22 August 2016 ~ 0 Comments

It’s Not All in the Haka: Networks Matter in Rugby Too

If there is a thing that I love more than looking at silly pictures on the Interwebz for work is to watch rugby for work. I love rugby: in my opinion it is the most beautiful team sport out there. It tingles my network senses: 15 men on the field have to coordinate like a single organism to achieve their goal — crossing the goal line with the ball by passing it backwards instead of forward. When Optasports made available some data collected during 18 rugby matches I felt I could not miss the opportunity for some hardcore network nerding on them. The way teams weave their collaboration networks during a match must have some relationship with their performance, and I was going to find out what this relationship might be.

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For my quest I teamed up with Luca Pappalardo and Paolo Cintia, two friends of mine who are making an impact on network and big data sports analytics, both in soccer and in cycling. The result was “The Haka Network: Evaluating Rugby Team Performance with Dynamic Graph Analysis“, a paper recently presented at the DyNo workshop in San Francisco. Our questions were:

  1. Is there a relationship between the topology of the network of passes and the success of the team?
  2. Is there a relationship between disruptions made by tackles and territorial gains?
  3. If we want to predict a team’s success, is it better to build networks of passes and disruptions for each action separately or for the entire match?
  4. Can we use these relationships to “predict” the outcome of the match?

rugby1

A passage network is simply a network whose nodes are the players of a team and the directed connections go from the player originating a pass to the player receiving the ball. We consider only completed passages: the ones that did not result in an error or lost possession. In the above picture, those are the green edges and they are always established between players belonging to the same team. In rugby, players are allowed to tackle the current ball carrier of the opponent team. When that happens, we create another directed edge, this time in what we call “disruption network”. The aim of a tackle is to prevent the opponent team from gaining meters. These are the red edges in the above picture and can only be established between players belonging to opposite teams. The picture you see is the collection of all passes and tackles which happened in the Italy vs New Zealand match in 2012. It is a multilayer network as it contains edges of two different types: passes and tackles.

Once we have pass and disruption networks we can calculate a collection of network measures. I’ll give a brief idea here, but if you are looking for more formal definitions you’ll have to search for them in the paper:

  • Connectivity: how many pass connections you have to remove to isolate players;
  • Assortativity: the tendency of players to pass the ball to players with a similar number of connections — in high assortativity central players pass to other central players and marginal players to other marginal players;
  • Components: how many “sinks” there are, in that the ball never goes back to the bulk of the team when it is passed to a player in a sink;
  • Clustering: how many triangles there are, meaning that the team can be decomposed in many different smaller sub-teams of three players.

These are the features we calculated for the pass networks. The disruption case is slightly different. We calculated the same features for the team when removing the tackled player, weighted on the relative number of tackles. If 50% of the tackles hit player number 11, then 50% of the disrupted connectivity is the connectivity value of the pass network when removing player 11. The reason is that the tackled player is temporarily removed from the game, so we need to know how the team performs without him, weighted on the number of times this occurrence happens.

So, it is time to give some answers. Shall we?

1. Is there a relationship between the topology of the network of passes and the success of the team?

rugby2

Yes, there is. We calculate “success” as the number of meters gained, ball in hand, by the team. The objective of rugby is to cross the goal line carrying the ball, so meters made is a pretty good indicator. We control for two things. First, the total number of passes: it simply means the team was able to hold onto the ball longer, so it is trivially expected to result in more meters. Second, the home advantage, which is a huge factor in rugby: Italy won only 12 out of 85 matches in the European “Six Nations” tournament, and 11 of them were in Italy. After these controls, we find that two features have good correlations with meters made: connectivity and components. The more edges are needed to isolate a player, the more meters a team is expected to make (p < .01, R2 = 47%). More sinks in a team is associated with lower gains in meters.

2. Is there a relationship between disruptions made by tackles and territorial gains?

rugby3

Again: yes. In this case it seems that all calculated features matter to predict meters made. The strongest factor is again leftover connectivity. It means that if the connectivity of the pass network increases after the tackled player is removed from it then the team is able to advance more. Simplifying: if you are able to tackle only low connectivity players, then your opponent is able to gain more territory (p < .01, R2 = 48%).

