work Load • Homeworks • Project • mid reports • final report • presentation • Paper presentations • mostly applied papers • network model of a online social network • Software evaluation • examination and evaluation of a software • (programable softare not software witha GUI only)

44. Course Structure • Basic tools • Network structure • Network models • Statistical analysis of networks – ERGMs • Processes on networks

45. Basic Tools and Stgructure • basic theoretical tools used to • describe and analyze networks, • graph theory: • measures and metrics for quantifying networkstructure • patterns revealed in real-world networks • mathematics and metrics • commnity detection

46. Network Models • Mathematical modelsof networks • Random graphs and extnsions • Small-world model • Network growth models • preferntial atacments and extensions • exponential random graph models • game theretic models

47. Detailed Course Outlines • Introduction to networks • Course overview • Basic Concepts • Graph theory • Verious types of networks • Metrics • path, components, • Centrality metrics • degree, betweenness, closeness, • eigen value, • Clique, clusters • Paths, components, degree distribution, clustering, degree correlations (assortativity) • Metrics for weighted spatial networks • Properties of real networks

48. Properties of real networks • Shortest paths and small-world property • Scale-free property and heavy-tailed degree distributions • Clustering coeficients • Hierarchy - Modularity • Network motifs • Network models • Random networks (Poisson and with a given degree distribution) • Watts-Strogatz model • Preferential attachment and its variants • Optimization-based network formation models • Exponential random graphs

49. Community detection methods • Strength of weak ties • Graph partitioning - Focus on spectral partitioning algorithm • Modularity maximization methods • Spectral partitioning • Hierarchical clustering - divisive and agglomerative methods • Overlapping communities, dynamic communities, • Statistical properties of community structures • Statistical analysis of network data • Network sampling methods • Bias in network sampling • Network inference based on cross-correlations

50. Dynamics of networks • Percolation and network resilience • Random and non-random removel of vertices • Network epidemics • SI, SIS, SIR models • Network immunization • Identification of major spreaders • Social networks and influence • Information and behavior spreading - measurements and models • Influence maximization • Outbreak detection