Social Network Optimization Problems Homework

Does your child stay up all night doing homework? Is he or she often texting or online while doing homework or studying? Is it possible for students to study and do their homework effectively while being distracted by technology? Is focusing attention on homework really all that important? It’s just homework, right? 

Welcome to the 21st century. A world filled with distractions every where you turn. How is it even possible to get homework done at all, let alone focus on doing homework without being distracted by a wide variety of electronic gadgets. Back in the not so distant past, you might have heard a kid saying "It doesn't matter if I have the TV on while I do my homework. It's not like I'm studying for a test." Today, it's a bit more complicated as students and their smart phones are inseparable. What might at first glance seem harmelss, doing homework or studying while watching TV, texting or checking social media can actually impair learning the material as well as lower test scores. Research has shown that it's one of the worst study habits a student can develop.

Is There an App For That?

With nearly everyone over the age of 10 having a cell phone and access to the internet these days, it's quite common to find students dividing their attention between texting, checking social media websites and surfing the internet while doing homework and studying for exams. Given that text messaging is the way many students communicate with each other, it's not easy for parents to explain to them that when it's time to do homework or study for an exam it's necessary to turn their phone off.

In all likelihood, they will argue about this as students of all ages seem to have a misconception that they can pay attention to more than one thing at a time and that multitasking is an effective way to do homework or study for a test. How are you, their parent, going to respond? With research. In this blog post, we reviewed the most up to date research that we could find on the subject of multitasking to give parents a better understanding of what it takes to be a successful student.

What Does Research Show About Studying While Distracted by Technology?

In a study conducted by Dr. Larry Rosen, a psychology professor at California State University - Dominguez Hills, students were observed studying for a 15 minute period where they were told to "study something important.” He found was that students generally started to lose focus after about three minutes. On average "students only spent about 65 percent of the observation period actually studying." That’s not exactly what you might consider “quality” studying time.

Dr. Rosen did another study where he surveyed high school students and asked them how often they switch from studying to doing something related to technology such as checking email, Facebook, texting or watching TV. Across all grade levels, 80% of students reported that they switch between studying and technology somewhat often to very often. Rosen calls this “Continuous Partial Attention,” meaning that most of the time, students are not focused on studying but rather are moving their attention back and forth between studying and various forms of technology. As you might expect, students who were the most distracted generally had the most windows open on their computers. Students who were less distracted had higher GPAs than students who switched back and forth fairly often and those who regularly check Facebook or text messages. Students who had strategies for studying also had higher GPAs according to Rosen’s findings.

Rosen explains, “Young people’s technology use is really about quelling anxiety...they don’t want to miss out or to be the last person to hear some news (or like or comment about a post online).” One of the major problems with texting and posting on Facebook and other social media sites while in class and/or studying, is that "they draw on the same mental resources—using language, parsing meaning—demanded by schoolwork." Ultimately, he concludes, if we want students to learn and perform at their best, smart phones and other online distractions must be managed.

Can Doing Homework While Distracted by Technology Affect Test Scores?

In another study of 8-18 year old students done by the Kaiser Family Foundation, nearly one third of the students surveyed confessed that when they were doing homework, they were also watching TV, texting, or listening to music. Victoria Rideout, the lead author of the study, warns parents about the dangers of media multitasking. This concern is distinct from worrying about how much kids are online or how much kids are media multitasking overall. “It’s multitasking while learning that has the biggest potential downside,”she says.

If a student is focused when doing their homework, they actually retain more of the information when it comes time to take a test on the same subject matter. It's like studying for the test little by little and absorbing the information in small chunks. The strategy of ‘chunking’ bits of information has been shown to be the most effective way to learn larger amounts of information and is a useful test prep strategy. If a student does her homework while multitasking, that will result in less information being retained and therefore  more time will be required for test preparation in order to achieve the same result. Compounding matters, if homework is done while multitasking in an introductory class, it will be more difficult to build on that “shaky foundation of knowledge” in the more advanced class the next semester.

Dr. David Meyer, a psychology professor at the University of Michigan observed that “under most conditions, the brain simply cannot do two complex tasks at the same time. Listening to a lecture while texting, or doing homework and being on Facebook—each of these tasks is very demanding, and each of them uses the same area of the brain, the prefrontal cortex." Most students incorrectly believe that they can perform two challenging tasks at the same time, according to Meyer. They may like to do it, they may even be addicted to it, but there’s no getting around the fact that it’s far better to focus on one task from start to finish.”

Quick Test for Students to Determine if Multitasking Impacts Performance

Here’s a fun, 3 minute test that you can do along with your kids to demonstrate if multitasking impacts performance (and the time it takes to complete homework). Taking this simple test will allow students to see for themselves if multitasking could potentially be affecting their studying.

http://davecrenshaw.com/multitasking-example/

Top 3 Negative Outcomes of Studying While Being Distracted by Technology

According to an article by Annie Murphy Paul, research has shown that there are various negative outcomes that result from students multitasking while doing homework. Paul describes the top 3 negative outcomes. "First, the assignment takes longer to complete, because of the time spent on distracting activities and because, upon returning to the assignment, the student has to re-familiarize himself with the material.” Second, the mental fatigue caused by repeatedly dropping and picking up a mental thread leads to more mistakes. “Third, students’ subsequent memory of what they’re working on will be impaired if their attention is divided.” Paul explains, “The moment of encoding information is what matters most for retention, and dozens of laboratory studies have demonstrated that when our attention is divided during encoding, we remember that piece of information less well—or not at all."