3. Is it better to build networks of passes and disruptions for each action separately or for the entire match?

The answer to the previous two questions were made by calculating the features on the global match networks. The global network uses all the data from a match, exactly like the pass and disruption edges depicted in the above figure. In principle, one could calculate these features as the match unfolds: sequence by sequence. In fact, networks features at the action level work very well in soccer, as Luca and Paolo already proved. Does that work also in rugby?

Surprisingly, the answer is no. We recalculated the features for each passage of play. A passage of play is the part of a match from when a team gets into possession of the ball until it loses it, scores, or the game flow stops for an infraction. When we calculate features at this level, we find very weak correlations: almost nothing is significant and, when it is, the predictive power is very low. We think that this is because in rugby our definition of sequence is too strict. While soccer is a tactical game — where each sequence counts for itself — rugby is a grand strategy game: sequences build cumulative advantage which pays off after a series of them — or only in the match as a whole.

4. Can we use these relationships to “predict” the outcome of the match?

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This is the real queen question of the post, and we do not fully answer it, unfortunately. However, we have a very good reason to think that the answer could be positive. We created a predictor which trains on 17 matches and then, given the global multi-layer network, will pick the winner. You can see the problem of the approach here: we use the network of the match as it happened to “predict” the outcome. However, we did that only because we did not have enough matches for each individual team: we believe we can first predict how pass and disruption networks will shape in a new match using historic data and then use that to predict the outcome. That will be future works, maybe if some team is intrigued by networks and wants to contact us for a collaboration… (wink wink).

The reason I still like to report on our predictor is that it has a very promising property. Its accuracy was 83%. We compared with a prediction made with official rugby rankings, whose performance is worse: 76% accuracy. We also tested against bookmakers, who are better than us with their 86% accuracy. However, historic data on bets only cover more important matches — only 14 out of 18 — and matches between minor teams are usually less predictable. The fact that we are on par on a more difficult task is remarkable. More importantly, bookies tend to just “choose the best team”. For instance, they always predict a New Zealand win. The Haka, however, is not always enough and our networks caught that. New Zealand lost to England in a big upset on December 1st 2012. The bookmakers didn’t see that coming, but our network approach could have.

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17 March 2016 ~ 0 Comments

Networks of Networks @ NetSci 2016

EDIT: Deadlines & speakers updated. Submission deadline is on April 27th, notification on April 29th.

 

Dear readers of this blog — yes, both of you –: it’s that time of the year again. As tradition dictates, I’m organizing the Networks of Networks symposium, satellite event of the NetSci conference.

Networks of networks are structures in which the nodes may be connected through different relations. They can represent multifaceted social interaction, critical infrastructure and complex relational data structures. In the symposium, we are looking for a diversity of research contributions revolving around networks of networks of any kind: in social media, in infrastructure, in culture. The call for contributed talks is OPEN, and you can submit your abstract here: https://easychair.org/conferences/?conf=non2016

The deadline for submissions is April 15th, 2016 April 27th, 2016, just a month from now. We will notify acceptance by April 22nd, 2016 April 29th, 2016.

Here’s my handy guide to few of the many reasons to come:

  • Networks of networks are awesome, a hot topic in network science and a lot of super smart people work on them. You wouldn’t pass the opportunity to mingle with them, would you?
  • We have a lineup of outstanding confirmed keynotes this year — truth to be told, we have that every year:
  • This year NetSci will take place at the K-Hotel, Seoul, Korea (South, whew…). You really should not miss this occasion to visit such fascinating place.

The Networks of Networks symposium will be held on May 31st, 2016. The full conference, including all satellites, runs from May 30th to June 3rd. You can find all relevant information for the conference in the official NetSci website. Our symposium has a website too: check it out. In it, you will find also the fundamental information about all the people organizing this event with me: without them none of this would be possible. Here they are:

And also a list of other people, helping with their ideas, time and enthusiasm:

  • Matteo Magnani
  • Ian Dobson
  • Luca Rossi
  • Leonardo Duenas-Osorio
  • Dino Pedreschi
  • Guido Caldarelli
  • Vito Latora

Hope to see many of you in Korea!

