Course Overview
General Info:
Instructor: Fragkiskos Malliaros
Email: fragkiskos.me [at] gmail.com
Office hours: Right after class (or send me an email and we will find a good time to meet)
TA: Abdulkadir Çelikkanat
Email: abdcelikkanat [at] gmail.com
Piazza: piazza.com/centralesupelec/winter2019/ngsa/home
Networks (or graphs) have become ubiquitous as data from diverse disciplines can naturally be mapped to graph structures. Social networks, such as academic collaboration networks and interaction networks over online social networking applications are used to represent and model the social ties among individuals. Information networks, including the hyperlink structure of the Web and blog networks, have become crucial mediums for information dissemination, offering an effective way to represent content and navigate through it. A plethora of technological networks, including the Internet, power grids, telephone networks and road networks are an important part of everyday life. The problem of extracting meaningful information from large scale graph data in an efficient and effective way has become crucial and challenging with several important applications and towards this end, graph mining and analysis methods constitute prominent tools. The goal of this course is to present recent and stateoftheart methods and algorithms for analyzing, mining and learning largescale graph data, as well as their practical applications in various domains (e.g., the web, social networks, recommender systems).
Schedule and Lectures
The topics of the lectures are subject to change (the following schedule outlines the topics that will be covered in the course). The slides for each lecture will be posted in
piazza just before the start of the class.
The due dates of the assignments/project are subject to change.
Week 
Date 
Topic 
Material 
Assignments/Project 
1  October 25 
○ Introduction to network science and graph mining
○ Graph theory and linear algebra recap; basic network properties

Lecture 1A
Lecture 1B
 
2  November 22 
○ Random graphs and the smallworld phenomenon
○ Powerlaw degree distribution and the Preferential Attachment model

Lecture 2A
Lecture 2B
 
3  November 29 
○ Timeevolving graphs and network models
○ Centrality criteria and link analysis algorithms

Lecture 3A
Lecture 3B
 Assignment 1 out 
4  December 6 
○ Graph clustering and community detection
 Lecture 4  
5  December 13 
○ Node similarity and link prediction
○ Graph similarity

Lecture 5A
Lecture 5B
 Project proposal due on December 16 Assignment 2 out 
6  December 20 
○ Representation learning in graphs
○ Graph sampling and summarization

Lecture 6A
Lecture 6B
 Assignment 1 due on December 23 
7  January 10 
○ Epidemic processes and cascading behavior in networks
○ Influence maximization in social networks

Lecture 7A
Lecture 7B
 Assigment 2 due on January 13 
8  January 17  Project presentations   Project final report due on January 17
Project poster session or presentations

