Disrupting Mexican cartel recruitment holds the key, a new study finds.

Not through courts and not through prisons. The only way to reduce violence in Mexico is to cut off Mexican cartels’ recruitment. Increasing incapacitation instead leads to both more homicides and cartel members, researcher Rafael Prieto-Curiel from the Complexity Science Hub and colleagues show in a study in Science.

In 2021, approximately 34,000 people died from intentional homicides in Mexico – the equivalent of nearly 27 victims per 100,000 population. This ranks Mexico among the least peaceful countries worldwide.

Demonstration against Javier Valdez murdering by ProtoplasmaKid is licensed under CC BY-SA 4.0
Demonstration against Javier Valdez murdering (source: ProtoplasmaKid; license CC BY-SA 4.0)


In order to be able to address this violence in the most targeted way possible, researchers now studied the cartels’ evolution by using data on murders, missing persons, and incarcerations in Mexico between 2012 and 2022. Therefore, they combined data from the National Institute of Geography and Statistics in Mexico (INEGI) on homicides, the national registry of missing persons (RNPDNO), and data from the Mexican prison census.


Their results show that Mexican cartels currently have between 160,000 and 185,000 members. This makes them the fifth largest employer in the country – with far-reaching effects on the population. And despite the state’s efforts to limit the cartels’ power by, for example, imprisoning nearly 6,000 cartel members annually, the cartels have increased their membership by 60,000 since 2012.

Size of Mexican cartels
A | Between 2012 and 2022, an estimated 285,000 people were cartel members, but only 60% remained active by 2022. Cartel careers are short and perilous, with approximately 17% deceased and 20% incapacitated.B | Number of employees from the top 10 companies in Mexico and the combined size of cartels. The total cartel membership ranged between 160,000 and 185,000.C | Among the 175,000 active cartel members, approximately 17.9% belong to CJNG, 8.9% to Cartel de Sinaloa, and 6.2% to Nueva Familia Michoacana, the three largest cartels by size.


At the same time, they themselves lose many members to killings. “The career path of cartels is very short and violent. In 10 years, 17% of the people recruited by cartels will be dead, and 20% will be incapacitated in some prison,” explains Rafael Prieto-Curiel from the Complexity Science Hub. At least 350 people need to be recruited per week to avoid collapse due to total losses, the researcher says.


Still, violence has not decreased. On the contrary, between 2012 and 2021, cartel-related deaths increased by 77%. “If Mexico continues this path, it will experience 40% more deaths by 2027 than it does today, and the cartels will have 26% more members,” Prieto-Curiel says. Even if it were possible to prosecute twice as many cartel members and have twice as many people in prison, there would still be 8% more deaths in 2027.


In comparison, halving the cartel’s ability to recruit would reduce weekly casualties by 25% and the cartel’s size by 11% until 2027. “Mathematically, therefore, a preventive strategy is significantly more successful than a traditional reactive strategy,” Prieto-Curiel emphasizes. However, the cartels are so large that even if recruitment were to drop to zero, it would take three years to return to the already high levels of violence seen in 2012. That’s why swift and comprehensive action is needed.

New CSH study: Weekly cartel-related deaths and cartel size and scenarios of how both might evolve.
Projected weekly cartel-related deaths (top) and cartel size (bottom) under different scenarios: continued trends, doubling incapacitation, halving recruitment, and reducing recruitment to zero.


Despite the Mexican cartels’ economic, social, and political importance, essential information about their size and the impact of various policies designed to limit their power has been lacking. “To the best of our knowledge, this work provides the first scientific attempt to mathematically quantify the size of cartels in Mexico and compare policy strategies to reduce violence in the country. We, therefore, hope to make an important contribution to a more peaceful future of Mexico,” Prieto-Curiel says.

The study “Reducing cartel recruitment is the only way to lower violence in Mexico” by Rafael Prieto-Curiel, Gian Maria Campedelli and Alejandro Hope was published in Science (doi: 10.1126/science.adh2888).

Diversity fuels prosperity in cities, but where do people from diverse backgrounds meet?


A study from the Complexity Science Hub now indicates that locations offering a range of rare shops and services may hold the key.

Extensive research consistently underscores a common factor in successful cities: diversity. Encouraging interactions between individuals of different backgrounds fosters the exchange of ideas, leading to innovation and economic success. “However, segregation persists in urban areas, not solely based on residence but also on the places people frequent,” CSH researcher Sándor Juhász explains.