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29 May 2015 ~ 0 Comments

Networks of Networks – NetSci 2015

The time has finally come! The NetSci conference—the place to be if you are interested in complex networks—is happening next week, from June 1st to June 5th. The venue is in Zaragoza, Spain. You can get all the information you need about the event from the official website. For the third year, I am co-organizing one of its satellite events: the Multiple Network Modeling, Analysis and Mining symposium, this year held jointly with Networks of Networks. The satellite will take place on June 2nd. As I previously said, unfortunately I am not going to be physically present in Span, and that makes me sad, because we have a phenomenal program this year.

We have four great invited speakers: Giovanni Sansavini, Rui Carvalho, Arunabha Sen and Katharina Zweig. It is a perfect mix between the infrastructure focus of the networks of networks crowd and the multidisciplinary approach of multiple networks. Sansavini works on reliability and risk engineering, while Carvalho focuses on characterizing and modeling networks in energy. Sen and Zweig provide their outstanding experience in the fields of computer networks and graph theory.

Among the contributed talks I am delighted to see that many interesting names from the network analysis crowd decided to send their work to be presented in our event. Among the highlights we have a contribution from the group of Mason Porter, who won last year’s Erdos Prize as one of the most outstanding young network scientists. I am also happy to see contributions from the group of Cellai and Gleeson, with whom I share not only an interest on multiplex networks, but also on internet memes. Contributions from groups lead by heavyweights like Schweitzer and Havlin are another sign of the attention that this event has captured.

I hope many of you will attend this seminar. You’ll be in good hands: Gregorio D’Agostino, Przemyslaw Kazienko and Antonio Scala will be much better hosts than I can ever be. I am copying here the full program of the event. Enjoy Spain!

NoN’15 Program

Session I

9.00 – 9.30 Speaker Set Up

9.30 – 9.45 Introduction: Welcome from the organizers, presentation of the program

9.45 – 10.15 Keynote I: Giovanni Sansavini. Systemic risk in critical infrastructures

10.15 – 10.35 Contributed I: Davide Cellai and Ginestra Bianconi. Multiplex networks with heterogeneous activities of the nodes

10.35 – 10.55 Contributed II: Mikko Kivela and Mason Porter. Isomorphisms in Multilayer Networks

10.55 – 11.30 Coffee Break

Session II

11.30 – 12.00 Keynote II: Rui Carvalho, Lubos Buzna, Richard Gibbens and Frank Kelly. Congestion control in charging of electric vehicles

12.00 – 12.20 Contributed III: Saray Shai, Dror Y. Kenett, Yoed N. Kenett, Miriam Faust, Simon Dobson and Shlomo Havlin. A critical tipping point in interconnected networks

12.20 – 12.40 Contributed IV: Adam Hackett, Davide Cellai, Sergio Gomez, Alex Arenas and James Gleeson. Bond percolation on multiplex networks

12.40 – 13.00 Contributed V: Marco Santarelli, Mario Beretta, Giorgio D’Urbano, Lorenzo Spina, Renato De Leone and Emilia Marchitto. Soccer and networks: changing the way of playing soccer through GPS, video analysis and social networks

13.00 – 14.30 Lunch

Session III

14.30 – 15.00 Keynote III: Arunabha Sen. Strategic Analysis and Design of Robust and Resilient Interdependent Power and Communication Networks with a New Model of Interdependency

15.00 – 15.20 Invited I: Alfonso Damiano,Univ. di Cagliari – Electric Market – Italy; Antonio Scala CNR-ICS, IMT, LIMS

15.20 – 15.40 Contributed VI: Rebekka Burkholz, Antonios Garas, Matt V Leduc, Ingo Scholtes and Frank Schweitzer. Cascades on Multiplexes with Threshold Feedback

15.40 – 16.00 Contributed VII: Soumajit Pramanik, Maximilien Danisch, Qinna Wang, Jean-Loup Guillaume and Bivas Mitra. Analyzing the Impact of Mentioning in Twitter

16.00 – 16.30 Coffee Break

Session IV

16.00 – 16.30 Keynote IV: Katharina Zweig. Science-theoretic musings on the analysis of networks (of networks)

16.30 – 16.50 Contributed VIII: Vinko Zladic, Sebastian Krause, Michael Danziger. Avoidable colors percolation

16.50 – 17.10 Contributed IX: Borut Sluban, Jasmina Smailovic, Igor Mozetic and Stefano Battiston. Sentiment Leaning of Influential Communities in Social Networks

17.10 – 17.30 Invited II: one speaker from the CI2C project (confirmed, yet to be defined)

