CS 695  Network Science: Principles and Applications (Spring 2019)
Information

Instructors: Amarda Shehu amarda\AT\gmu.edu
Place and Time: Innovation Hall #136, M 4:307:10 pm
Office Hours: ENGR #4452, M 3:304:30 pm, F 11:30 am 1:30 pm
Description

The objective of this course is to introduce students to complex systems and networkbased treatments of such systems. Complex systems, whether living or abstract, can be represented as static or dynamic networks of many interacting components. They are typically composed of components that are much simpler in behavior or function than the system. The complex behavior of a multicomponent system is an emergent property of the network that can be constructed to describe the local relationships between the components that make up the system.
Network science is a new discipline that investigates the topology and dynamics of such complex networks, aiming to better understand the behavior, function and properties of the underlying systems. Applications of network science address physical, informational, biological, cognitive, and social systems.
In this course, we will emphasize the fundamental underpinnings of network science to graphtheoretic concepts and graph algorithms. We will study algorithmic, computational, and statistical methods of network science. We will also highlight diverse applications in machine learning, robotics, communications, biology, ecology, brain science, sociology, and economics. The course will go beyond the strictly structural concepts of smallworld and scalefree networks, focusing on dynamic network processes such as epidemics, synchronization, or adaptive network formation.
Target Audience

Target audience: Graduate students that have completed two core courses. Students are encouraged to contact the instructors for more information on what fundamental background knowledge is assumed for students to do well in this course.
Format

The class will emphasize active participation by students. Students can provide "minitalks" (10 minutes) on specific topics that will be announced in class. Students should also actively participate in discussions during the lectures. Attendance is mandatory.
Categorization

The final project, whose topic needs to be negotiated with the instructors, determines the area satisfied by this course. Both instructors need to agree on the area based on the topic of the project.
Grade Breakdown

Homeworks (3): 45%
Minitalk: 10%
Class Participation: 5%
Final Project: 40%
A Very Tentative and Overambitious List of Possible Topics

[1] Overview of Network Science, History, and Relation to Graph Theory (1.5 WKS)
 Required reading:
 What is network science? by Ulrik Brandes et al. (Editorial from a new Network Science journal)
 The architecture of complexity by AlbertLászló Barabási
 Why Model? by J.Epstein (why abstract modeling even in era of "big data")
 Optional reading:
 Networks in Neuroscience: Complex brain networks: graph theoretical analysis of structural and functional systems by Bullmore and Sporns
 Networks in Social Science: Network Analysis in the Social Sciences by Stephen Borgatti et al.
 Networks in Economics: Economic Networks: The New Challenges by Frank Schweitzer et al.
 Networks in Ecology: Networks in ecology by Jordi Bascompte
 Networks and the Web: Web science: an interdisciplinary approach to understanding the web by James Hendler et al.
 Networks and the Internet: Network Topologies: Inference, Modelling and Generation by Hamed Haddadi et al.

[2] Network Analysis Metrics and Relation to Graphtheoretic Concepts (1.5 WKS)
Paths, components, degree distribution, clustering, degree correlations (assortativity)
Centrality metrics (and meaning across application domains)
A fast algorithm for the computation of betweenness centrality
Metrics for weighted or spatial networks

Required reading:
 Complex networks: Structure and dynamics by S.Boccaletti et al. (sections 2.1, 2.4, and 2.5)
 A Faster Algorithm for Betweenness Centrality by U.Brandes (Betweenness centrality and how to compute it efficiently)

Optional reading:
 Centrality measures in spatial networks of urban streets by P.Crucitti et al.
 The architecture of complex weighted networks by A.Barrat et al.
 Parallel Algorithms for Evaluating Centrality Indices in RealWorld Networks by D.Bader and K.Madduri.
 [3] Emergent Properties of Many Real Networks (2 WKS)
Smallworld property
Scalefree property and heavytailed degree distributions
Hierarchy  Modularity
Network motifs
Networkcentric and motifcentric algorithms for motif detection