“We must comprehend the characteristics of urban locations that attract people from diverse strata”


Given this premise, it would be advantageous for cities to proactively create urban diversity and establish spaces where individuals from varying socio-economic backgrounds can come together. Juhász emphasizes, “To achieve this, we must comprehend the characteristics of urban locations that attract people from diverse strata and understand why these locations possess such an appeal.”


This new study contributes a piece here. Working alongside colleagues from ANET Lab Budapest, Juhász demonstrates that locations in Budapest offering diverse but not widely available amenities effectively attract people with different socio-economic backgrounds.

Budapest © Unsplash

These “complex” locations provide a range of shops and services, such as cinemas, zoos, and coffee shops, which are not universally accessible, like a zoo, for instance. Juhász explains, “We draw inspiration from the economic complexity framework, which posits that economies with a diversified product portfolio, featuring numerous non-ubiquitous outputs, tend to thrive.”


Following this approach, the researchers developed indices reflecting the complexity of neighborhoods in Budapest based on the distribution of different Point-of-Interest (POI) categories on Google Maps.

Map of Budapest colored by (A) the amenity diversity (B) the average amenity ubiquity (C) the amenity complexity of neighborhoods.
Map of Budapest colored by (A) the amenity diversity (B) the average amenity ubiquity (C) the amenity complexity of neighborhoods.

They also assessed the complexity of amenity types by considering their ubiquity and the number of other POI types available nearby. Just like neighborhoods, less common amenities, such as zoos, surrounded by various POIs attract a more diverse audience.

Both are strongly tied to how centrally located the respective neighborhood or amenity is. While the mixing of different people naturally depends heavily on centrality, understanding the complexity of neighborhoods and amenities provides an even more precise insight.


To uncover visitation patterns inside Budapest, the researchers employed GPS data from mobile phones. Juhász clarifies, “When you use smartphone apps, you may be asked to permit the collection of your location data. If you consent, the app developers can collect information on your mobility, including time and precise location, but without personal data. This anonymous data can be used by researchers like us to find out how cities can become a better place for everyone.”

Location data © Unsplash

The team then tracked all the places where people stopped for a short stay over months in Budapest, considering at what period of the day. During the night, the stop is most likely at people’s homes, and from 9 am to 5 pm, it’s most likely their workplace. “So, we focused on so-called ‘third places,’ such as coffee shops or cinemas, as potential locations for interaction,” Juhász states.


They also used real estate prices in the individuals’ residential areas as an indicator of their wealth.


By combining this information, the researchers could assess a person’s economic situation and the types of places they frequented. In Budapest, Városliget, the city’s largest and oldest public park, stood out as the most complex neighborhood, boasting a museum, spa, zoo, and other unique facilities not widely accessible. In terms of amenity categories, Zoo turned out to be the most complex type.

“People with various aims and objectives are drawn to locations like these, offering a diverse portfolio that includes super-rare amenities. This is why we believe that socio-economically diverse individuals will adhere to this rationale and seek out complex places,” Juhász notes. While interaction in these places isn’t guaranteed, the likelihood is significantly higher than in other, less complex neighborhoods or amenities.


“Consequently, for policymakers, this knowledge is essential as it enables them to identify potential areas of segregation in their city using the economic complexity framework and to implement measures to foster urban diversity,” Juhász emphasizes.


The study “Amenity complexity and urban locations of socio-economic mixing” by Sándor Juhász, Gergő Pintér, Ádám J Kovács, Endre Borza, Gergely Mónus, László Lőrincz and Balázs Lengyel was published in EPJ Data Science (doi: 10.1098/rstb.2022.0402).

It’s no longer just about stopping, but how we can live with climate change. 

To figure this out, we must delve into our cultures, as highlighted in a special issue of The Royal Society. A study by the Complexity Science Hub points out how our history could help guide the way.

Special Issues


in scientific journals are a collection of articles that focus on a specific topic, promote emerging trends, foster interdisciplinary collaboration, and provide a platform for experts to share their research.

Currently, we are grappling with a global crisis convergence. Various types of threats intersect, intertwine, and test our collective resilience, from climate change and economic inequality to political polarization. Although the scale and global reach of these challenges present new hurdles, these threats have been faced and, sometimes, overcome in the past. Societies today barely have time to recover from one crisis to the next, but we possess a significant advantage: knowledge. The knowledge we can obtain from our history through new methods.