Paul goes on to write, "Finally, researchers have found that media multitasking while learning is correlated with lower grades. In Rosen’s study (discussed above), students who used Facebook during the 15-minute observation period had lower grade-point averages than those who didn’t go on the site. In addition, two recent studies by Reynol Junco, a faculty associate at Harvard’s Berkan Center for Internet & Society, found that texting and using Facebook—in class and while doing homework—were negatively correlated with college students’ GPAs."

In conclusion, the evidence is overwhelming. Studying or doing homework while sitting in front of the TV, using social media or texting, makes it more difficult to learn and retain the information, increases the time it takes to complete homework, and may ultimately result in lower test scores.

Is your child attached to his smart phone or other electronic gadgets? If so, and grades are suffering, it might be time to take action. Are you ready to help your child break the multitasking habit, learn to focus attention on homework and get on the path to academic success?

How Parents Can Help Children Manage Distractions While Studying

Teach your child to take technology breaks to separate doing homework from using technology. Here's the strategy: After your child has worked on his homework without interruption for 15 minutes, he is then allowed a technology break for 2-3 minutes to text and post to social media. When the break time is up, you instruct him to turn off his electronic devices for another 15 minutes of doing homework or studying. Students can extend their working time to 20, 30 or 45 minutes and perhaps extend their technology break time to 5-7 minutes. If your child complains that the technology break time is too short, you can let him know that when he is finished with his homework, he can use technology for as long as he wants (or whatever amount of time you say is ok).

Would you like to cut your child's homework time in half?

If so, click below to download our free guide to "Cutting Homework Time in Half." You might also want to contact us to see if Executive Function coaching can help your child with focusing attention on homework.

 

 

 

Photo credit: Gitte Laasby


Attribution: A much more detailed discussion of some of these studies can be found in Slate Magazine (May 3, 2013) by Annie Murphy Paul, a fellow at the New America Foundation and author of the book Brilliant: The Science of How We Get Smarter.

Michael Howard is the Director of Marketing for Beyond BookSmart. He joined the company in 2012 and works remotely from Los Angeles. He is responsible for researching and developing marketing strategies, marketing materials, updating and optimizing the company website, social media, and search engine optimization. Michael earned his BA in Psychology from the University of Illinois at Urbana-Champaign and his MS in Industrial/Organizational Psychology from Lamar University.

 

 

 

 



CS224W:

Analysis of Networks

Autumn 2017

Handouts

Homeworks

  • Homework 0 (Due at 11:59pm Oct. 5, 2017). Submission Template for HW0 [pdf | tex]. Solutions: [PDF].
  • Homework 1 (Due at 11:59pm Oct. 12, 2017). Submission Template for HW1 [pdf | tex]. Solutions: [PDF].
  • Homework 2 (Due at 11:59pm Oct. 26, 2017). Submission Template for HW2 [pdf | tex]. Solutions: [PDF].
  • Homework 3 (Due at 11:59pm Nov. 9, 2017). Submission Template for HW3 [pdf | tex]. Solutions [PDF].
  • Homework 4 (Due at 11:59pm Nov. 30, 2017). Submission Template for HW4 [pdf | tex]. Solutions [PDF].

Recitations

Lecture notes and further reading

Pointers to the slides will be posted here just before the start of the class.

09/26: Introduction and Structure of Graphs [Slides]

Reading:

Readings and the list of future lectures will be useful to you when you are thinking about the course project.

09/28: Web as a Graph and the Random Graph Model [Slides]

Reading:
  • A. Broder, R. Kumar, F. Maghoul, P. Raghavan, S. Rajagopalan, R. Stata, A. Tomkins, J. Wiener. Graph structure in the Web. Computer Networks, 33, 2000.
  • Chapter 2: Graphs
Optional Readings:
  • P. Erdos, A. Renyi. On Random Graphs I. Publ. Math. Debrecen, 1959.
  • P. Erdos, A. Renyi. On the evolution of random graphs. Magyar Tud. Akad. Mat. Kutato Int. Koezl., 1960.
  • B. Bollobas. Random Graphs. Cambridge University Press.
  • M.E.J. Newman, S. H. Strogatz and D.J. Watts. Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64, 026118, 2001.
  • R. Milo, N. Kashtan, S. Itzkovitz, M.E.J. Newman, U. Alon. On the uniform generation of random graphs with prescribed degree sequences. Arxiv, 2004.
  • D. Ellis. The expansion of random regular graphs. Lecture notes from Algebraic methods in combinatorics, Cambridge University, 2011.
  • S. Arora, S. Rao and U. Vazirani. Expander Flows, Geometric Embeddings and Graph Partitioning. In proc. STOC '04, 2004.

10/03: The Small World Phenomena [Slides]

Reading: Optional Readings:
  • Demo: Erdos-Renyi random graph
  • Demo: Watts-Strogatz small-world model
  • S. Milgram. The small world problem. Psychology Today 1(1967).
  • J. Travers and S. Milgram. An experimental study of the small world problem. Sociometry 32, 1969.
  • P. S. Dodds, R. Muhamad, D. J. Watts. An Experimental Study of Search in Global Social Networks. Science 301(2003), 827.
  • J. Leskovec, E. Horvitz. Worldwide Buzz: Planetary-Scale Views on an Instant-Messaging Network. Proc. International WWW Conference, 2008.
  • P. Killworth and H. Bernard, Reverse small world experiment. Social Networks 1, 1978.
  • J. Kleinfeld. Could it be a Big World After All? The `Six Degrees of Separation' Myth. Society, 2002.
  • P. Killworth, C. McCarty, H.R. Bernard, M. House. The accuracy of small-world chains in social networks. Social Networks 28, 85-96, 2006.
  • F. Menczer. Growing and Navigating the Small World Web by Local Content. Proc. Natl. Acad. Sci., 99(22): 14014-14019, 2002.
  • L. Backstrom, P. Boldi, M. Rosa, J. Ugander, S. Vigna. Four Degrees of Separation. ACM Web Science Conference. 2012.
  • J. Ugander, B. Karrer, L. Backstrom, C. Marlow. The Anatomy of the Facebook Social Graph. Arxiv 2012.