[October 25] Lecture 1A: Introduction
Introduction to graph mining and network analysis, administrivia, course structure and overview of the topics that will be covered in the course.
Reading:
[October 25] Lecture 1B: Graph theory and linear algebra recap; basic network properties
Presentation of basic concepts in graph theory, linear algebra and spectral graph theory that will be used throughout the course. Basic network properties: degree distribution, clustering coefficient and shortest path length.
Reading:
Additional:
[November 22] Lecture 2A: Random graphs and the smallworld phenomenon
The ErdosRenyi random graph model and its basic properties. Comparison to the properties of real networks. The smallworld phenomenon and the smallworld model.
Reading:
Additional:
 Random graphs, lecture notes by Aaron Clauset (CU Boulder)
 Diameter on dregular random graphs, lecture notes by Yaron Singer (Harvard University)
 Networks: An Introduction (Chapter 12)
 P. Erdos and A. Renyi. On Random Graphs I. Publicationes Mathematicae (6) 290297, 1959
 P. Erdos and A. Renyi. On the evolution of random graphs. Magyar Tud. Akad. Mat. Kutato Int. Koezl., 1960
 D. J. Watts and S. H. Strogatz. Collective dynamics of 'smallworld' networks. Nature 393:44042, 1998
 P. S. Dodds, R. Muhamad, D. J. Watts. An Experimental Study of Search in Global Social Networks. Science 301, 2003
 D. J. Watts, P. S. Dodds, M. E. J. Newman. Identity and Search in Social Networks. Science, 296, 13021305, 2002
 M. E. J. Newman. Models of the Small World: A Review., J. Stat. Physics 2000
 J. Kleinberg. The smallworld phenomenon: An algorithmic perspective. Proc. ACM Symposium on Theory of Computing, 2000
 L. Backstrom, P. Boldi, M. Rosa, J. Ugander, and S. Vigna. Four Degrees of Separation. ACM Web Science Conference. 2012
 J. Ugander, B. Karrer, L. Backstrom, and C. Marlow. The Anatomy of the Facebook Social Graph. arXiv, 2012
[November 22] Lecture 2B: Powerlaw degree distribution and the Preferential Attachment model
Powerlaw degree distribution in real networks. How to analyze and visualize powerlaw distributions. The Preferential Attachment model. Consequences of skewed degree distribution in the robustness of real networks.
Reading:
Additional:
 A. Clauset, C.R. Shalizi, and M.E.J. Newman. Powerlaw distributions in empirical data. SIAM Review 51(4), 661703, 2009
 Networks, crowds, and markets (Chapter 18)
 Graph Mining: Laws, Tools, and Case Studies (Chapter 2 and 9)
 Bela Bollobas, Oliver Riordan, Joel Spencer and Gabor Tusnady. The degree sequence of a scalefree random graph process. Journal Random Structures and Algorithms 18(3), 2001
 M. Mitzenmacher. A Brief History of Generative Models for Power Law and Lognormal Distributions. Internet Mathematics 1(2), pp. 226251, 2004
 M. Faloutsos, P. Faloutsos, C. Faloutsos. On PowerLaw Relationships of the Internet Topology. In SIGCOMM, 1999.
 R. Albert, H Jeong, and A.L. Barabasi. The diameter of the world wide web. Nature 401, 130131, 1999
 A.L Barabasi, R. Albert. Emergence of scaling in random networks. Science, 286, 1999
[November 29] Lecture 3A: Timeevolving graphs and network models
Properties of timeevolving graphs. The ForestFire and Kronecker graph models.
Reading:
Additional:
 J. Leskovec, J. Kleinberg, and C. Faloutsos. Graph Evolution: Densification and Shrinking Diameters. ACM TKDD, 2007
 J. Leskovec, D. Chakrabarti, J. Kleinberg and C. Faloutsos. Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication. Proc. European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2005.
 C. Seshadhri, A. Pinar, and T. G. Kolda. An InDepth Analysis of Stochastic Kronecker Graphs. Journal of the ACM, 2013
 M. Mahdian and Y. Xu. Stochastic Kronecker Graphs. Proc. 5th Workshop on Algorithms and Models for the Web Graph (WAW), 2007
 Graph Mining: Laws, Tools, and Case Studies (Chapter 11)
 A. Pinar, C. Seshadhri and T. G. Kolda. The Similarity between Stochastic Kronecker and ChungLu Graph Models. In Proc. SDM, 2012
 M. Kim, J. Leskovec. Multiplicative attribute graph model of realworld networks. Internet Mathematics, 2012
 E. Zheleva, H. Sharara, and L. Getoor. Coevolution of social and affiliation networks. Proc. KDD, 2009
[November 29] Lecture 3B: Centrality criteria and link analysis algorithms
Centrality criteria in graphs (degree, closeness, betweenness, eigenvector, Katz). Link analysis ranking algorithms (HITS and PageRank).
Reading:
Additional:
 Networks: An Introduction (Chapter 7, Sections 7.1  7.7)
