Category Archives: Net. Dynamics

Winter School of Network Theory and Applications

At the very beginning of this year, from 5th to 8th January, I had attended the Winter School of Network Theory and Applications at the University of Warwick, UK. It was organized by complexity research centres from universities of Oxford and Warwick and in contrast to other workshops on networks, this school was less focused on social networks in particular, and was more concerned with networks in general, their statistics, modelling and dynamics.

The majority of the programme consisted of blocks of typically two 1.5h lectures on broader topics like ‘network statistics’ or ‘dynamics of neural networks’. Besides these major blocks, there were couple of 45min short talks on concrete topics like community detection or biologically inspired network dynamics. There were also three tutorial sessions, were attenders worked in small teams on some elementary network analysis tasks in Matlab, like generation of Erdos-Renyi graphs, investigation of its criticality, generation of small-world network and inspection of its diameter, etc.

As network science is not completely new for me, certain lectures were rather repetition. However, it was indeed useful to have all this previous knowledge to be framed in the unified perspective, which can be offered only by experts in the field. As I understood, the school should be organized again next year, so I would recommend it to anybody interested in incorporation of network science/analysis into his/her own research toolkit — especially at the beginning of the research.


WebScience: The Next Big Thing or a Buzzword?

Let me start this post with a question each graduate in computer or information science is supposed to be able to answer: What is Web? Besides a completely right but in our context useless Salomonian answer that it’s a spider’s tool to catch insect, we really need to answer this question if we want to study Web. Let’s accept just for the purpose of this post that web is a global communication space using Internet as a medium. Note that this does not directly exclude non-HTTP communication. I will get back to this at the end of the post. Well, we have it defined and now we may study it! Why? Because it has penetrated our lives to an extent where it is advisable to know more precisely:

  • What new types of interactions and behaviours of people it brought?
  • What is the relation between large-scale communication structures like free software movement, social networks, Wikipedia, etc. and individual motivations and actions from which these structures emerge?
  • What is an economic impact of Web? Does it affect the ways we perceive/create wealth? Has it brought some new types of utility that has never occurred before?
  • Are there any differences between social norms and stereotypes between off-line and on-line worlds? Are there two different notions of privacy, friendship, … between those two worlds?
  • What are the proper scientific methods for studying the Web?

Connecting now 28.7% of the Earth’s population and still rapidly growing, the Web is becoming a ubiquitous part of our culture. Therefore, the study of similar questions is inevitable if we want to catch up in understanding it. The Science of the Web, or WebScience, has thus been pushed forward as an independent research stream by figures like Tim Berners-Lee, Nigel Shadbolt, or Dame Wendy Hall. At the end of September, two public events related to WebScience were organized by the Royal Society.

The first one took place at Royal Society centre in London for two days and about two hundreds of participants could be there I guess.  The speakers were mostly the core figures of their respective disciplines which are understood as influential, inspiring, and fundamental elements of what WebScience is supposed to be. Namely, network science (Albert László-Barabási, Jon Kleinberg), mathematics (Jennifer Chayes, Robert May), sociology (Manuel Castells), computer science and engineering (Tim Berners-Lee, Luis von Ahn), communication science (Noshir Contractor), and law (Jonathan Zittrain). There were more speakers, as you can see in the programme, but those listed here particularly arrested my attention and somehow remained in my mind. Being a computer scientist working on graph mining techniques, I was particularly amazed by Jon Kleinberg’s presentation on the state of the art in link prediction, structural balance in graphs, and other things which I surely do not remember completely, so I am looking forward to the video streams recorded on site. Another great talk was given by Luis von Ahn. An excellent presenter with smart ideas that help world to digitize books or translate Wikipedia by employing millions of people (very likely even you!) without them being necessarily aware of it! Jennifer Chayes presented some advancements in mathematical apparatus for handling of network data – in particular a proper sampling of networks and game theoretic approaches for modelling of dynamics on social networks. Having some elementary background and being interested in political economics, I particularly enjoyed Bob May’s talk on how model’s of spread of diseases are similar to models of financial crisis. I also liked his side-comments on current political neo-libertanian doctrine and its influence on the current mess, which were only seemingly of marginal importance – in fact, they were very essential for the whole talk, I would say. I was waiting the whole two days for some presentation about the semantic web – and finally with the presentation of Tim Berners-Lee, I had lived to see it. He mainly told about the current Linked Data project and what are the bottlenecks of the present semantic web – namely it was the lack of higher level libraries/components for work with RDF data. It was nice to hear that because it means that there are RDF data out there already and now it’s time to consume them! In fact, Tim’s talk was not the first one about the semantic web – David Karger showed us an interesting way how to produce and visualize RDF data in browser using Exhibit. I really loved that talk, because it was a nice introduction into rich possibilities structured on-line data give us but without mentioning words like triple, logic, ontology, RDF, etc. And the whole platform seems to be really useful for creation of rich on-line presentation of mid-size data sets. All aforementioned speakers were presenting personally – except Jonathan Zittrain, whose speech was transmitted on-line. His presentation had a provocative title: Will the web break? He spoke about different legislative problems related to services like web archive, which operate on the edge of the law (or even illegally), because of obsolete copyright law. Quite interesting remark was also about URL shorteners like, that simply can cause parts of the web to break, as if they stop to operate, part of the hyperlink structure will become dead. Regarding .ly domain, Tim Berners-Lee recently twitted about the potential infringements of the online human rights by the Lybian government, under which jurisdiction this domain belong, so it is really worth to think about which one to use.

