The 5th International Workshop on Complex Networks and their Applications

November 30 - December 02 2016

Milan, Italy

Contribution Types

Two types of contributions are accepted:

  • Full Paper: Full Papers are recommended to be between 8-10 pages. They should not exceed 12 pages in total including bibliography.
  • Extended Abstract: Extended Abstracts are recommended to be between 1-2 pages. They should not exceed 3 pages.

We will not accept any paper that, at the time of submission, is under review or has already been published or accepted for publication in a journal or conference. This restriction does not apply to extended abstracts since they are not targeted for publication in the proceedings. If in doubt, please contact the PC Chairs.


Each submission must follow the Springer publication format available on the website of Studies in Computational Intelligence Series in the Authors and Editors instructions entry.

  • LaTeX templates are available here
  • Word templates are available here

For more information refer to the Springer Website.


All contributions should be submitted electronically online via EasyChair.

  1. Visit
  2. If you haven't got a login, you’ll be asked to create one
  3. Once you’re logged, select the option “New Submission” and enter the authors' information
  4. Enter the abstract of your contribution. In case you are submitting an Extended Abstract, the EasyChair field "abstract" should not be used for writing the entire Extended Abstract
  5. Enter at least 3 keywords
  6. Select between 1 to 3 topics from the list provided
  7. Select your contribution category and upload your abstract or paper. Click on “Submit” to upload your contribution to the reviewing system. Only pdf files using the proper format will be accepted. Submissions not meeting these guidelines risk rejection without consideration of their merits.

After this process, you should receive an email indicating the submission was successful. If you don’t receive this email you should contact the program chairs. Please check your spam folder as the automated message may be stored there.


All submitted contributions will be carefully evaluated based on originality, significance, technical soundness, and clarity of expression by at least two reviewers. The organizers will examine the reviews and make the final papers selection.


Full papers accepted for publication will be published by Springer-Verlag on the Studies in Computational Intelligence Series. Authors will be required to transfer copyright to Springer. The books of this series are submitted for indexing to SCOPUS, DBLP, MathSciNet, Zentralblatt Math, MetaPress, Ulrichs and Springerlink.

Book of Abstracts

Accepted Extended Abstracts will be published in the Book of Abstract (with ISBN) along with the abstracts of the keynote presentations.

General Chair

Hocine Cherifi
University of Burgundy, France

Program Co-Chairs

Sabrina Gaito
University of Milan, Italy
Walter Quattrociocchi
IMT Institute for Advanced Studies, Italy
Alessandra Sala
Bell Labs Dublin, Ireland

Poster Chairs

Chantal Cherifi
University of Lyon2, France
Antonio Scala
Sapienza University, Italy

Publicity Chair

Bruno Gonçalves
New York University, USA

Local Arrangement Committee

Sabrina Gaito
University of Milan, Italy
Carlo Piccardi
Politecnico di Milano, Italy
Giorgio Valentini
University of Milan, Italy
Matteo Re
University of Milan, Italy
Fabio Della Rossa
Politecnico di Milano, Italy
Matteo Zignani
University of Milan, Italy
Christian Quadri
University of Milan, Italy

Program Committee


Matteo Zignani, University of Milan, Italy

Registration rates

The registration costs and benefits depend on the registration category and on the date of registration. Please read the instructions to make sure you register for the correct category. If in doubt, please contact Hocine Cherifi (


  • At least one author of each accepted contribution must be registered by the author registration deadline (October 25, 2016) in order for that contribution to appear in the proceedings or book of abstract and to be scheduled for presentation.
  • Attendees must register under one of the following registration categories.
  • All registration categories include access to technical sessions, lunches, coffee breaks and opening reception.
CategoryEarly (by Oct 20, 2016)Late (After Oct 20, 2016)Dinner BanquetProceedings
Paper Registration590€690€1 IncludedIncluded
Abstract Registration350€450€1 IncludedNot Included
Extra Paper300€350€-Not Included
Extra Abstract200€250€-Not Included
Regular Attendee350€400€1 IncludedNot Included
Student Attendee180€200€Not IncludedNot Included
  • Student fee is for non-author attendees. It is not available for authors of papers and abstracts.
  • Extra paper or abstract registration rate is applicable only once for each full registration. Authors with 3 papers must pay 2 papers registration and one extra paper.