17.30   Planning Netonets Future Activities

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02 April 2015 ~ 0 Comments

A Marriage of Networks

My personal quest as ambassador of multiple networks at NetSci (previous episodes here and here) is continuing also this year. And, as every year, there are new exciting things coming along. This year, the usual satellite I organize is marrying another satellite. We are in fact merging our multiple networks with the Networks of Networks crowd. Networks of Networks is a society holding its own satellite at NetSci since quite a while. They are also interested in networks with multiple node and edge types, with more attention to infrastructure-flavored networks: computer networks, power grids, water infrastructure and so on. We are very excited to see what the impact between multiple networks and networks of networks, directed in particular by Antonio Scala and Gregorio D’Agostino, will generate.

The marriage is a promising one because, when talking about multiple networks and infrastructure, technical knowledge is dispersed among experts of different sectors – system operators from different industries (electric, gas, telecommunication, food chain, water supply, etc) – while researchers from different fields developed a number of different strategies to deal with these complex objects – from computer science to physics, from economics to humanities. To be exposed to these approaches and to confront one’s understanding of the potentialities of the analysis of multiple interdependent networks is key for the development of a common language to integrate the knowledge from all sectors. Complex Networks can be a common language for the needed federated approaches at both microscopic and macroscopic level. This satellite is here exactly to foster the development of such common language.

The usual practical information you might find useful:

  • The satellite will take place on June 2nd, 2015. It will be held, as usual, jointly with the other NetSci satellites. The location will be Zaragoza, Spain. The information about how to get there is included in the NetSci website.
  • The official website of the satellite is hosted by the Net-o-Nets parent website. The official page is this one. Information about the satellite is pretty bare-bones at the moment, but we’ll flesh it out in the following weeks.
  • We are open to submissions! You can send in your abstract and we’ll consider you for a contributed talk. The submission system goes through EasyChair, and this is the official link. The deadline for submission is April 19th, 2015 and we will notify you on April 29th.

Sadly, I will not be present in person to the event due to conflicting schedules. So I will not be able to write the usual report. I’ll leave you in the best hands possible. Submit something, and stop by in Zaragoza: you’ll find an exciting and stimulating crowd!

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22 May 2014 ~ 0 Comments

The NetSci Multiple Networks Menu

Friends, scientists, network fanatics, lend me your eyes: I come to announce the program of the Multiple Network Modeling, Analysis and Mining symposium, introduced some months ago on these pages. To give you a quick recap: this is a satellite event which will happen at the 2014 edition of NetSci, a major network science event of the year. The symposium will take place on Monday June 2nd, while the conference itself will start on June 4th and it will last until the end of the week. Differently from last year, we now have space for contributed talks and I like the program we were able to set up. So, I’ll boast about it here.

You can find the overview of the entire event on the official website, but let me give you the highlights.

We have four invited speakers: Frank Schweitzer, Renaud LambiotteNitesh Chawla and Mason Porter. They come from different backgrounds (System Design, Mathematics and Computer Science) which is a great plus for the event. They are going to:

  • Tackle the mathematical foundations of multiple networks;
  • Describe models for multiple networks;
  • Analyse them, both in the flavour of bipartite temporal social networks and in the extension of the classic link prediction problem. Usually in link prediction we are interested in evaluating the likelihood of seeing “a” connection between two nodes. Since in multiple networks there are different types of connections, we are also interested in predicting “which” connection we will observe.

As for the contributed talks, we have a pretty good team, including (but not limited to) works signed by David Lazer from Northeastern University, Juyong Park from KAIST, Eugene Stanley from Boston University and many more. We had such a positive reaction to our call for papers, that we had to increase the slots for contributed talks from 5 to 7 and still reject presentations that we really wanted to see. Among my favourites works there are:

  • Multiple network applications to study the productivity of countries and predicting their growth;
  • The study of evolution of different relations among almost 2000 students from 14 US universities;
  • A network-based approach for ranking the performances of sport teams;
  • Novel way to classify nodes in complex networks where multiple different relations are present;
  • … and more!

For completeness, here’s the detailed schedule, I hope to see many of you there!