 F. Bonchi, G. De Francisci Morales, and M. Riondato. Centrality Measures on Big Graphs: Exact, Approximated, and Distributed Algorithms. Tutorial at WWW, 2016.
 U Brandes. A faster algorithm for betweenness centrality. Journal of Mathematical Sociology 25(2), 163177, 2001
 T. H. Haveliwala. TopicSensitive PageRank. In Proc 11th International World Wide Web Conference, 2002
 G. Jeh and J. Widom. Scaling Personalized Web Search. In Proc. roceedings of the 12th international conference on World Wide Web (WWW), 2003
 A. Borodin, J. S. Rosenthal, G. O. Roberts, and P. Tsaparas. Finding Authorities and Hubs From Link Structures on the World Wide Web. In Proc. 10th International World Wide Web Conference, 2001
 P. Berkhin. A Survey on PageRank Computing. Internet Mathematics 2(1), pages 73120, 2005
[December 6] Lecture 4: Graph clustering and community detection
Community detection in networks. GirvanNewman algorithm. Modularity and modularity optimization (greedy, spectral, Louvain method). Spectral clustering. Community evaluation criteria. Community detection in directed networks. Overlapping community detection. Community structure of large scale networks.
Reading:
 M. Girvan and M.E.J. Newman. Community structure in social and biological networks. PNAS 99, 2002
 M.E.J. Newman. Modularity and community structure in networks. PNAS, 2006
 U. von Luxburg. Tutorial on spectral clustering. Statistics and Computing 17(4), 2007
 Networks: An Introduction (Sections 11.5, 11.6)
 G. Palla, I. Derenyi, I. Farkas, T. Vicsek. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814818, 2005
 J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney. Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large WellDefined Clusters. Internet Mathematics, 2009
Additional:
 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.E.J. Newman, M. Girvan. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113, 2004
 A. Clauset, M.E.J. Newman, C. Moore. Finding community structure in very large networks. Phys. Rev. E 70, 066111, 2004
 V. D. Blondel, J.L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008
 S. Fortunato. Community detection in graphs. Physics Reports, 2010
 Social media mining (Chapters 6)
 U. Brandes, D. Delling, M. Gaertler, R. Gorke, M. Hoefer, Z. Nikoloski, and D. Wagner. On Modularity Clustering. IEEE TKDE 20(2), 2008
 S. Fortunato and S. Barthelemy. Resolution limit in community detection. Proc. Natl. Acad. Sci., 2007
 L. Backstrom, D. Huttenlocher, J. Kleinberg, X. Lan. Group Formation in Large Social Networks: Membership, Growth, and Evolution. In Proc. KDD, 2006
 J. Yang and J. Leskovec. Defining and Evaluating Network Communities based on GroundTruth. In ICDM, 2012
 S. Fortunato. Community detection in graphs. Physics Reports, 2010
 Social media mining (Chapters 6)
 J. Shi and J. Malik. Normalized Cuts and Image Segmentation. IEEE Transactions on Pattern Analysis And Machine Intelligence, vol. 22, no. 8, 2000
 A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: analysis and an algorithm . In NIPS, 2001
 F. D. Malliaros and M. Vazirgiannis. Clustering and Community Detection in Directed Networks: A Survey. Physics Reports, 533(4): 95142, 2013
[December 13] Lecture 5A: Link prediction
Node similarity measures. Link prediction in networks.
Reading:
Additional:
 G. Jeh and J. Widom. SimRank: A Measure of StructuralContext Similarity. In KDD, 2002
 P. Gupta, A. Goel, J. Lin, A. Sharma, D. Wang, and R.Zadeh. WTF: The Who to Follow Service at Twitter. In WWW, 2013
 A. Clauset, C. Moore, M.E.J. Newman. Hierarchical structure and the prediction of missing links in networks. Nature, 2008
 L.A. Adami and E. Adar. Friends and neighbors on the Web. Social Networks 25, 211230, 2003
 L. Lua and T. Zhoua. Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications 390(6), 11501170, 2011
[December 13] Lecture 5B: Graph similarity
Graph similarity. Graph kernels.
Reading:
Additional:
 P. Papadimitriou, A. Dasdan and H. GarciaMolina. Web Graph Similarity for Anomaly Detection. Journal of Internet Services and Applications 1(1), pp. 1930, 2010
 S. Soundarajan, T. EliassiRad, and B. Gallagher. A Guide to Selecting a Network Similarity Method. In SDM, 2014
 K. M. Borgwardt and H. Kriegel. Shortestpath kernels on graphs. In ICDM, 2005
 N. Shervashidze, T. Petri, K. Mehlhorn, K. M. Borgwardt, and S. Vishwanathan. Efficient Graphlet Kernels for Large Graph Comparison. In AISTATS, 2009
 T. Gartner, P. Flach, and S. Wrobel. On Graph Kernels: Hardness Results and Efficient Alternatives. Learning Theory and Kernel Machines, 2003
