Slides for my keynote at Complex Networks 2019

LisbonTalkCover

I gave a keynote talk at the Complex Networks 2019 conference in Lisbon—here are the slides, if you are interested.

If you are interested in temporal networks in general, here are some pointers:

Postdoc Wanted — Network Science, Public Transport Networks, Cities, etc

HelsinkiPTN2

We are looking for a postdoc (2 years) to work on the intersection of complex systems/networks, transport engineering, human mobility, the science of cities, and data science.

This position is related to ongoing collaboration between my group and prof. Milos Mladenovic’s (Twitter: milosplanner) transport engineering group  (both at Aalto University, Helsinki area, Finland).

We want to bridge the gap between network science and transport engineering, including city planning and public transport network planning; for our earlier joint works, see, e.g.,

What we can offer:

  • Access to unique data: e.g., details of all trips from Kutsuplus, famous for being the world’s first on-demand public transport service; vehicle-level geocoordinate trajectories for public transport in the Helsinki region; aggregated mobile-phone flow data; and more coming in.
  • True multidisciplinarity with real-life application potential: in addition to the two teams from different domains (networks & transport), we interact with on-demand transport companies, the Helsinki Region Transport Authority, etc.
  • Access to heavy-duty computational resources (our Triton cluster, etc)
  • Access to lots of in-house expertise on networks, data science, and transport studies
  • Lively environment: Aalto University with a campus ~10 km from the centre of Helsinki with its own subway station (great public transport connectivity!)
  • Decent salary: >3keur/month, which is really quite OK in the Helsinki area (despite taxes + costs of living being a bit higher than in most countries)
  • Darkness of winter that is compensated by almost around-the-clock sunlight in the summer!

What we expect:

  • Expertise in network science/complex systems/data science
  • Some level of expertise in cities, transport, spatial networks, geodata, etc
  • PhD in a field relevant to the above
  • Skills in Python or willingness to learn them fairly quickly (packages such as gtfspy will help you get started)
  • Interest in the topic!

The call is open until 20th of December; the applications will be processed and (Skype) interviews with shortlisted candidates will be conducted in January 2020.

Please email a single combined PDF document containing 1) a cover letter, 2) your CV and publication list, 3) contact details for two references, to jari.saramaki@aalto.fi, with “Mobility postdoc” in the topic.

What is scientific creativity—and how do you feed it? (Part I)

acid-citric-citrus-997725

Last winter, on a speaking trip to Norrköping, someone asked me to write about skills (and meta-skills) that scientists and PhD students need, beyond writing papers. Turns out that this is a lot more difficult than writing about writing, where the end product—a scientific paper—is something tangible and amenable to analysis: how do great introductions look like? How do the greatest writers finish their papers? It is much more difficult to write, say, about learning to be creative, which is what I shall try to do here. But what would be more important for aspiring scientists than creativity?

Science is all about creativity: coming up with the right questions, developing clever methods to answer those questions, and connecting the answers in imaginative ways to learn something greater. But we rarely talk about creativity as a skill—often, people view it as something that you either have or don’t have, just like an ear for music or an eye for design. And just like with music and design, this view is wrong: everything can be learned. So how do you learn to be creative?

Before attempting to answer this question, let’s take the bull by the horns and ask what creativity is. If by creativity we mean the ability to bring forth ideas that are entirely new, we are immediately hit by a very difficult, philosophical question: where do new ideas come from? At least to us (recovering ex-) physicists, the emergence of something that wasn’t there before is kind of strange: aren’t there conservation laws that forbid this kind of travesty from happening? What is it that gives birth to new information (because that is what happens when a new idea emerges, whether it is a question or an answer)?

If physics doesn’t provide us with answers, let’s drop it for a while and put on the hat of a biologist: in the realm of living things, don’t new things gradually emerge, driven by the slow Darwinian evolution? Notice the word “gradually”—biological evolution is slow tinkering, a process where existing forms and shapes and organs are gradually transformed into something new, of dinosaurs developing feathers that eventually help some of them to learn to fly, of finches’ beak shapes adapting to their habitats. So in biological evolution, everything that is “new” is built on top of a lot of something old, and this happens slowly: a slow expansion into the adjacent possible, if you’ve read your Kauffman.