Session I

9.00 – 9.30 Registration / Set Up
9.30 – 9.50 Introduction: Welcome from the organizers, presentation of the program
9.50 – 10.30 Keynote I: Frank Schweitzer, Professor for Systems Design at ETH Zurich
Analysing temporal bipartite social networks
10.30- 11.00 Coffee Break

Session II

11.00 – 11.40 Keynote II: Renaud Lambiotte, Associate Professor, Department of Mathematics at University of Namur
Non-Markovian Models of Networked Systems
11.40 – 12.00 Daniel Romero, Nina Mishra and Panayiotis Tsaparas
Estimating the Relative Utility of Networks for Predicting User Activities
12.00- 13.30 Lunch

Session III

13.30 – 14.10 Keynote III: Nitesh Chawla, Associate Professor, Department of Computer Science & Engineering at the University of Notre Dame
Predicting links in heterogeneous social networks
14.10 – 14.30 Katherine Ognyanova, David Lazer, Michael Neblo, Brian Rubineau and William Minozzi
Ties that bind across contexts: personality and the evolution of multiplex networks
14.30 – 14.50 Neave O’Clery
A Multi-slice Approach to Understanding the Evolution of Industrial Complexity and Growth
14.50 – 15.30 Coffee Break

Session IV

15.30 – 16.10 Keynote IV: Mason Porter, Associate Professor at the Oxford Centre for Industrial and Applied Mathematics
Mathematical Formulation of Multilayer Networks
16.10 – 16.30 Seungkyu Shin, Sebastian Ahnert and Juyong Park
Degree-Neutralizing Weighted Random Walk Ranking in Competition Networks
16.30 – 16.50 Tomasz Kajdanowicz, Adrian Popiel, Marcin Kulisiewiecz, Przemysław Kazienko and Bolesław Szymański
Node classification in multiplex networks
16.50 – 17.10 Francesco Sorrentino
Stability of the synchronous solutions for networks with connections of different types
17.10 – 17.30 Andreas Joseph, Irena Vodenska, Eugene Stanley and Guangron Chen
MLR Fit-Networks: Global Balance of Payments

Conclusion and final announcements

17.30 – 18.00

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20 March 2014 ~ 0 Comments

When Dimensions Collide

The literature about community discovery, which deals with the problem of finding related groups of nodes in a network, is vast, interesting and full of potential practical applications. However, if I would have to give one critique of it, it would be about its self referential character. Most community discovery papers I read in computer science and physics journals are mainly about finding communities. Not much time is spent thinking about what to do with them, or what they mean. My first post in this blog was about a community discovery algorithm. Recently, an extended version of that paper has been accepted in a computer science journal. Since that first post, I (mainly) added some crucial modifications and features to the algorithm. I don’t want to talk about those here: they are boring. I also didn’t bring up this paper to boast about it. Okay, maybe a little. I did it because the paper touches upon the issue I am talking about here: it tries to do something with communities, it tries to explain something about them. Namely, it asks: why do communities overlap?

First of all: communities do overlap. When trying to detect them, many researchers realized that hard partitions, where each node can belong to one and only one community, are not always a good idea. Most of them found this a problem. Others were actually very happy: the problem gets harder! Nice! (Researchers are weird). Blinded by their enthusiasm, they started developing algorithms to deal with this overlap. Not many asked the question I am trying to answer here: why do communities overlap? As a result, some of these algorithms detect this overlap, but using approaches that do not really mean anything in real life, it’s just a mathematical trick. Others, instead, build the algorithm around a core hypothesis.

This hypothesis is nothing unheard of. Communities overlap because people have complex lives. Some of your college mates also attend your yoga class. And you know your significant other’s colleagues, which puts you in their community. All these communities have you as common member, and probably some more people too. The beauty of this is that it is not only intuitive: it works well in finding communities in real world social networks. So well that it is the assumption of my approach and of many others outstanding algorithms (this and this are the first two that pop into my mind, but there are probably many more). Another beautiful thing about it is that it is almost obvious, and so it is probably true. But here we hit a wall.

The fact that it is simple, reasonable and it works well in practice proves nothing about its property of being true. There are things that are not simple nor reasonable, but nevertheless true (hello quantum physics!). And there is practical knowledge that does not quite correspond to how things work (in my opinion, most computer science is a patch and nobody really knows why it works). Unless we test it, we cannot say that this nice practical principle actually corresponds to something happening in reality. So how do we go on and prove it? In the paper I proposed a first step.