 X. Gao, B. Xiao, D. Tao, and X. Li. A survey of graph edit distance. Pattern Analysis and Applications 13(1), 113129, 2010
 R.C. Wilson and P. Zhu. A Study of Graph Spectra for Comparing Graphs and Trees. Journal of Pattern Recognition 41(9), 28332841, 2008
[December 20] Lecture 6A: Representation learning in graphs
Methods for learning node embeddings in graphs (LINE, DeepWalk and node2vec).
Reading:
 J. Tang, M. Qu, M. Wang, M. Zhang, J. Yan, and Q. Mei. LINE: Largescale information network embedding. In WWW, 2015
 B. Perozzi, R. AlRfou, and S. Skiena. DeepWalk: Online Learning of Social Representations. In KDD, 2014
 A. Grover and J. Leskovec. node2vec: Scalable Feature Learning for Networks. In KDD, 2016
Additional:
 J. B. Tenenbaum, V. De Silva, and J. C. Langford. A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science, 290:5500, pp. 23192323, 2000
 S. T. Roweis and L. K. Saul. Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science, 290:5500, pp. 23232326, 2000
 M. Belkin and P. Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, 2001
 T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv, 2013
 T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In NIPS, 2013
 S. Cao, W. Lu, and Q. Xu. GraRep: Learning Graph Representations with global structural information. In CIKM, 2015
 S. Cao, W. Lu, and Q. Xu. Deep Neural Networks for Learning Graph Representations. In AAAI, 2016
 W. L. Hamilton, R. Ying, and J. Leskovec. Representation Learning on Graphs: Methods and Applications. IEEE Data Engineering Bulletin, 2017
 P. Goyal and E. Ferrara. Graph Embedding Techniques, Applications, and Performance: A Survey. arXiv, 2017
 A. Narayanan, M. Chandramohan, L. Chen, Y. Liu, and S. Saminathan. subgraph2vec: Learning Distributed Representations of Rooted Subgraphs from Large Graphs. In KDD (MLG Workshop), 2016
 M. Niepert, M. Ahmed, and K. Kutzkov. Learning Convolutional Neural Networks for Graphs. In ICML, 2016
[December 20] Lecture 6B: Graph sampling and summarization
Graph sampling. Graph sparsification for community detection. Graph summarization.
Reading:
Additional:
 P. Hu and W. C. Lau. A Survey and Taxonomy of Graph Sampling. arXiv, 2013
 M. Gjoka, M. Kurant, C.T. Butts, and A. Markopoulou. Walking in Facebook: A Case Study of Unbiased Sampling of OSNs. In INFOCOM, 2010
 C. Hubler, H.P. Kriegel, K. Borgwardt, and Z. Ghahramani. Metropolis Algorithms for Representative Subgraph Sampling. In ICDM, 2008
 A.S. Maiya and T.Y. BergerWolf. Benefits of Bias: Towards Better Characterization of Network Sampling. In KDD, 2011
 K. LeFevre and E. Terzi. GraSS: Graph Structure Summarization. In SDM, 2010
 Y. Liu, A. Dighe, T. Safavi, and D. Koutra. Graph Summarization: A Survey. arXiv, 2016
[January 10] Lecture 7A: Cascading behavior in networks
Cascading behavior. Models of virus and information probagation.
Reading:
Additional:
 Networks: An Introduction (Chapter 17: 17.1  17.4)
 Network science (Chapter 10)
 D. Chakrabarti, Y. Wang, C. Wang, J. Leskovec, and C. Faloutsos. Epidemic thresholds in real networks. ACM Trans. Inf. Syst. Secur. 10(4): 1:11:26, 2008
 B.A. Prakash, D. Chakrabarti, N.C. Valler, M. Faloutsos, and C. Faloutsos. Threshold conditions for arbitrary cascade models on arbitrary networks. Knowledge and Information Systems 33 (3), 2012
 M. Goetz, J. Leskovec, M. Mcglohon, and C. Faloutsos. Modeling blog dynamics. In ICWSM, 2009
 E. Adar and L. Adamic. Tracking information epidemics in blogspace. In Web Intelligence, 2005
 J. Ugander, L. Backstrom, C. Marlow, and J. Kleinberg. Structural Diversity in Social Contagion. PNAS 109 (16), 2012
 A. Goyal, F. Bonchi, and L.V.S. Lakshmanan. Learning influence probabilities in social networks. In WSDM, 2010
[January 10] Lecture 7B: Influence maximization
Influence maximization in social networks. The Greedy algorithm. Outbreak detection in networks.
Reading:
 D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the Spread of Influence through a Social Network. In KDD, 2003
 M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik, H. E. Stanley, and H. A. Makse. Identification of influential spreaders in complex networks. Nature Physics 6, 888893, 2010
Additional:
 A. Goyal, W. Lu, and L. S.V. Lakshmanan. SIMPATH: An Efﬁcient Algorithm for Inﬂuence Maximization under the Linear Threshold Model. In ICDM, 2011
 J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. VanBriesen, and N. Glance. Costeffective Outbreak Detection in Networks. In KDD, 2007