The second satellite meeting was in a certain sense a continuation of the big discussion event in London. It was held in a lovely countryside house near Milton Keynes and was organized as a series of short presentations and follow-up workshops focused on several defining aspects of WebScience like network science, linked (government) data, crowdsourcing, etc. There was much more space to discuss things and people made use of it. On Wednesday evening, there was also a poster session, where I presented a one about our work on cross-community analysis. As there were only 9 posters altogether, it was a great opportunity to get a feedback. I think I may say that our work was quite well accepted there:-). All posters are accessible either as a list, or as a map. What I was missing there was a dedicated block on methodology of Web Science. At the end of this two-day event, there was a short workshop in which one group was working on methodology-related topics, but this was IMO insufficient. I think if Web Science is supposed to be a real scientific discipline and not just a label for a bunch of loosely related topics of different disciplines with a Web as a common denominator, we really do need a common language, methodology toolkit – a common paradigm. I am aware of that the whole discipline is just at its infancy and that this may be overcame in the future, but I think it is important to keep this in mind as a number one priority, because otherwise the Web Science itself may become just a buzzword and a missed opportunity.

Now I am getting back to the beginning of this post, where I postponed the question which Internet services we may consider as the Web and which ones we may not? I think it is quite unfortunate to call this endeavour Web Science without properly making a distinction between World Wide Web as a service relying on HTTP and the global communication space in a more general sense. If we constrain the WebScience just only on communication realized via HTTP, we are shooting ourselves into our own feet, because we are putting aside many other interesting parts of cyberspace: IRC, World of Warcraft, Usenet, e-mail, FTP, BitTorrent, … Without any doubts, the World Wide Web is the most important service of the Internet if it comes to communication, but it is not the only one. Things become even more complicated with some people pushing forward a term Internet Science. What are the relation of these two: Web and Internet Sciences? I have always assumed that Internet is a set of low-level protocols, wires, routers and other hardware, whose only purpose is to transmit packets from point A to point B. So in that interpretation there is no space to investigate the actual communication process between humans and an actual impact of these processes on the behaviour of people. And that is what I find the most interesting on Web Science.

Applications of Social Network Analysis: ASNA 2010

By some strange coincidence, I managed to visit Switzerland again this year – even two times in the same month!;-) I visited Zurich between 15.-17. September, where ASNA 2010 conference took place. This year’s topic was dynamics of social networks, which pretty much resonates with our current work on mutual effects of bibliographic communities, so I presented a full paper about it there (see below the slides).

The majority of talks was given by social scientists – namely sociologists and political scientists. There were couple of computer scientists as well. Particularly Tanya Berger-Wolf‘s talk arrested my attention, as she presented their work on social network analysis of zebras. One particular feature of zebra communities is that individual members visit for some period of time another communities. Therefore, they developed a community detection algorithm to detect communities stable in time, which also allows to treat an individual to be a part of its “base” community while it may be occasionally visiting another community. They introduced economically motivated notion of community affiliation, which seemed to me very interesting, as it brings to community detection methodology well argued notion of what does it mean to be a part of the community and what community itself is.