This brings me back to another old love of mine. Multidimensional networks. They are networks in which we put multiple relations in a cage together in mating season and see what happens (research is fun). The idea behind the paper is that multidimensional networks give us the perfect tool to test the hypothesis. In monodimensional networks you have no clue why two people are connecting besides the obvious “they know each other”. In a multidimensional network, you know why they know each other, it’s information embedded in the type of the relation. So, the hypothesis is that different types of relations are the cause of the community overlap, and with multidimensional networks we can look at how communities distribute over relations. First, let us take a look at what two overlapping communities look like in a multidimensional network.

We collected a multidimensional social network putting together relationships between users in Facebook, Twitter and Foursquare. We then used DEMON to extract overlapping communities from each dimension. We then took two communities with extensive overlap in the Facebook dimension (picture below).

We then looked at the very same set of nodes, but now in the Foursquare network. In the picture below, we kept the edges, and the node positioning, of the Facebook network to make the comparison easier, but keep in mind that the edges in the Foursquare dimension are different, and they are the ones that decide to what community the nodes belong.

Very interesting. The communities look a lot alike, although the shared (and non shared) nodes are slightly different. Now node 7369 is shared (it wasn’t in Facebook) while node 8062 isn’t (whereas it was before). Let’s put another nail on the coffin and see the communities these nodes belong in Twitter (same disclaimer applies):

Surprise surprise, in Twitter there is actually only one community, which brings together the majority of the nodes of the two communities. So here’s where our overlap comes from: common affiliations in different dimensions! Now, I’m going to deal with that voice in your head that is screaming “Anecdotal! Anecdotal!”. (You don’t hear it? Did I already mention that researchers are weird? In any case “Anecdotal” refers to a type of evidence that bears no value in scientifically proving a point if not backed by more solid proofs). Put in a more general way: the more two communities overlap in some dimension, the more likely it is we can find a dimension in which these communities are actually a single community. This involves boring details you can find in the paper which ultimately generate this plot:

Does this plot prove our theory without leaving out any reasonable doubt? Maybe, or not really. There are still things to check. But science is made by tiny steps forward. And this is certainly one.

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25 February 2014 ~ 0 Comments

It’s happening! (Again)

You know that I am a guy deeply involved in multiple networks, networks containing different types of relations. You probably also remember that last year I organized, with Matteo Magnani, Luca Rossi, Dino Pedreschi, Guido Caldarelli and Przemyslaw Kazienko, a symposium on such a fascinating mathematical model and its applications (my report on it). Well: it is going to happen again at the 2014 edition of NetSci.

Some things changed since last year. Let me start from the most important. This year we are open to contributed talks! Last year the speakers where invitation-only: we were just starting and we wanted to understand what kind of crowd we had. The response was positive for the event and negative for the poor guy (me) who had to find extra chairs to accommodate the larger-than-expected bunch of people who showed up. So, choose your best work on multiple (multiplex, multidimensional, multilayer, multirelational… whatever!) networks and send a 300-word abstract and a figure here: https://www.easychair.org/conferences/?conf=mnam2014. You have time until April 14th, and you will receive notification of acceptance in two weeks, April 28th.

We are still going to have exciting keynotes, of course. So far, the first confirmed speaker is Renaud Lambiotte. If you read this blog, chances are that you are already familiar with the outstanding work he is doing in network science.

M74-729872

The date of the event didn’t change, it is still the first week of June (UPDATE: the exact day the satellite will take place is June 2nd, for the entire day). The location, of course, did: it is going to happen in Berkeley California, at the Claremont Hotel and the Clark Kerr Campus of the University of California. We go where NetSci goes, and with a reason: NetSci is the premier venue if you are interested in network science. And we want to play with the coolest kids, don’t we?

Two other things did not change and are worth remembering. First, participation is free of charge. We are interested in your brains, not your wallets. (UPDATE: Apparently, even if you are just attending this satellite, NetSci’s organizers require you to register anyway. Rates and information are here. Early registration is April 4th. Here, we are still interested just in your brains, though :) ). Second, we are still asking you to tell us if you are planning to come to the event. It is mostly a selfish maneuver: I want to limit my quests in finding extra chairs to the minimum. For this reason, we set up this handy and quick Google form for you.

So, don’t forget to send us an abstract. And see you in just a couple of months in Berkeley!

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