Are there some other natural processes where new forms emerge more rapidly? The human immune system provides a great example. Somewhat surprisingly, not all our cells carry the same sets of genes: the T and B cells of our immune system, our ultimate smart weapons against viruses and other invaders, display an enormous diversity of different receptors that recognise those invaders. This diversity results from those cells carrying some randomised (but not too randomised) parts of our genome. The precursor cells that eventually become T and B cells have strings of different modules in their genetic code, and in the process of randomisation, some of those modules are randomly picked and joined together (the rest are discarded). Then, a bit of extra randomness (extra letters, deleted letters, and so on) is added to their junction. So to arrive at new kinds of receptors, our bodies randomly merge things that are known to work (those receptor modules) and then add some noise on top. Again, “new” equals “old, but with added something.”

Let’s now return back to creativity, in the context of science or otherwise. The above examples point out that the old rhyme—“something old, something new, something borrowed, something blue”—is scientifically highly accurate, except for the blue bit perhaps. In other words, the things that we think are new are in fact modifications and clever combinations of old things, with perhaps some small amount of additional randomness. Ideas do not live in a vacuum, they emerge because of other ideas.

Therefore, creativity is the ability to merge existing ideas in new ways (while possibly adding a magic ingredient on top).

This brings us to a fairly simple recipe for feeding one’s creativity: collect lots of things that can be combined/transmogrified into something new, and then just combine them! In other words, first, feed your head with lots of information—and not just any information, but preferably pieces of information that haven’t yet been combined.

To maximise the chance of something entirely new emerging out of this process, your input information—the stuff that you feed your head with—should be diverse enough. There are, however, different possibilities: on the one hand, if you know everything that there is to know about your field, you can probably see where the holes are and combine bits of your knowledge in order to fill them. On the other hand, if you know enough about a lot of fields, you might be able to spot connections between them (think of, say, network neuroscience, applying network theory to problems of neuroscience). There are different styles here, but even if you choose to go deep instead of wide, do keep the diversity of input information in mind: just for fun, learn some mathematical techniques that people do not (yet) use in your field! You never know, those might turn out to be useful later.

To be continued…

Functional brain networks: the problem of node definition

Summary: Nodes in brain networks from fMRI are usually defined using ROI’s (Regions of Interest) so that each ROI node has a time series that is the average of the BOLD time series of the ROI’s voxels and links represent correlations between nodes. Here, we show that this averaging of voxel time series is problematic.

The human brain is a complex network of neurons. The problem is that there are about 10^12 of them with ~10^5 outgoing connections each; mapping out a network of this scale is not possible. Therefore, one needs to zoom out and look at the coarse-grained picture. This coarse-grained picture can be anatomical – a map of the large-scale wiring diagram between parts of the brain – or functional, indicating which parts of the brain tend to become active together under a given task.

But how should this coarse-graining be done in practice? How to define the nodes of a brain network –– what should brain nodes represent? In functional magnetic resonance imaging (fMRI), the highest level of detail is determined by the imaging technology. In a fMRI experiment, subjects are put inside a scanner that measures the dynamics of blood oxygenation in a 3D representation of the brain, divided into around 10,000 volume elements (voxels). Blood oxygenation is thought to correlate with the level of neural activity in the area. As each voxel contains about 5.5 million neurons, the network of voxels is significantly smaller than the network of neurons. However, it is still too large for many analysis tasks, and further coarse-graining is needed.

A typical way in the fMRI community is to group voxels into larger brain regions that are for historical reasons known as Regions of Interest (ROIs). This can be done in many ways, and there are many pre-defined maps (“brain atlases”) that define ROIs; these maps are based on anatomy, histology, or data-driven methods. It is common to use ROIs as the nodes of a brain functional network. The first step in constructing the brain network is to assign to each ROI a time series that is the average of the time series of its voxels measured in the imaging experiment. Then, to get the links, similarities between the ROI time series are calculated, usually with the Pearson correlation coefficient. The correlation between the two ROIs becomes their link weight. Often, only the strongest correlations are retained, and weak links are pruned from the network.

If the ROI approach is to work, the ROIs should be functionally homogeneous: their underlying voxels should behave approximately similarly. Otherwise, it is not clear what the brain network represents. Because this assumption hasn’t really been tested properly and because it is fundamentally important, we recently set out to explore whether it really holds.

We used resting-state data – data recorded with subjects who are just resting in the scanner, instructed to do nothing – to construct functional ROI-level networks based on some available atlases. We defined a measure of ROI consistency that has a value of one if all the voxels that make up the ROI have identical time series (making the ROI functionally homogeneous, which is good), and a value of zero if the voxels do not correlate at all (making that ROI a bad idea, in general).