Extra Dinner Banquet Ticket70€
Extra Copy of Proceedings70€
Extra Page Fee (max 2 Pages over the 12 pages limit)100€ per extra page

Workshop Registration Fees are non-Refundable


Tutorials will be offered on November 29, 2016. They will run from 1:30 PM to 6:00 PM. Please note that you do not need to register for the workshop to register for the tutorials.

Registration for 1 Tutorial150€
Registration for 2 Tutorials200€

Registration is on a first-come, first-served basis and is limited to 20 participants per tutorial. All the interested participants are kindly invited to register before October 25, 2016. It will be possible to register to tutorials after that date, provided that capacity constraints are not already violated.

Payment Methods

By Credit Card (recommended)

Please use the following link

By Wire Transfer

The net amount received must be equal to the registration fees due. Please send sufficient funds to cover the registration fee and any fee your bank may charge.
Ask your bank to include the following identifying information: Workshop Complex Networks 2016.
Send Wire transfer to:

Beneficiary addressBP 27877, 21078 DIJON CEDEX - FRANCE
Account# (IBAN):FR7610071210000000100392010
Bank addressDIJON TRESOR PUBLIC, Place de la Banque, 21000 Dijon, France
VAT NUMBERFR88192112373

Complete and send by email to :

  • the registration form
  • a scanned copy of your transfer receipt as a proof of payment

Host City: Milan, Italy

Milan, the capital of Lombardy, is Italy's economic and financial heart. Fashion, design, finance and media are the advanced sectors that drive its economy. Milan has eleven university centers with 44 faculties and 174,000 new students each year. This history in education and advanced research goes hand in hand with invention and innovation from the Romans to Leonardo da Vinci and Marconi and continues in recent years. The great Italian masters of the past have left their sign on the history of art, followed in the 20th century by internationally influential Futurists and Arte Povera group. Milan offers a total of 150 art galleries, 28 museums and 38 theatres.  Opera lovers will be able to enjoy performances at La Scala. For more information about the city and what’s going on in Milan refer to the tourism official website.

Host Institution: The University of Milan

A leading institute in Italy and Europe for scientific productivity, the University of Milan is the largest university in the region, with approximately. The University of Milan also possesses a remarkable artistic and cultural heritage that includes important historic buildings, inherited and acquired collections, archives, botanical gardens and the old Brera Observatory commissioned by Maria Teresa of Austria. The University’s departments are housed in important historic edifices in the centre of Milan and in modern buildings in the area known as Città Studi (the City of Studies). The University also has a Choir and its own Orchestra, which actively contributes to the cultural life of the city and receives international acknowledgements on an increasingly frequent basis.

Venue: Sala Napoleonica di Palazzo Greppi

Via S. Antonio, 12

The 18th-century Palazzo Greppi has been designed by Giuseppe Piermarini who built the Scala Theatre in Milan.

Palazzo Greppi commissioned by Count Antonio Greppi, banker and entrepreneur recently nobility, was among the first models of the Milanese neoclassicism.

The staircase and the main floor rooms still retain the neoclassical decoration work of Giocondo Albertolli, Martin Knoller and Andrea Appiani.

How to reach the Workshop Venue

  • Underground line MM1 Red line get off at stop “Duomo”, walk 600 meters
  • Underground line MM3 Yellow line get off at stop “Missori”, walk 450 meters
  • Surface lines 60, 73 bus, get off at stop “L.go Augusto”, walk 500 meters
  • Surface line 12, 27, 24, 16 trolley (tram), get off at stop Missori M3


