The recent popularity of micro blogging and instance messaging services (such as Twitter), social networking sites (such as Facebook), photo and video sharing sites (such as Instagram, YouTube and Flickr), blogging platforms and many others have revolutionize all aspects of everyday life including social interactions, businesses, education and entertainment. From a computer science perspective, the ever increasing employment of social networks has resulted in unprecedented amounts of data and has opened up opportunities for new research in their storage, processing and analysis.

The first part of the course covers issues regarding the structure and analysis of large social and information networks as well as models that abstract their basic properties. Topics include among others methods for link analysis, diffusion and information propagation, and event detection.

A version of this part has been offered as a graduate course in the Computer Science and Engineering Department of the University of Ioannina, Greece during the Academic Years 2012-2013 and 2013-2014.

There is no official text for this part. The following books are recommended as optional reading:

The second part of this course is focused on social network analysis for software engineering. Software development is a collaborative activity in which the social processes among the members of a project team are necessary for achieving the project goals [1].This is true also for distributed teams in which communication and collaboration issues are exacerbated [2].To address communication challenges in distributed teams is particularly important since collaboration is driven by coordination needs and relies on knowledge exchange and trust.

In recent years, the use of the Web has widely affected interpersonal communication, thanks to the diffusion of social software that facilitates interaction and enhances our everyday life. As a consequence, social software is now playing a crucial role in facilitating collaboration in global software projects [3][4]. This worldwide diffusion of social software caused the availability of a huge amount of real data about collaboration patterns in distributed groups recently attracting the interest of empirical researchers towards Social Network Analysis (SNA) [5]. SNA is a well-consolidated discipline in social sciences that provides us with a rich and well-established set of concepts and measures for the understanding of social processes, including for instance the information flow among members of distributed groups.

This part aims at providing basic SNA concepts and measures that can be used to understand and to discover the actual social processes in global software engineering (GSE). It also discusses the application of these concepts and measures in GSE and offers the participants the chance to practice them using a real project dataset. The part is suitable for anyone interested in global software development and collaborative work. An introductory version of this part has been provided to students attending the collaborative software development classes in the master degree in Computer Science at the University of Bari (academic year 2012-13).

The third part of the course addresses the subject of Social Networks from a performance analysis viewpoint and treats the integration of SN into an SME workflow.


Course Organization


The course is organized as follows.


PART A: Online Social Networks

Lecture 1: offers an introduction to social networks and to the course content as well as a short graph theory reminder.

Lecture 2: focuses on network measurements including centrality measures, degree distributions and the clustering coefficient.

Lecture 3: presents models of social networks that abstract their basic characteristic including models that capture their evolution.

Lecture 4: covers strong and weak ties and the notion of betwenness.

Lecture 5: focuses on surrounding contexts covering issues such as homophily and affiliations.

Lecture 6:  introduces navigation in small worlds.

Lecture 7: covers the structural balance theory in the case of positive and negative relationships.

Lecture 8: introduces the topic of information cascades.

Lecture 9: deals with epidemics and influence including the SIR and SIS models and percolation theory.

Lecture 10 focuses on influence maximization.

Lecture 11: covers link analysis and web search including random walks, the PageRank and HITS algorithms and their variations.

Lecture 12: presents the fundamentals of link prediction.

Lecture 13: focuses on using content from online social networks and media to predict stock changes, track earthquakes, and understand news cycles.


PART B: Social Network Analysis for Software Engineering

Lecture 14: introduces basic concepts and measures in SNA and describes how SNA.

Lecture 15: describes how SNA can be used to address research issues in global software engineering


PART C: Social Network Analysis for SMEs

Lecture 16:  Social Network Analytics: addresses the subject of Social Networks from a performance analysis viewpoint. The objective is to cover the basic measures commonly used for characterizing a network, analyzing its structure and measuring its performance.

Lecture 17: Integrate Social Media into an SME workflow: treats the integration of SN into an SME workflow and addresses the ways Social Media could be used to enhance customer loyalty and increase reputation and sales of Small/Medium Enterprise.


  1. B. Curtis, H. Krasner, and N. Iscoe, “A field study of the software design process for large systems”. Communications of the ACM, vol. 31. no. 11, pp. 1268–1287, November 1988
  2. J. Herbsleb, A. Mockus, T. Finholt, and R. Grinter, “An empirical study of global software development: distance and speed. In Proc. of the Int’l Conference on Software Eng., IEEE Computer Society, 2001, pp. 81–90.
  3. Begel, J. Bosch, and M.D. Storey: Social Networking Meets Software Development: Perspectives from GitHub, MSDN, Stack Exchange, and TopCoder. IEEE Software 30(1), pp. 52-66, 2013.
  4. F. Calefato and F. Lanubile, “Augmenting Social Awareness in a Collaborative Development Environment”, 5th Int’l Workshop on Cooperative and Human Aspects of Software Engineering (CHASE’12), pp. 12-14, June 2012,.
  5. S. Wasserman and K. Faust, Social Network Analysis: Methods and Applications. Crambidge University Press: Crambidge, United Kingdom, 1994
  6. M. E. J. Newman, The structure and function of complex networks, SIAM Reviews, 45(2): 167-256, 2003
  7. M. E. J. Newman, Power laws, Pareto distributions and Zipf’s law, Contemporary Physics.
  8. B. Bollobas, Mathematical Results in Scale-Free random Graphs.
  9. D.J. Watts. Networks, Dynamics and Small-World Phenomenon, American Journal of Sociology, Vol. 105, Number 2, 493-527, 1999
  10. Watts, D. J. and S. H. Strogatz. Collective dynamics of ‘small-world’ networks. Nature 393:440-42, 1998
  11. Michael T. Gastner and M. E. J. Newman, Optimal design of spatial distribution networks, Phys. Rev. E 74, 016117 (2006)
  12. Chapter 21 from the book “Introduction to Information Retrieval” by C. Manning, P. Raghavan, H. Schutze
  13. P. G. Doyle, J. L. Snell. Random Walks and Electrical Networks.
  14. D. Bindel, J. Kleinberg, S. Oren. How Bad is Forming Your Own Opinion? Proc. 52nd IEEE Symposium on Foundations of Computer Science, 2011.
  15. David Liben-Nowell, Jon Kleinberg. The Link Prediction Problem for Social Networks. J. American Society for Information Science and Technology.
  16. Aaron Clauset,Cristopher Moore, M. E. J. Newman. Hierarchical structure and the prediction of missing links in networks. Nature 2008
  17. D. Kempe, J. Kleinberg, E. Tardos. Maximizing the Spread of Influence through a Social Network. Proc. 9th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2003.
  18. Eduardo J. Ruiz, Vaggelis Hristidis, Carlos Castillo, Aristides Gionis, Alejandro Jaimes, Correlating Financial Time Series with Micro-Blogging Activity, WSDM 2012.
  19. Takeshi Sakaki, Makoto Ozakaki, Yutaka Matsuo, Earthquake shakes Twitter users: Real-time event detection by social sensors, WWW 2010.
  20. Jure Leskovek, Lars Backstorm, Jon Kleinberg, Meme-tracking and the dynamics of the news cycle, KDD 2009