10/05: Decentralized search in small-world and P2P networks [Slides]

Reading: Optional Readings:
  • M. E. J. Newman. Models of the Small World: A Review., J. Stat. Physics 2000.
  • J. Kleinberg. Small-World Phenomena and the Dynamics of Information. Advances in Neural Information Processing Systems (NIPS), 2001.
  • D. J. Watts, P. S. Dodds, M. E. J. Newman. Identity and Search in Social Networks. Science, 296, 1302-1305, 2002.
  • L. A. Adamic, R. M. Lukose, A. R. Puniyani, B. A. Huberman. Search in Power-Law Networks. Phys. Rev. E, 64 46135, 2001.
  • A. Clauset and C. Moore. How Do Networks Become Navigable? arXiv:cond-mat/0309415v2, 2003.
  • L. A. Adamic, E. Adar. How to search a social network. Social networks, 27 3, 187-203, 2005.
  • D. Liben-Nowell, J. Novak, R. Kumar, P. Raghavan, A. Tomkins. Geographic routing in social networks. In Proc. Natl. Acad. Sci., 102, 2005.
  • R. West, J. Leskovec. Human Wayfinding in Information Networks. In Proc. WWW, 2012.
  • R. West, J. Leskovec. Automatic versus Human Navigation in Information Networks. In Proc. ICWSM, 2012.
  • G. Manku, M. Naor, U. Wieder. Know thy Neighbor's Neighbor: The Power of Lookahead in Randomized P2P Networks. Proc. STOC 2004.
  • E-K Lua, J. Crowcroft, M. Pias, R. Sharma and S. Lim. A Survey and Comparison of Peer-to-Peer Overlay Network Schemes. IEEE Communications Surveys and Tutorials, 2005.
  • O. Sandberg and I. Clarke. The Evolution of Navigable Small-World Networks. arxiv cs.DS/0607025, 2006.
  • I. Clarke, O. Sandberg, B. Wiley, T. Hong. Freenet: A Distributed Anonymous Information Storage and Retrieval System. International Workshop on Design Issues in Anonymity and Unobservability, 2000.
  • I. Stoica, R. Morris, D. Karger, F. Kaashoek, H. Balakrishnan. Chord: A Scalable Peer-to-peer Lookup Service for Internet Applications. Proc. SIGCOMM, 2001.
  • H. Zhang, A. Goel, R. Govindan. Using the small-world model to improve freenet performance. Computer Networks, 2004.

10/10: Applications of Social Network Analysis [Slides]

Reading: Optional Readings:
  • A. Anderson, D. Huttenlocher, J. Kleinberg, J. Leskovec. Discovering Value from Community Activity on Focused Question Answering Sites: A Case Study of Stack Overflow . In Proc. KDD, 2012.
  • J. Leskovec, D. Huttenlocher, J. Kleinberg. Governance in Social Media: A case study of the Wikipedia promotion process. In Proc. ICWSM, 2010.
  • C. Danescu-Niculescu-Mizil, G. Kossinets, J. Kleinberg, L. Lee. How opinions are received by online communities: A case study on Amazon.com helpfulness votes. In Proc. ACM WWW, 2009.
  • R. Guha, R. Kumar, P. Raghavan, A. Tomkins. Propagation of trust and distrust. In Proc. WWW, 2004.
  • L. A. Adamic, J. Zhang, E. Bakshy, and M. S. Ackerman. Knowledge sharing and yahoo answers: everyone knows something. In Proc. 17th International World Wide Web Conference, 2008.
  • M. Burke and R. Kraut. Mopping up: Modeling wikipedia promotion decisions. In Proc. CSCW '08: ACM Conference on Computer-Supported Cooperative Work, 2008.
  • D. Lauterbach, H. Truong, T. Shah, L. A. Adamic. Surfing a Web of Trust: Reputation and Reciprocity on CouchSurfing.com. International Symposium on Social Intelligence and Networking, 2009.
  • C. Y. Teng, D. Lauterbach, L. A. Adamic. I rate you. You rate me. Should we do so publicly? In Proc. WOSN, 2010.
  • L. Muchnik, S. Aral, S. J. Taylor. Social Influence Bias: A Randomized Experiment. Science, Vol. 341 no. 6146 pp. 647-651, 2013.