Guido Caldarelli studied Statistical Physics, and he works in the field of Complex Networks. He got his degree in 1992 in Rome (La Sapienza), his PhD in 1996 in Trieste (SISSA). After Postdocs in Manchester and Cambridge he became firstly "Research Assistant" in INFM and secondly "Primo Ricercatore" at ISC-CNR where he is still working with many friends and colleagues. Presently he is Full Professor of Physics at IMT Lucca, and a LIMS Fellow. From November 15th 2015 he is the Vice-President of the Complex Systems Society.
Following the financial crisis of 2007-2008, a deep analogy between the origins of instability in financial systems and in complex ecosystems has been pointed out: in both cases, topological features of network structures influence how easy it is for distress to spread within the system. However, in financial network models, the intricate details of how financial institutions interact typically play a decisive role. Hence, a general understanding of precisely how network topology creates instability remains lacking. Here we show how processes that are widely believed to stabilise the financial system, integration and diversification, can actually drive it towards instability, as they contribute to create cyclical structures which tend to amplify financial distress, thereby undermining systemic stability and making large crises more likely. This result holds irrespective of the precise details of how institutions interact, and demonstrates that policy-relevant analysis of the factors affecting financial stability can be carried out while abstracting away from such details.
Raissa D’Souza is Professor of Computer Science and of Mechanical Engineering at the University of California, Davis, as well as an External Professor at the Santa Fe Institute. She received a PhD in Statistical Physics from MIT in 1999, then was a postdoctoral fellow at Bell Laboratories and at Microsoft Research. Her interdisciplinary work on network theory spans the fields of statistical physics, theoretical computer science and applied math, and has appeared in journals such as Science, PNAS, and Physical Review Letters. She serves on the editorial board of numerous international mathematics and physics journals, is a member of the World Economic Forum's Global Agenda Council on Complex Systems, and is the President of the Network Science Society.
Networks are at the core of modern society, spanning physical, biological and social systems. Each distinct network is typically a complex system, shaped by the collective action of individual agents and displaying emergent behaviors. Moreover, collections of these complex networks often interact and depend upon one another, which can lead to unanticipated consequences such as cascading failures and novel phase transitions. Simple mathematical models of networks can provide important insights into such phenomena. Here we will cover several such models, beginning with control of phase transitions in an individual network then moving on to modeling phenomena in coupled networks, including cascading failures and optimal interdependence.
After a PhD in Physics at ULB, and Post-docs at ULg, UCLouvain and Imperial College, he is currently associate professor in the Department of Mathematics of the University of Namur. His recent research includes the development of algorithms to uncover information in large-scale networks, the study of empirical data in social and biological systems, and the mathematical modelling of human mobility and diffusion on networks. He has authored more than 60 publications in peer-reviewed journals and conference proceedings, with around 5000 citations (Google Citations). He also acts as an academic editor for PLoS One and the European Physical Journal B.
When modelling dynamical systems on networks, it is often assumed that the process is Markovian, that is future states depend only upon the present state and not on the sequence of events that preceded it. Examples include diffusion of ideas or diseases on social networks, or synchronisation of interacting dynamical units. In each case, the dynamics is governed by coupled differential equation, where the coupling is defined by the adjacency matrix of the underlying network. The main purpose of this talk is to challenge this Markovian picture. We will argue that non-Markovian models can provide a more realistic picture in the case of temporal networks where edges change in time, or in situations when pathways can be measured empirically. We will focus on the importance of non-Poisson temporal statistics, and show analytically the impact of burstiness on diffusive dynamics, before turning to applications and incorporating memory kernels in predictive models of retweet dynamics.
Prof. Yamir Moreno is the head of the Complex Systems and Networks Lab (COSNET) since 2003 and is also affiliated to the Department of Theoretical Physics of the Faculty of Sciences, University of Zaragoza. He is the Deputy Director of the Institute for Bio-computation and Physics of Complex Systems (BIFI) and member of its Government Board and Steering Committee. He has been working on nonlinear dynamical systems coupled to complex structures, transport processes and diffusion with applications in communication and technological networks, dynamics of virus and rumors propagation, game theory, systems biology, the study of more complex and realistic scenarios for the modeling of infectious diseases, synchronization phenomena, the emergence of collective behaviors in biological and social environments, the development of new optimization data algorithms and the structure and dynamics of socio-technicaland biological systems. He has published more than 145 scientific papers in international refereed journals and he serves as reviewer for around 30 scientific journals and research agencies. His research works have collected more than 9300 citations (h=39). At present, he is a member of the Editorial Board of Scientific Reports, Applied Network Science and Journal of Complex Networks, and Academic Editor of PLoS ONE. Prof. Moreno is the elected President of the Complex Systems Society (CSS) and also belongs to its Executive Committee and Council. He is also the Vice-President of the Network Science Society and a member of the Future and Emerging Technology Advisory Group of the European Union’s Research Program: H2020. Besides, he belongs the Advisory Board of the WHO Collaborative Center “Complexity Sciences for Health Systems” (CS4HS), whose headquarters is at the University of British Columbia Centre for Disease Control, in Vancouver, Canada. He is a Fellow of the Institute for Scientific Interchange Foundation (ISI), Turin, Italy since 2013.