Required reading:
 The structure and function of complex networks by M.Newman (section III(A,B,C,E,F) and Section IVA)
 Collective dynamics of smallworld networks by D.Watts and S.Strogatz (A classic that largely started the "network science" area and transferred the concept of "smallworld networks" from sociology to many other network contexts)
 Emergence of scaling in random networks by R.Albert and A.L.Barabasi (The second classic in network science  it started the "scale free networks" research thread)
 Network Motifs Simple Building Blocks of Complex Networks by R.Milo et al. (Networks contain certain subgraphs that are much more common than in random graphs)

Optional reading:
 Hierarchical organization in complex networks by E.Ravasz and A.L.Barabasi (Networks also show hierarchical organization. There are different ways to think about network hierarchies  this is just one of them)
 Scale_free networks in cell biology by R.Albert
 SmallWorld Networks and Functional Connectivity in Alzheimer’s Disease by C.Stam et al.
 Disrupted smallworld networks in schizophrenia by Y.Liu et al.

[4] Network Models (2 WKS)
Random networks (G(n,p) and generative model)
WattsStrogatz model
Preferential attachment and its variants
Kleinberg's duplicationbased model
Optimizationbased network formation models (e.g., HOT)

Required reading:
 Chapters 3, 4 and 5 of Network Science book by AL.Barabasi (Primary readings for this week and the next.)
 The Web as a graph  measurements, models and methods by J.Kleinberg (This is the first description of the duplicationbased model. It is also one of the first graphcentered studies of the WWW.)
 Heuristically Optimized Tradeoffs  A New Paradigm for Power Laws in the Internet by A.Fabrikant (One of the first papers that created a connection between heavytailed degree distributions and optimization principles.)

Optional reading:
 Random graphs with arbitrary degree distributions and their applications by M.Newman et al. (This is probably the simplest way to analyze random graphs with a given degree distribution. It also gives resuts for directed and bipartite graphs.
 Application paper, Optional reading) A Model of LargeScale Proteome Evolution by R.Sole et al. (This is basically the duplicationbased network generation model applied in the context of proteome evolution.)
 [5] Algorithms for Community Detection (2WKS)
Graph partitioning  Focus on spectral partitioning algorithm (resolution limit, surprise)
Modularity maximization methods
Hierarchical divisive and agglomerative methods
Overlapping communities, dynamic communities, and other variations
Properties of communities in realworld networks
 Required reading:
 Chapter 9 of Network Science book by AL.Barabasi (Primary reading for these two weeks.)
 Graph clustering by S.E.Schaeffer (This is a survey of graph clustering methods.)
 Community detection in graphs by S.Fortunato (This is a survey of community detection methods.)
 Community modularity for feature selection by Zhao, 2015; Zhang and Hancock, 2011
 Optional reading:
 Overlapping community detection in networks: the stateoftheart and comparative study by J.Xie et al. (This is a survey and comparison of overlapping community detection methods.)
 Statistical properties of community structure in large social and information networks by J.Leskovec et al. (How do communities look like in a wide range of realworld networks?)
 [6] Statistical Analysis of Network Data (1 WK)
Network sampling methods
Bias in traceroutelike network sampling
Network inference based on crosscorrelations
Identification of missing or spurious network edges
* 1hour guest lecture
 [7] Network Mining and Machine Learning Methods (1.5 WKS)
Anomaly detection in networks
Graph summarization
 Required Reading:
 Spectral analysis (I. S. Dhillon, Y. Guan, and B. Kulis. Kernel kmeans, spectral clustering and normalized cuts. ACM SIGKDD pg. 556–556, 2004.)
 Inference of network evolution models
 Learning evolving networks
* 1hour guest lecture highlighting related work by domainexpert faculty in department
 Optional reading:
 Spectral analysis in social networks (P. Symeonidis and N. Mantas. Spectral clustering for link prediction in social networks with positive and negative links; Social Network Analysis and Mining, 3(4):1433–1447, 2013; Fengjiao Wang, Guan Wang, Shuyang Lin, and Philip S Yu.; Concurrent goaloriented coclustering generation in social networks. IEEE Semantic Computing, pg. 350–357, 2015. Raghvendra Mall, Rocco Langone, and Johan AK Suykens).
Spectral analysis in big data networks (R. Mall, R. Langone, and J. AK Suykens. Kernel spectral clustering for big data networks. Entropy, 15(5):1567–1586, 2013)
Faithful sampling of networks (H. Zare, P. Shooshtari, A. Gupta, and R. R Brinkman. Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics, 11 (1):403, 2010)
Spectral analysis in biological networks (A. Borg, N. Lavesson, and V. Boeva. Comparison of clustering approaches for gene expression data. SCAI, pg. 55–64, 2013; Z. Yu, L. Li, J. You, H.S. Wong, and G. Han. Sc3: Triple spectral clusteringbased consensus clustering framework for class discovery from cancer gene expression profiles. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(6):1751–1765, 2012; Y. Kluger, R. Basri, J. T. Chang, and M. Gerstein. Spectral biclustering of microarray data: coclustering genes and conditions. Genome research, 13(4):703–716, 2003; J. Ruan and W. Zhang. An efficient spectral algorithm for network community discovery and its applications to biological and social networks. In Data Mining, 2007. ICDM pg. 643–648, 2007; J. Ruan, A. K. Dean, and W. Zhang. A general coexpression networkbased approach to gene expression analysis: comparison and applications. BMC Systems Biology, 4 (1):8, 2010; Y. X. R. Wang and H. Huang. Review on statistical methods for gene network reconstruction using expression data. Journal of Theoretical Biology, 362:53–61, 2014)
 [8] Network Dynamics (2.5 WKS)
Percolation and network resilience to random and targeted attacks
Growth and Densification
Rewiring
Network epidemics and epidemic threshold (SI, SIS, SIR models)
Immunization strategies
Identification of major spreaders
Computational network epidemiology
Social networks and influence
Information and behavior spreading  measurements and models
Seeding strategies to maximize influence
Influence versus homophily
Synchronization on networks
Networks with capacity constraints and overloadbased failures
Decentralized search on networks
Controlling networks
*40minute guest lecture highlighting related work by domainexpert faculty at GMU
 [9] Additional Topics depending on time and interests of students:

(a) Coevolutionary Dynamics in Adaptive Networks
Instances of adaptive networks in practice
Coevolutionary dynamics in opinion/consensus formation
Coevolutionary dynamics in epidemics
Coevolutionary effects in population dynamics

(b) Temporal networks
Instances of interdependent networks in practice
Layered networks
Cascade phenomena in intedependent networksbr/>
Temporal networks

(c) Network Formation Games
Games on networks
Strategic network formation
Evolution of cooperation on social networks
Social learning on networks
*guest lectures highlighting related work by domainexpert faculty at GMU
Materials that facilitate homework programming assignments and final project:
 Network datasets:
Mark Newmann's
Eric Kolaczyk's
Albert Barabasi's
Uri Alon's
Pajek's
Alex Arenas'
Jon Kleinberg's
Jure Leskovec's
Peter Skomoroch's
TrustLet project's
AOL dataset: 20M web queries collected from ~650k users over three months.
Dataset for the evolution of the Internet AS ecosystem between 19982008
 Network analysis tools:
Gephi
Infomap
Igraph
Statnet
Network Workbench
Pajek network visualization (Windows)
Jung network analysis
GraphViz
Uri Alon's network motif detection software
Matlab's Random Boolean Networks (RBN) toolbox
Information on Final Project and Milestones:


Ideally, the final project should have the potential to lead to an original research paper that addresses a question not previously addressed in published literature. Groups of two students are ideal; individual projects are also acceptable. Larger teams will need instructor approval. The instructor will work closely with every student/group during the semester. All projects will be presented in class during the last week of the semester.
 Project milestones:
WK 3: project proposal
WK 6: first progress report
WK 9: second progress report
WK 14: paper due
Final's day: inclass project presentations