10/12: Networks with Signed Edges [Slides]

Reading: Optional Readings:
  • D. Cartwright, F. Harary. Structural balance: A generalization of Heider's theory. Psychological review, 1956.
  • F. Heider. Attitudes and cognitive organization. Journal of Psychology. 21, 107-112, 1946.
  • J. Leskovec, D. Huttenlocher, J. Kleinberg. Predicting Positive and Negative Links in Online Social Networks. In Proc. WWW, 2010.
  • R. Guha, R. Kumar, P. Raghavan, A. Tomkins. Propagation of trust and distrust. In Proc. WWW, 2004.
  • T. Antal, P. Krapivsky, S. Redner. Dynamics of Social balance on Networks. Phys. Rev. E, 2005.
  • S. Marvel, S. Strogatz, J. Kleinberg. Energy landscape of social balance. Physical Review Letters, 103, 2009.
  • S. Marvel, J. Kleinberg, R. Kleinberg, S. Strogatz. Continuous-Time Model of Structural Balance. Proc. National Academy of Sciences, 2011.
  • J.A. Davis. Structural balance, mechanical solidarity, and interpersonal relations. American Journal of Sociology, 68:444-62, 1963.
  • P. Doreian, A. Mrvar. A partitioning approach to structural balance. Social. Networks 18, 1996.
  • M. J. Brzozowski, T. Hogg, G. Szabo. Friends and foes: ideological social networking. In Proc. CHI, 2008.
  • P. Doreian. Evolution of human signed networks. In Metodološki zvezki, 2004.
  • C. J. Hsieh, K Chiang, and I. S. Dhillon. Low-Rank Modeling of Signed Networks. In Proc. KDD, 2012.
  • K. Chiang, I. S. Dhillon, N. Natarajan, and A. Tewari. Exploiting Longer Walks for Link Prediction in Signed Network. In Proc. CIKM, 2011.
  • G. Facchetti, G. Iacono, C Altafini. Computing global structural balance in large-scale signed social networks. In Proc. National Academy of Sciences, 2011.
  • M. Szell, R. Lambiotte, S. Thurner. Multirelational Organization of Large-scale Social Networks in an Online World. Proc. National Academy of Sciences, 2010.
  • J. Kunegis, A. Lommatzsch, C. Bauckhage. The Slashdot Zoo: Mining a social network with negative edges. In Proc. WWW, 2009.

10/17: Cascading Behavior: Decision Based Models of Cascades [Slides]

Reading: Optional Readings:
  • S. Morris. Contagion. Review of Economic Studies 67, 57-78, 2000.
  • N. Immorlica, J. Kleinberg, M. Mahdian, T. Wexler. The Role of Compatibility in the Diffusion of Technologies Through Social Networks. In Proc. ACM EC, 2007.
  • E. Berger. Dynamic Monopolies of Constant Size. Journal of Combinatorial Theory Series B 83, 191-200, 2001.
  • D. Centola, M. Macy. Complex Contagions and the Weakness of Long Ties. American Journal of Sociology, 2007.
  • M. Jackson, L. Yariv. Diffusion of Behavior and Equilibrium Properties in Network Games. American Economic Review , Vol 97, No. 2, 2007.
  • S. Bikhchandani, D. Hirshleifer, I. Welch. A theory of fads, fashion, custom and cultural change as information cascades. Journal of Political Economy. Vol. 100, pp. 992-1026, 1992.
  • T. Schelling. Micromotives and Macrobehavior. Norton, 1978.
  • D. Strang, S. Soule. Diffusion in organizations and social movements: From hybrid corn to poison pills. Annual Review of Sociology, 24:265--290, 1998.
  • D. Watts. A simple model of global cascades on random networks. Proc. Natl. Acad. Sci., vol. 99 no. 9, 5766-5771, 2002.
  • H. P. Young. The Diffusion of Innovations in Social Networks. Santa Fe Institute Working Paper 02-04-018.
  • P. Dodds and D. J. Watts. Universal Behavior in a Generalized Model of Contagion. Phyical Review Letters, 2004.
  • E. Lieberman, C. Hauert, M. A. Nowak. Evolutionary Dynamics on Graphs. Nature 433: 312-316, 2005.
  • D. Centola, M. Macy, V. Eguiluz. Cascade Dynamics of Multiplex Propagation. Physica A 374, 449-456, 2007.
  • D. Centola. The Spread of Behavior in an Online Social Network Experiment. Science, 2010.
  • M. Granovetter. Threshold models of collective behavior. American Journal of Sociology 83(6):1420-1443, 1978.
  • A. V. Banerjee. A Simple Model of Herd Behavior. The Quarterly Journal of Economics, Vol. 107, No. 3, pp. 797-817, 1992.

10/19: Cascading Behavior: Probabilistic Models of Information Flow [Slides]

Reading: Optional Readings:
  • S. Myers, C. Zhu, J. Leskovec. Information Diffusion and External Influence in Networks. In Proc. KDD, 2012.
  • A. Goyal, F. Bonchi, L.V.S. Lakshmanan. Learning influence probabilities in social networks. In Proc. WSDM, 2010.
  • D. Cosley, D. Huttenlocher, J. Kleinberg, X. Lan, S. Suri.Sequential Influence Models in Social Networks. In Proc. ICWSM, 2010.
  • J. Leskovec, A. Singh, J. Kleinberg. Patterns of Influence in a Recommendation Network In Proc PAKDD, 2006.
  • L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan. Group Formation in Large Social Networks: Membership, Growth, and Evolution. In Proc. KDD, 2006.
  • E. Adar, L. Adamic. Tracking information epidemics in blogspace. In Proc. Wed intelligence, 2005.
  • J. Leskovec, M. McGlohon, C. Faloutsos, N. Glance, M. Hurst. Cascading Behavior in Large Blog Graphs. In Proc. SIAM International Conference on Data Mining, 2007.
  • D. Gruhl, R. Guha, D. Liben-Nowell, A. Tomkins. Information Diffusion through Blogspace. In Proc. International WWW Conference, 2004.
  • M. Goetz, J. Leskovec, M. Mcglohon, C. Faloutsos. Modeling blog dynamics, In AAAI Conference on Weblogs and Social Media (ICWSM), 2009.
  • M. Miller, C. Sathi, D. Wiesenthal, J. Leskovec, C. Potts. Sentiment Flow Through Hyperlink Networks. In Proc. ICWSM, 2011.
  • J. Ugander, L. Backstrom, C. Marlow, J. Kleinberg. Structural Diversity in Social Contagion. In Proc. National Academy of Sciences, 2012.