Distribution of consistency for ROIs as brain network nodes
[Figure from our paper in Network Neuroscience]

We found that consistency varied broadly between ROIs. While a few ROIs were quite consistent (values around 0.6), many were not (values around 0.2).  There were many low-consistency ROIs in three commonly used brain atlases.

From the viewpoint of network analysis, the existence of many low-consistency ROIs is a bit alarming.  We also observed strong links between low-consistency ROIs – how should this be interpreted? These links may be an artefact, as they disappear if we look at the voxel-level signals. This means that the source of the problem is probably the averaging of voxel signals into ROI time series. While this averaging can reduce noise, it can also remove the signal: at one extreme, if one subpopulation of voxels goes up while another goes down, the average signal is flat. More generally, if a ROI consists of many functionally different subareas, their average signal is not necessarily representative of anything.

In conclusion, we would recommend being careful with functional brain networks constructed using ROIs; at least, it would be good to go back to the voxel-level data to verify that the obtained results are indeed meaningful.

For details, see our recent paper in Network Neuroscience.

This post was co-written by Onerva Korhonen, Enrico Glerean & Jari Saramäki.

[PS: The definition of brain network nodes is not the only complicated issue in the study of functional brain networks. Even before one has to worry about node selection, a possible distortion has already taken place: preprocessing of the measurement data. We’ll continue this story soon.]

Why can writing a paper be such a pain?

This is the first in a series of “self-help” posts for PhD students on how to write a scientific paper.

Writing a scientific paper

Show me a researcher who has never struggled with writing, and I’ll show you someone who hasn’t written anything, or who doesn’t care about the quality of the output. Science is hard, and so is writing. Together they are harder. Now add in lack of experience as a researcher and as a writer, together with the usual time pressure, and it’s no wonder that the blank document in front of you looks like the north face of Mount Everest. We’ve all been there, staring at that wall.

While no mountaineer would risk climbing Everest without a route plan, an inexperienced writer tends to neglect the importance of planning. Having no plan, she tries to do everything at once. She opens the blank document in her editor, stares at it, tries to decide what to make of her results, what the first sentence of the first paragraph should be, what the point of the first paragraph should be, and what the point of the whole paper should be.

It’s no wonder that this feels impossible. No-one can solve that many problems in parallel. Problems are best solved one at a time.

Writing becomes easier if one separates the process of thinking from the process of writing. To write clearly is to think clearly, and thinking precedes writing. Writing becomes a lot less of a struggle when you think through the right things in the right order, before putting down a single word.  A successful software project begins with the big picture: what functions and classes are needed, and for what purpose. It doesn’t begin with developing code for the internal bits of these functions and classes. A writing project should also begin with addressing the overall point and structure of the paper, before moving to details such as words or sentences.

Another way of looking at the problem is linearity versus modularity. The fear of the blank page arises out of linearity: the feeling that the only way to fill the page is to start with the first word and proceed towards the last, word by word. This is not so. Whereas reading is usually linear, writing doesn’t have to be. The process of writing should be modular – first, sculpt your raw materials into rough blocks that form your text, and then start working on the blocks, filling in more and more details, so that entire sentences only begin appearing towards the end of this process.

The approach I try to teach my students is splitting the writing process into a series of hierarchical tasks. This way, getting from a pile of results to a polished research paper is a bit less painful.

This approach begins by identifying the key point of the paper and then moving on to structuring the material that supports this point into a storyline. This storyline is then condensed into the abstract of the paper. My advice is to always write the abstract first, not last! This serves as an acid test: if you cannot do it, you haven’t developed your storyline enough.

After that, there are many steps to be taken before writing any more complete sentences: planning the order of presentation, including figures, and for each section of the paper, mapping the arc of the storyline into paragraphs so that the point addressed by each of the paragraphs is decided in advance. Then, the paragraph contents are expanded into rough sketches, and these sketches are finally transformed into whole sentences. At this point, there is no fear of the blank page, because there are no blank pages: for each section, for each paragraph, there is a map, a route plan, and the only decision that is needed is how to best transform that plan into series of words. Often, this feels almost effortless.

[Next in the series on how to write a scientific paper: how to write a great abstract]

There is now an ebook based on this series, available from a number of stores (Kindle Store, Apple Books, Kobo, Tolino, etc!)