The availability of highly detailed data of real-world systems have allowed to study systems that are made up of multiple layers. In this talk, we will revise recent advances on the topic of multilayer networked systems. First, we will discuss how to represent these networks and what kind of metrics are needed to capture the new topological complexity arising from the interdependency of the layers. Secondly, we will also study contagion processes on these topologies and present results regarding the interplay between the critical properties of the system and its structural scales. To summarize, we will discuss possible future directions in this new, but very active field of research.
Eiko Yoneki is a Research Fellow in the Systems Research Group of the University of Cambridge Computer Laboratory. She leads a group called ‘data centric systems and networking’, where current research focuses on the exploration of new abstractions for supporting the design and implementation of robust and heterogeneous large-scale data processing. More information can be found at
The emergence of big data requires fundamental new methodology for data analysis, processing, and information extraction. The main challenge here is to perform efficient and robust data processing, while adapting to the underlying resource availability in a dynamic, large-scale computing environment. I would introduce our recent work on the graph processing that have billion-scale of vertices and edges in a commodity single computer, which requires secondary storage as external memory. Executing algorithms results in access to such secondary storage and performance of I/O takes an important role, regardless of the algorithmic complexity or runtime efficiency of the actual algorithm in use.
Ben Zhao is a Professor at the Computer Science department, U. C. Santa Barbara. He completed his M.S. and Ph.D. degrees in Computer Science at U.C. Berkeley (2000, 2004), and his B.S. from Yale (1997). He is a recipient of the National Science Foundation's CAREER award, MIT Technology Review's TR-35 Award (Young Innovators Under 35), ComputerWorld Magazine's Top 40 Technology Innovators award, Google Faculty awards, the IEEE ITC Early Career Award, and an ACM Distinguished Scientist. His work has been covered by media outlets such as New York Times, Boston Globe, MIT Tech Review, and Slashdot. He has published over 120 publications in areas of security and privacy, networked and distributed systems, wireless networks, data-intensive computing and HCI, with more than 20,000 citations (H-index 51). Finally, he has chaired a number of conferences (WOSN, WWW OSN track, IPTPS, IEEE P2P), and the upcoming World Wide Web Conference (WWW 2016). He is a co-founder and on the steering committee of the ACM Conference on Online Social Networks (COSN).
Algorithms based on complex networks affect our online experience on a daily basis. One of the most ubiquitous examples of this is the link prediction problem, which is a core part of friend recommendations on social networks like LinkedIn, Facebook, and Pinterest, and also part of broader recommendation systems like personal livestreaming on Periscope or Q&A sites like Quora. Given the success of these systems, and the decade of work on link prediction, it is reasonable to assume that this is a solved problem. Yet no quantitative study has been performed to understand just how successful (or unsuccessful) these algorithms are. Meanwhile, there are plenty of anecdotes online of poor recommendations that represent poor prediction results (e.g. Kashmir Hill, Fusion 2016). In this talk, I will present some of our recent work on taking an empirical view to the well studied problem of link prediction in dynamic networks. We implement and apply 18 link prediction algorithms (some metric-based, some machine learning based) to several traces of detailed network dynamics (Renren, Facebook, YouTube), and evaluate their prediction accuracy. We find that on absolute terms, link prediction accuracy is embarrassingly poor across the board, highlighting the fact that this is still very much an open problem. Machine learning approaches tend to outperform relatively, but are often prohibitively high computation costs. We then propose a novel approach to build "prediction filters” using past patterns in network dynamics. Evaluated on our large datasets, our results significantly boost prediction accuracy across all algorithms.
Professor Estrada has an internationally leading reputation for shaping and developing the study of complex networks. His expertise ranges in the areas of network structure, algebraic network theory, dynamical systems on networks and the study of random models of networks. He has a distinguished track record of high-quality publications, which has attracted more than 8, 500 citations. His h-index (number of papers with at least h citations) is 53. His publications are in the areas of network theory and its applications to social, ecological, engineering, physical, chemical and biological real-world problems. Professor Estrada has published two text books on network sciences both published by Oxford University Press in 2011 and 2015, respectively. He has demonstrated a continuous international leadership in his field where he has been invited and plenary speaker at the major conferences in network sciences and applied mathematics. His research interests include the use of matrix functions; random geometric networks; generalised Laplacian operators for networks; generalised diffusion models for networks; study of indirect peer pressure over consensus dynamics on networks; applications of network sciences to oil and gas exploration; spatial efficiency of networks; Euclidean geometrical embedding of networks, among many others.