10/24: Influence Maximization [Slides][Handout]

Reading: Optional Readings:
  • M. Richardson, P. Domingos. Mining Knowledge-Sharing Sites for Viral Marketing. In Proc. KDD, 2002.
  • M. Richardson, P. Domingos. Mining the Network Value of Customers. In Proc. KDD, 2001.
  • S. Hill, F. Provost, C. Volinsky. Network-Based Marketing: Identifying Likely Adopters via Consumer Networks. Statistical Sciece, 2006.
  • J. Leskovec, L. Adamic, B. Huberman. The Dynamics of Viral Marketing. TWEB, 2007.
  • E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, Everyone’s an influencer: quantifying influence on twitter. In Proc. WSDM, 2011.
  • S. Aral, L. Muchnik, A. Sundararajan. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. In Proc. National Academy of Sciences, 2009.
  • A. Goyal, W. Lu, L. S.V. Lakshmanan. SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model. In Proc. ICDM, 2011.
  • W. Chen, Y. Wang, S. Yang. Efficient Influence Maximization in Social Networks. In Proc. KDD, 2009.
  • W. Chen, Y. Yuan, L. Zhang. Scalable Influence Maximization in Social Networks under the Linear Threshold Model. In Proc. ICDM, 2010.
  • C. Lerman, R. Ghosh. Information Contagion: Empirical Study of the Spread of News on Digg and Twitter Social Networks. In Proc. ICWSM, 2010.
  • Y. Singer. How to win friends and influence people, truthfully: Influence maximization mechanisms for social networks. In Proc. WSDM, 2012.
  • A. Gionis, E. Terzi, P. Tsaparas. Opinion maximization in social networks. In Proc. SDM, 2013.

10/26: Outbreak Detection [Slides]

Reading: Optional Readings:
  • E. Mossel and S. Roch. On the Submodularity of Influence in Social Networks. In Proc. STOC, 2007.
  • N. Agarwal, H. Liu, L. Tang, P. Yu. Identifying the Influential Bloggers in a Community In Proc. WSDM, 2008.
  • A. Krause, J. Leskovec, C. Guestrin, J. VanBriesen, C. Faloutsos. Efficient Sensor Placement Optimization for Securing Large Water Distribution Networks. Journal of Water Resources Planning and Management, 2008.
  • A. Krause, C. Guestrin, A Note on the Budgeted Maximization on Submodular Functions. Technical report, Carnegie Mellon University, no. CMU-CALD-05-103, 2005.
  • A. Ostfeld et al. The Battle of the Water Sensor Networks (BWSN): A Design Challenge for Engineers and Algorithms. Journal of Water Resources Planning and Management, 2009.
  • M. Cha, H. Haddadi, F. Benevenuto, K.P. Gummadi. Measuring user influence in Twitter: The million follower fallacy. In Proc. ICWSM, 2010.
  • A. Goyal, W. Lu, L. V.S. Lakshmanan. Celf++: optimizing the greedy algorithm for influence maximization in social networks. In Proc. WWW, 2011.
  • R. Pastor-Satorras, A. Vespignani. Immunization of complex networks. Physical Review E, 2002.
  • T. Lappas, E. Terzi, D. Gunopoulos, H. Mannila. Finding Effectors in Social Networks. In Proc. KDD, 2010.

10/31: Power-laws and Preferential attachment [Slides][Handout]

Reading: Optional Readings:
  • M. Mitzenmacher. A Brief History of Generative Models for Power Law and Lognormal Distributions. Internet Mathematics, vol 1, No. 2, pp. 226-251, 2004.
  • A. Clauset, C.R. Shalizi, and M.E.J. Newman. Power-law distributions in empirical data. SIAM Review 51(4), 661-703, 2009.
  • M.E.J. Newman. Power laws, Pareto distributions and Zipf's law. Contemporary Physics 46(5), 323-351, 2005.
  • M. Faloutsos, P. Faloutsos, C. Faloutsos. On Power-Law Relationships of the Internet Topology. In Proc. SIGCOMM, 1999.
  • A.L Barabasi, R. Albert. Emergence of scaling in random networks. Science, 286, 1999.
  • B.A. Huberman, L. A. Adamic. Growth dynamics of the World-Wide Web. Nature, 399, 1999.
  • H.A. Simon. On a class of skew distribution functions. Biometrika 42, 425-440, 1955
  • D.J. de S. Price. A general theory of bibliometric and other cumulative advantage processes. J. Amer. Soc. Inform. Sci. 27: 292-306, 1976.
  • A.L. Barabasi, R. Albert, H. Jeong. Mean-field theory for scale-free random networks. Physica A 272 173-187, 1999.
  • R. Kumar, P. Raghavan, S. Rajagopalan, D. Sivakumar, A. Tomkins, E. Upfal. Stochastic models for the Web graph. In Proc. FOCS 2000.
  • W. Aiello, F. Chung, L. Lu. Random evolution of massive graphs. Handbook of Massive Data Sets, Kluwer, pages 97-122, 2002.
  • B. Bollobas, C. Borgs, J. Chayes, O. Riordan. Directed scale-free graphs. In Proc. SODA 2003.
  • R. Kleinberg, J. Kleinberg. Isomorphism and Embedding Problems for Infinite Limits of Scale-Free Graphs. In Proc. SODA, 2005.
  • A. Fabrikant, E. Koutsoupias, C. Papadimitriou. Heuristically Optimized Trade-offs: A New Paradigm for Power Laws in the Internet. In Proc. ICALP, 2002.
  • N. Berger, C. Borgs, J. Chayes, R. D'Souza, R. Kleinberg. Competition-Induced Preferential Attachment. In Proc. ICALP 2004.
  • M. Molloy and B. Reed. A Critical Point for Random Graphs with a Given Degree Sequence. Random Structures and Algorithms 6, 161-180, 1995.
  • J. Carlson and J. Doyle. Highly Optimized Tolerance: A Mechanism for Power Laws in Designed Systems. Physical Review E 60:2, 1999.
  • M.E.J. Newman. The first-mover advantage in scientific publication. European Physics Letters 86, 68001, 2009.
  • S. Redner. Citation statistics from 110 years of Physical Review. Physics Today 58, 49-54, 2005.
  • S. Goel, A. Broder, E. Gabrilovich, B. Pang. Anatomy of the Long Tail: Ordinary People with Extraordinary Tastes. In Proc. WSDM, 2010.
  • D. Pennock, G. Flake, S. Lawrence, E. Glover, C. Lee Giles. Winners don't take all: Characterizing the competition for links on the web. PNAS 99(8), 2002.