 W. Chen, Y. Wang, and S. Yang. Efficient Influence Maximization in Social Networks. In KDD, 2009
 W. Chen, Y. Yuan, and L. Zhang. Scalable Influence Maximization in Social Networks under the Linear Threshold Model. In ICDM, 2010
 A. Goyal, W. Lu, and L. V.S. Lakshmanan. CELF++: optimizing the greedy algorithm for influence maximization in social networks. In WWW, 2011
 E. Cohen, D. Delling, T. Pajor, and R.F. Werneck. Sketchbased Influence Maximization and Computation: Scaling up with Guarantees. In CIKM, 2014
 Y. Wang, G. Cong, G. Song, and K. Xie. Communitybased greedy algorithm for mining topK influential nodes in mobile social networks. In KDD, 2010
 M. Richardson and P. Domingos. Mining the Network Value of Customers. In KDD, 2001
 M. Richardson and P. Domingos. Mining KnowledgeSharing Sites for Viral Marketing. In KDD, 2002
 J. Leskovec, L. Adamic, and B. Huberman. The Dynamics of Viral Marketing. TWEB, 2007
 A. Goyal, F. Bonchi, and K. V.S. Lakshmanan. A DataBased approach to Social Influence Maximization. In PVLDB, 2012
Course Structure
Learning objectives
The course aims to introduce students to the field of graph mining and network analysis by:
 Covering a wide range of topics, methodologies and related applications.
 Giving the students the opportunity to obtain handson experience on dealing with graph data and graph mining tasks.
We expect that by the end of the course, the students will have a thorough understanding of various graph mining and learning tasks, will be able to analyze largescale graph data as well as to formulate and solve problems that involve graph structures.
Prerequisites
There is no official prerequisite for this course. However, the students are expected to:
 Have basic knowledge of graph theory and linear algebra.
 Be familiar with fundamental data mining and machine learning tasks.
 Be familiar with at least one programming language (e.g., Python or any language of their preference).
In the second lecture, we will review basic concepts in graph theory, linear algebra and machine learning.
Reading material
Most of the material of the course is based on research articles. Some of the topics are also covered by the following books:
 David Easley and Jon Kleinberg. Networks, Crowds, and Markets. Cambridge University Press, 2010.
 Mark E.J. Newman. Networks: An Introduction. Oxford University Press, 2010.
 Deepayan Chakrabarti and Christos Faloutsos. Graph Mining: Laws, Tools, and Case Studies. Synthesis Lectures on Data Mining and Knowledge Discovery, Morgan and Claypool Publishers, 2012.
 Reza Zafarani, Mohammad Ali Abbasi and Huan Liu. Social Media Mining. Cambridge University Press, 2014.
 AlbertLaszlo Barabasi. Network Science. Cambridge University Press, 2016.
Evaluation
The evaluation of the course will be based on the following:
 Two assignments: the assignments will include theoretical questions as well handson practical questions and will familiarize the students with basic graph mining and analysis tasks.
 Project: this will be the main component for the evaluation of the course. The students are expected to form groups of 34 people, propose a topic for their project, and submit a final project report (it would also be interesting to organize a poster session at the end of the quarter). Please, read the project section for more details.
The grading will be as follows:
Assignment 1 (individually):  25% 
Assignment 2 (groups of 34 students):  25% 
Project (groups of 34 students):  50% 
Academic integrity
All of your work must be your own. Don't copy another student's assignment, in part or in total, and submit it as your own work. Acknowledge and cite source material in your papers or assignments.
Project
Details about the project of the course can be found on piazza.
Resources
Datasets
Software tools
 NetworkX: Python software package for graph analytics
 igraph: collection of software packages for graph theory and network analysis (Python, C++ and R)
 SNAP: high performance system for the analysis of large network (C++ and Python)
 Gephi: graph visualization and exploration software
Related conferences
Please find below a list of conferences related to the contents of the course (mostly in the field of data mining, social network analysis and the Web). We provide the DBLP website of each venue where you can access the proceedings (papers, tutorials, etc).
Check out the website of each conference (e.g.,
KDD 2016 ) for more information.