Another interesting talk I enjoyed was by Thomas Valente. He has done a lot of work on social network intervention programs, e.g. who to influence and how in order to prevent drug abuse among adolescents. I was quite surprised, that methods he presented were quite simple. It’s not a rocket science! For example, one may specify, that the desired goal is to rise cohesiveness of the network, so then s/he may try to add various links between nodes and identify the marginal growth of objectivity function, e.g. cohesiveness. I was immediately thinking of that in our work on co-citation networks, we may look at the similar process. That is to say, to inspect which scientists should be connected together in order to maximize their impact on the network, growth of their community, etc. I assume that a necessary step allowing this would be to calibrate a multi-agent model describing the behaviour of the scientific communities we have analyzed. Probably a catch is how to construct such a model: what should be the parameters? How should new link be added among the scientists? And should the existing links decay? What influence the formation of a citation link between scientists? Certainly it is a topical similarity. But what else? Spatial similarity? Position in the existing network? Anything else? I think the recent work of Leskovec and Myunghwan may be a good starting point for such a model.

One of the take away messages I brought from ASNA is that social and computer scientists have very different notion of scale. When they talk about “large-scale”, they usually mean hundred and more. When we talk about big networks, we usually mean tens of thousands or even millions. One of the reason I guess is the methodology of obtaining the studied networks. Whereas we usually scrape, mine, and integrate data in order to obtain those networks, they interview people, which is of course much more time-consuming.

The conference itself was organized by the University of Zurich and was held at their campus, which is nearby the city centre. The whole city is beautiful, clean, well-organized and pleasant to stay. The architecture and overall look is quite different to Geneva or Lausanne, though. In general, those French-speaking parts differs to German-speaking ones. This is quite surprising, I would say, as Switzerland is really a small country. As the conference ended on Friday evening, I booked my flight on Saturday afternoon. Since I had to check out at my hotel in the morning, I had some time to look around and to do some quick sightseeing. But being astonished by the beauty of the city’s architecture, I suddenly realized that I really do not have time left and that I have to run to the railway station. Without all this famous Swiss punctuality, I would have been pretty doomed, because I caught the last train to the airport and checked-in at the time, when the desk was about to close. Good luck:-).

Knitting the Dublin Clique

Last two weeks of August I spent in Dublin visiting the UCD Clique group. Even though modern technologies and Internet in particular allow us to communicate freely and efficiently, the face-to-face encountering is still irreplaceable, what I realized right after the first brainstorm session on what should I work on during my two-week visit. It turned out, that there is a significant intersection between what I would like to work on in the close future and what the guys in Dublin plan to research.

My work on cross-community dynamics relies on arbitrary community detection algorithm. However, I have not used any overlapping communities detection method. Dublin group works on two of those methods (GCE and MOSES), so I applied these and assessed preliminary the quality of the result communities. It turned out, that these communities are probably topically less cohesive, than communities mined with Infomap or Louvain methods, which, however, does not necessarily imply worse performance of Dublin methods. I would rather say, that the structure of overlapping communities is simply more “open”. I’m looking forward to  see, what cross-community effects we will be able to identify among these overlapping communities. I’m thinking of at least one special case not possible using non-overlapping communities: transdisciplinary, or “intermediary” communities. Those communities, which are formed by parts of other, more sharply defined communities (in terms of their topic), should themselves be identified by overlapping communities detection algorithm. Therefore, it should be easy to just look at communities, whose majority consists of other communities.

Daniel Archambauld together with Derek Greene developed very useful tool for analysis of dynamic communities: TextLUAS. It’s an application, that visualizes the dynamic life-cycle of the communities, but not only that. It also visualizes tags associated to each community, or in general, associated to sub-part of a community life-cycle. One can then very easily inspect, how topics of one community disseminate to other communities, which the community interacted with. Together with Daniel, I worked on tweaking this software for our purposes of cross-community analysis. As a result, we are now able to use it with arbitrary community detection algorithm. In future, we plan to develop a life-cycles clustering support, so that one will be able to inspect only certain type of life-cycle, e.g. “communities, which emerged from two other communities and then grew”. This will be particularly useful in case of analysis of many dynamic communities, as then the complete visualization of their life-cycles starts to be really unclear and messy.