11/02: Models of evolving networks [Slides]

Reading: Optional Readings:
  • J. Leskovec, J. Kleinberg, C. Faloutsos. Graph Evolution: Densification and Shrinking Diameters. ACM TKDD, 2007.
  • R. Kumar, J. Novak, A. Tomkins. Structure and evolution of online social networks. In Proc. KDD, 2006.
  • A.L. Barabasi, H. Jeong, Z. Neda, E. Ravasz, A. Schubert, T. Vicsek. Evolution of the social network of scientific collaborations. Physica A: Statistical, 2002.
  • G. Kossinets, D.J. Watts. Empirical Analysis of an Evolving Social Network. Science, 2006.
  • D. Fetterly, M. Manasse, M. Najork, J.L. Wiener. A large-scale study of the evolution of web pages. In Proc. WWW, 2003.
  • A. Ntoulas, J. Cho, C. Olston. What's new on the web? The evolution of the web from a search engine perspective. In Proc. WWW, 2004.
  • A.L. Barabasi. The origin of bursts and heavy tails in human dynamics. Nature 435, 207-211, 2005.
  • J. Kleinberg. Bursty and Hierarchical Structure in Streams. In Proc. KDD, 2002.
  • R. Kumar, J. Novak, P. Raghavan, A. Tomkins. On the bursty evolution of Blogspace. In Proc. WWW, 2003.
  • M. Dubinko, R. Kumar, J. Magnani, J. Novak, P. Raghavan, A. Tomkins. Visualizing Tags over Time. In Proc. WWW, 2006.
  • S. Lattanzi, D. Sivakumar. Affiliation Networks. In Proc. STOC, 2009.

11/07: Kronecker graphs [Slides]

Reading: Additional readings:
  • J. Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos. Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication. European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2005.
  • M. Kim, J. Leskovec.Multiplicative attribute graph model of real-world networks. Internet Mathematics, 2012.
  • C. Seshadhri, A. Pinar and T. G. Kolda. An In-Depth Analysis of Stochastic Kronecker Graphs. Journal of the ACM, 2013.
  • D. Chakrabarti, Y. Zhan and C. Faloutsos. R-MAT: A Recursive Model for Graph Mining. SIAM Data Mining, 2004.
  • J. Leskovec, C. Faloutsos. Scalable Modeling of Real Graphs using Kronecker Multiplication. International Conference on Machine Learning (ICML), 2007.
  • M. Kim, J. Leskovec.Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model. In Proc. UAI, 2011.
  • M. Kim, J. Leskovec.Latent Multi-group Membership Graph Model. In Proc. ICML, 2012.
  • M. Mahdian, Y. Xu. Stochastic Kronecker Graphs. 5th Workshop on Algorithms and Models for the Web Graph (WAW), 2007.
  • G. Palla, L. Lovasz, T. Vicsek. Multifractal Network Generator. PNAS, 2010.
  • S. Moreno, S. Kirshner, J. Neville, S.V.N. Vishwanathan. Tied Kronecker Product Graph Models to Capture Variance in Network Populations. In Proceedings of the 48th Annual Allerton Conference on Communications, Controland Computing, 2010.
  • E. Bodine, B. Hassibi, A. Wierman. Distance-dependent Kronecker Graphs for Modeling Social Networks. IEEE THEMES, 2010.
  • A. Pinar, C. Seshadhri and T. G. Kolda. The Similarity between Stochastic Kronecker and Chung-Lu Graph Models. In Proc. SDM, 2012.
  • H. Yun, S. V. N. Vishwanathan. Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs. In Proc. AISTATS, 2012.
  • M. Kim, J. Leskovec.The Network Completion Problem: Inferring Missing Nodes and Edges in Networks. In Proc. SDM, 2011.