Consensus is well documented across the social sciences, with examples ranging from behavioral flocking in popular cultural styles, emotional contagion, collective decision making, pedestrians’ walking behavior, and others. We can model consensus in a social group by encoding the state of each individual at a given time in a vector. The group reaches consensus at when the difference in the “opinions” for every pair of individuals is asymptotically zero, and the collective dynamics of the system is modeled by a diffusion equation dominated by the graph Laplacian. Decisions in groups trying to reach consensus are frequently influenced by a small proportion of the group who guides or dictates the behavior of the entire network. In this situation a group of leaders indicates and/or initiates the route to the consensus, and the rest of the group readily follows their attitudes. The study of leadership in social groups has always intrigued researchers in the social and behavioral sciences. Specifically, the way in which leaders emerge in social groups is not well understood. Leaders may emerge either randomly in response to particular historical circumstances or from the individual having the most prominent position (centrality) in the social network at any time. In this tutorial I will introduce the theoretical model of consensus in a network, for the general case of undirected as well as directed ones. First, I will introduce the mathematical concepts of the model, and show when in every case there is a consensus in the network. I will also introduce some properties of the Laplacian matrix for networks that will help to understand the main results of the model. Then, I will introduce a controllability problem and its solution in networks consisting of leaders and followers. Following this initial part I will how to use Matlab to model a consensus process in a given network (codes will be provided to participants). At this point I will motive the necessity of considering the indirect influence of peers apart from the direct peers pressure. In mathematical terms I will make a generalization of the Laplacian matrix on graphs to consider the k-path Laplacians and their transform. Using this transformed k-path Laplacians I will show how to study a few interesting topics on networks, such as the controllability of networks, the selection of leaders, the diffusion of innovations under direct+indirect peers pressure. Finally, I will prove and illustrate how the consensus and diffusion of innovations can be superdiffusive or ballistic in complex networks under the effect of direct and indirect peers pressure. Some examples, such as diffusion of methods among high schools or the adoption of a biotechnological product among farmers will be used in the tutorial.
Bruno Gonçalves is a Data Science fellow at NYU's Center for Data Science while on leave from a tenured faculty position at Aix-Marseille Université. He has a strong expertise in using large scale datasets for the analysis of human behavior. After completing his joint PhD in Physics, MSc in C.S. at Emory University in Atlanta, GA in 2008 he joined the Center for Complex Networks and Systems Research at Indiana University as a Research Associate. From September 2011 until August 2012 he was an Associate Research Scientist at the Laboratory for the Modeling of Biological and Technical Systems at Northeastern University. Since 2008 he has been pursuing the use of Data Science and Machine Learning to study human behavior. By processing and analyzing large datasets from Twitter, Wikipedia, web access logs, and Yahoo! Meme he studied how we can observe both large scale and individual human behavior in an obtrusive and widespread manner. The main applications have been to the study of Computational Linguistics, Information Diffusion, Behavioral Change and Epidemic Spreading. He is the author of 60+ publications with over 3800+ Google Scholar citations and an h-index of 26. In 2015 he was awarded the Complex Systems Society's 2015 Junior Scientific Award for "outstanding contributions in Complex Systems Science" and he is the editor of the book Social Phenomena: From Data Analysis to Models (Springer, 2015).
The data deluge we currently witnessing presents both opportunities and challenges. Never before have so many aspects of our world been so thoroughly quantified as now and never before has data been so plentiful. On the other hand, the complexity of the analyses required to extract useful information from these piles of data is also rapidly increasing rendering more traditional and simpler approaches simply unfeasible or unable to provide new insights.
In this tutorial we provide a practical introduction to some of the most important algorithms of machine learning that are relevant to the field of Complex Networks in general, with a particular emphasis on the analysis and modeling of empirical data. The goal is to provide the fundamental concepts necessary to make sense of the more sophisticated data analysis approaches that are currently appearing in the literature and to provide a field guide to the advantages an disadvantages of each algorithm.
In particular, we will cover unsupervised learning algorithms such as K-means, Expectation-Maximization, and supervised ones like Support Vector Machines, Neural Networks and Deep Learning. Participants are expected to have a basic understanding of calculus and linear algebra as well as working proficiency with the Python programming language.

Extended version of accepted contributions (full papers and extended abstracts) will be invited for publication in special issues of the journals:

Papers will be subject to a fast track review procedure.

The manuscript submission deadline is February 15, 2017.

Papers will be published as soon as they are accepted.