CSH researchers Peter Turchin and Daniel Hoyer have pioneered fresh approaches to drawing lessons from history. Together with colleagues from different fields, they have compiled the Crisis Database (CrisisDB) as part of the Global History Databank Seshat, containing over 150 past crises spanning different time periods and regions.

When we experience environmental shocks – when earthquakes shook the earth, droughts parched the land, or floods ravaged regions, some societies succumbed to social unrest, civil violence, or total collapse, while others exhibited resilience, maintaining essential social functions or even achieving improvement through systemic reforms that promoted well-being and increased democratic participation.


Daniel Hoyer remarks, “What we observe is that not every ecological shock or climatic anomaly leads to collapse or even a severe crisis, and not every crisis involves a major environmental stressor.” But what makes the difference? What drives collapse versus positive change?


To illustrate the divergent dynamics experienced by past societies, and to highlight the comprehensiveness of their data, the researchers provide three examples.


The Zapotec hilltop settlement of Monte Albán in southern Mexico emerged as the most significant settlement in the region. Extreme, persistent drought hit the region in the 9th century, and the once-great site of Monte Albán was entirely abandoned along with many other cities in Mesoamerica. However, recent research presented here shows that this was hardly a case of ‘societal collapse’, as many former residents of Monte Albán resettled in smaller communities nearby, likely without massive mortality, but rather through an ideological and socio-economic reorientation that also preserved many aspects of their society.

On the opposite end of the spectrum, the immensely wealthy Qing Dynasty in China proved resilient to adverse ecological conditions – recurrent floods, droughts, swarms of locusts – during the early part of their reign, but by the 19th century, social pressures had built up leaving them more vulnerable to these same challenges. It was in this period that suffered the Taiping Rebellion, often seen as the bloodiest civil war in human history, and ultimately collapsed completely in 1912 after 250 years of rule. Learn more about the causes in a new study.

Map of the Qing Dynasty in 1820 © Wikimedia Commons CC BY-SA 3.0

In between, the researchers highlight the Ottoman Empire, which faced environmental shocks and daunting conditions during the 16th century, including recurrent droughts and the Little Ice Age, leading to social unrest and numerous rebellions led by disgruntled local officials and wealthy families, yet they managed to maintain key social and political structures and avoided collapse, ruling a large swath of territory for several hundreds of years more.


“Many studies typically concentrate on a single event or a specific society. However, it is only by exploring the responses of all, or at least many, societies affected by a particular climate ‘regime’ that we can ascertain the causal influence and overall effectiveness of the environmental stressor,” Peter Turchin says.


With this objective in mind, the researchers have developed a methodological framework aimed at producing insights that can be applied to numerous cases across different regions and time periods, helping identify the underlying causes of divergent outcomes.


“The course of a crisis hinges on numerous factors. Environmental forces are undeniably pivotal, but it’s not as straightforward as a specific climate event triggering a predetermined societal response,” asserts Turchin. Instead, these forces interact with cultural, political, and economic dynamics. Only by comprehending these dynamics can we fathom the interactions.


Through their work on the CrisisDB program, the researchers and colleagues aim to unveil these patterns and pinpoint the key factors that either fortify or undermine resilience to contemporary environmental shocks.


One key initial finding is that slowly evolving structural forces, such as escalating social inequality, which also happens currently, can erode social resilience. Hoyer emphasizes, “Dealing with large-scale threats demands considerable societal cohesion.”


As an example, he cites the Covid pandemic. Societies that showed higher levels of cohesion and the capacity for collective action before Covid broke out navigated the pandemic more effectively and successfully implemented the necessary distancing measures.


“Given that we reside in an era marked by increasing environmental shocks, economic disruptions, inequality, and major conflicts, our focus should be on reducing these structural pressures to build this kind of cohesion and resilience,” Hoyer underscores.



The Special Issue also features a study by Stephen Lansing (CSH External Faculty and Santa Fe Institute) and I Wayan Alit Artha Wiguna (Balai Pengkajian Teknologi Pertanian Bali) that could not only transform rice farming methods but also significantly mitigate greenhouse gas emissions. Asia alone boasts over 200 million rice farms, and rice fields contribute to a substantial 11% of global methane emissions.