11/09: Link Analysis: HITS and PageRank [Slides][Handout]

Reading: Optional Readings:
  • S. Brin and L. Page. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Proc. 7th International World Wide Web Conference, 1998.
  • J. Kleinberg. Authoritative sources in a hyperlinked environment. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms, 1998.
  • P. Berkhin. A Survey of PageRank Computing. Internet Mathematics, 2005.
  • S. Chakrabarti, B. Dom, D. Gibson, J. Kleinberg, S.R. Kumar, P. Raghavan, S. Rajagopalan, A. Tomkins. Mining the link structure of the World Wide Web. IEEE Computer, August 1999.
  • A. Arasu, J. Cho, H. Garcia-Molina, A. Paepcke, S. Raghavan. Searching the Web. ACM Transactions on Internet Technology 1(1): 2-43, 2001.
  • A. Borodin, J. S. Rosenthal, G. O. Roberts, P. Tsaparas,Finding Authorities and Hubs From Link Structures on the World Wide Web. 10th International World Wide Web Conference, May 2001.
  • D. Achlioptas, A. Fiat, A. Karlin, F. McSherry. Web Search via Hub Synthesis. 42nd IEEE Symposium on Foundations of Computer Science, p.611-618, 2001.
  • D. Rafiei, A. Mendelzon. What is this Page Known for? Computing Web Page Reputations. Proc. WWW Conference, 2000.
  • P. Domingos, M. Richardson. The Intelligent Surfer: Probabilistic Combination of Link and Content Information in PageRank. In Proc. NIPS, 2002.
  • T. H. Haveliwala. Topic-Sensitive PageRank. 11th International World Wide Web Conference, 2002.
  • A. Altman, M. Tennenholtz. Ranking Systems: The PageRank Axioms. In Proc. of ACM EC, 2005.
  • Z. Gyongyi, H. Garcia-Molina, J. Pedersen. Combating Web Spam with TrustRank. In Proc. of VLDB, 2004.
  • Z. Gyongyi, P. Berkhin, H. Garcia-Molina, J. Pedersen. Link Spam Detection Based on Mass Estimation. In Proc. of VLDB, 2006.
  • A. Borodin, G. O. Roberts, J. S. Rosenthal, P Tsaparas. Link Analysis Ranking: Algorithms, Theory, and Experiments. ACM TOIT, 2005.
  • A. Ntoulas, J. Cho, C. Olston. What’s New on the Web? The Evolution of the Web from a Search Engine Perspective. In Proc. WWW, 2004.
  • M. A. Najork. Comparing the effectiveness of HITS and SALSA. In Proc. CIKM, 2007.
  • B. Bahmani, A. Chowdhury, A. Goel. Fast Incremental and Personalized PageRank. In Proc. of VLDB, 2010.

11/14: Strength of weak ties and Community structure in networks [Slides]

Reading: Optional Readings:
  • M. Granovetter. The strength of weak ties. American Journal of Sociology, 78(6):1360-1380, 1973.
  • J.-P. Onnela, J. Saramaki, J. Hyvonen, G. Szabo, D. Lazer, K. Kaski, J. Kertesz, A.L. Barabasi. Structure and tie strengths in mobile communication networks. PNAS, 2007
  • M. Girvan and M.E.J. Newman. Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 8271-8276, 2002.
  • M.E.J. Newman. Modularity and community structure in networks., Proc. Natl. Acad. Sci., 2002.
  • C. Marlow, L. Byron, T. Lento, I. Rosenn. Maintained relationships on Facebook. 2009.
  • B.A. Huberman, D.M. Romero, F. Wu. Social networks that matter: Twitter under the microscope. First Monday, 14(1), 2009.
  • L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan. Group Formation in Large Social Networks: Membership, Growth, and Evolution. In Proc. KDD, 2006.
  • P.S. Bearman, J. Moody. Suicide and Friendships Among American Adolescents. Am J Public Health, 94(1): 89-95, 2004.
  • R. Burt. Structural Holes versus Network Closure as Social Capital. Chapeter in Social Capital: Theory and Research, 2001.
  • R. Burt. Structural Holes and Good Ideas. American Journal of Sociology, Vol. 110, No. 2 2004.
  • G. Flake, S. Lawrence, C.L. Giles, F. Coetzee. Self-Organization and Identification of Web Communities. IEEE Computer, 35:3, 2002.
  • G. Flake, K. Tsioutsiouliklis, R.E. Tarjan. Graph Clustering Techniques based on Minimum Cut Trees. Technical Report 2002-06, NEC, Princeton, NJ, 2002.
  • S. Fortunato Community detection in graphs, Arxiv 2009.
  • A. Clauset, M.E.J. Newman, C. Moore. Finding community structure in very large networks. Phys. Rev. E 70, 066111, 2004
  • M.E.J. Newman, M. Girvan. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113, 2004.
  • U. Brandes. A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 2001.
  • J. Reichardt, S. Bornholdt. Statistical Mechanics of Community Detection., Phys. Rev. E 74 016110, 2006.
  • S. Fortunato, S. Barthelemy. Resolution limit in community detection. Proc. Natl. Acad. Sci., 2007.
  • U. Brandes, D. Delling, M. Gaertler, R. Goerke, M. Hoefer, Z. Nikoloski, D. Wagner. On Modularity Clustering. IEEE TKDE, 2007.

11/16: Network community detection: Spectral Clustering [Slides][Handout]

Reading:
    A. Rajaraman, J. Ullman, J. Leskovec. Chapter 10.4 of Mining Massive Datasets. 2013.
Additional readings:
  • J. Shi, J. Malik. Normalized Cuts and Image Segmentation. IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 22, no. 8, 2000.
  • R. Kannan, S. Vempala, A. Vetta. On clusterings: Good, bad and spectral. Journal of the ACM, 51(3):497-515, 2004.
  • M. Fiedler. Algebraic connectivity of graphs. Czechoslovak Mathematical Journal, 1973.
  • A. Pothen, H.D. Simon, K.P. Liou. Partitioning sparse matrices with egenvectors of graph. SIAM Journal of Matrix Anal. Appl., 11:430--452, 1990.
  • L. Hagen, A.B. Kahng. New spectral methods for ratio cut partitioning and clustering. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1992.
  • A. Ng, M. Jordan, Y. Weiss. On spectral clustering: Analysis and an algorithm. NIPS, 2001.
  • U. von Luxburg. Tutorial on spectral clustering. Arxiv 2009.
  • S. Dill, R. Kumar, K. McCurley, S. Rajagopalan, D. Sivakumar, A. Tomkins. Self-similarity in the Web. In Proc. VLDB, 2001.

11/28: Biological networks [Slides][Handout]

Readings: Additional readings:
  • S.D. Himmelstein, S.E. Baranzini. Heterogeneous Network Edge Prediction: a Data Integration Approach to Prioritize Disease-Associated Genes. PLoS Computational Biology, 2015.
  • M. Costanzo, et al. The Genetic Landscape of a Cell. Science, 2010.
  • A. Baryshnikova. Systematic Functional Annotation and Visualization of Biological Networks. Cell Systems, 2016.
  • J.X. Hu, C.E. Thomas, S. Brunak. Network Biology Concepts in Complex Disease Comorbidities. Nature Reviews Genetics, 2016.
  • J. Menche, et al. Uncovering Disease-Disease Relationships Through the Incomplete Interactome. Science, 2015.
  • A.B. Jensen, et al. Temporal Disease Trajectories Condensed from Population-Wide Registry Data Covering 6.2 Million Patients. Nature Communications, 2014.
  • S. Mostafavi, et al. GeneMANIA: a Real-Time Multiple Association Network Integration Algorithm for Predicting Gene Function. Genome Biology, 2008.
  • M. Hofree, et al. Network-Based Stratification of Tumor Mutations. Nature Methods, 2013.
  • H. Yu, et al. High-Quality Binary Protein Interaction Map of the Yeast Interactome Network. Science, 2008.
  • X. Zhou, et al. Human Symptoms-Disease Network. Nature Communications, 2014.
  • A. Mazza, K. Klockmeier, E. Wanker, R. Sharan. An Integer Programming Framework for Inferring Disease Complexes from Network Data. Bioinformatics, 2016.
  • D.M. Leiserson, et al. Pan-Cancer Network Analysis Identifies Combinations of Rare Somatic Mutations Across Pathways and Protein Complexes. Nature Genetics, 2015.
  • C.S. Greene, et al. Understanding Multicellular Function and Disease with Human Tissue-Specific Networks. Nature Genetics, 2015.
  • M. Kramer, et al. Inferring Gene Ontologies from Pairwise Similarity Data. Bioinformatics, 2014.
  • Y. Hulovatyy, H. Chen, T. Milenkovic. Exploring the Structure and Function of Temporal Networks with Dynamic Graphlets. Bioinformatics, 2015.
  • S. Lee, S. Kong, E.P. Xing. A Network-Driven Approach for Genome-Wide Association Mapping. Bioinformatics, 2016.
  • Y. Li, et al. Expansion of Biological Pathways Based on Evolutionary Inference. Cell, 2014.
  • E. Guney, et al. Network-Based in Silico Drug Efficacy Screening. Nature Communications, 2016.

11/30: Overlapping communities in networks [Slides]

Reading: Additional readings:
  • E. Tomita, A. Tanaka, H. Takahashi. The worst-case time complexity for generating all maximal cliques and computational experiments. Theoretical Computer Science, Volume 363, Issue 1, 2006.
  • G. Palla, A.L. Barabasi, T. Vicsek. Quantifying social group evolution. Nature 446, 664-667, 2007.
  • J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney. Statistical Properties of Community Structure in Large Social and Information Networks. In Proc. WWW, 2008.
  • J. Leskovec, K. Lang, M. Mahoney. Empirical Comparison of Algorithms for Network Community Detection. In Proc. WWW, 2010.
  • J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney. Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. Internet Mathematics, 2009.
  • G. Karypis, V. Kumar. Multilevel k-way Partitioning Scheme for Irregular Graphs. J. Parallel Distrib. Comput, 48(1): 96-129, 1998.
  • I. Dhillon, Y. Guan, and B, Kulis. A Fast Kernel-based Multilevel Algorithm for Graph Clustering. In Proc. KDD, 2005.
  • R. Andersen, F. Chung, K. Lang. Local graph partitioning using pagerank vectors. In Proc. FOCS, 2006.
  • T. Leighton, S. Rao. Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms. Journal of the ACM, 1999.

12/04: Representation Learning on Graphs [Slides]

Readings:
  • W. Hamilton, R. Ying, J. Leskovec. Representation Learning on Graphs: Methods and Applications. IEEE Data Engineering Bulletin, 2017.
  • Tentative list of future lectures

    12/07: Networks: Two Fun Topics

    Readings:
    • C. Danescu-Niculescu-Mizil, R. West, D. Jurafsky, J. Leskovec, C. Potts. No Country for Old Members: User lifecycle and linguistic change in online communities ACM International Conference on World Wide Web (WWW), 2013
    • A. Anderson, D. Huttenlocher, J. Kleinberg, J. Leskovec. Steering User Behavior With Badges. ACM International Conference on World Wide Web (WWW), 2013.
    • J. Kleinberg. The convergence of social and technological networks. CACM, 2008.
    Additional readings:

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