This study has the potential to be a game-changer. Initial indications suggest that by regulating irrigation, greenhouse gas emissions could be reduced by a remarkable 70%, while also reducing excess commercial nitrogen fertilizer flowing from rice paddies to rivers and coral reefs.




With this method, the rice field was not flooded as usual and, therefore, did not provide an ideal environment for anaerobic, methane-emitting bacteria. Instead, it was drained and irrigated only when hairline cracks appeared on the surface. In addition to reducing greenhouse gas emissions, the farmer who owned the demonstration plot increased his crop yield on the drained field by more than 20%.


Lansing, who is an ecological anthropologist, has been researching Indonesia’s rice paddies since his arrival in Bali in 1974.


The study “Navigating Polycrisis: long-run socio-cultural factors shape response to changing climate” by Daniel Hoyer, James S. Bennett, Jenny Reddish, Samantha Holder, Robert Howard, Majid Benam, Jill Levine, Francis Ludlow, Gary Feinman and Peter Turchin was published in Philosophical Transactions B (doi: 10.1098/rstb.2022.0402).

The Qing Dynasty in China, after over 250 years, crumbled in 1912.

Led by the Complexity Science Hub (CSH), an international research team has pinpointed key reasons behind the collapse, revealing parallels to modern instability and offering vital lessons for the future.

Social instability today - different times, similar mechanisms, says a study from the Complexity Science Hub; pictures from Unsplash

China is considered today to be the world’s largest economy (in terms of PPP). However, this position is not new. In 1820, China’s economy already held the top spot, accounting for 32.9% of the global GDP. In the interim, there was a period of decline followed by a resurgence. After over 250 years in power, in 1912, the Qing Dynasty collapsed despite being considerably wealthier at the time than modern-day China. “This clearly demonstrates that any economy must be vigilant as circumstances can change, and sometimes rather rapidly,” emphasizes Georg Orlandi, the study’s first author.


“It’s crucial to comprehend the origins of such instabilities. Assuming it’s a thing of the past and can’t recur would be a mistake. Such changes can indeed happen because the underlying mechanisms bear surprising similarities,” CSH researcher Peter Turchin points out.


Scientists have been attempting to pinpoint the causes behind the fall of the Qing Dynasty for two centuries. Various factors had previously been proposed, including environmental disasters, foreign incursions, famines, or uprisings. However, “none of these factors provides a comprehensive explanation,” notes Turchin.

Map of the Qing Dynasty in 1820 © Wikimedia Commons CC BY-SA 3.0
Map of the Qing Dynasty in 1820 © Wikimedia Commons CC BY-SA 3.0


Hence, in this study, researchers amalgamated various factors and discovered that three elements dramatically heightened socio-political pressures:

Firstly, there was a fourfold population explosion between 1700 and 1840. This resulted in reduced land per capita and caused an impoverishment of the rural populace.


Secondly, this led to increased competition for elite positions. While the number of contenders soared, the number of awarded highest academic degrees declined, reaching its nadir in 1796. Because such a degree was necessary for obtaining a position in the powerful Chinese bureaucracy, this mismatch between the number of positions and those desiring them created a large pool of disgruntled elite aspirants. The leaders of the Taiping Rebellion, perhaps the bloodiest civil war in human history, were all such failed elite-wannabes.


Thirdly, the state’s financial burden escalated due to rising costs associated with suppressing unrest, declining per capita productivity, and mounting trade deficits stemming from depleting silver reserves and opium imports.

Collectively, these factors culminated in a series of uprisings that heralded the end of the Qing Dynasty and exacted a heavy toll in terms of Chinese lives lost.


According to the study’s findings, social tensions had already peaked between 1840 and 1890. “Assuming that the Qing rulers were unaware of this mounting pressure would be erroneous,” explains Turchin. The fact that the dynasty endured until 1912 rather underscores its institutional structures’ robustness.


However, many of their attempted solutions proved short-sighted or inadequate to the task; for instance, the government raised the allowable quota for people passing certain degree exams but without increasing the number of available openings. This ended up exacerbating the already-building tensions. With the arrival of potent geopolitical challengers through the late 19th century, the rulers ultimately couldn’t avert their downfall.


We can draw valuable lessons from this historical process for the contemporary era and the future. Many nations worldwide are grappling with potential instability and conditions that closely resemble those of the Qing Dynasty. For instance, competition for top positions remains exceedingly fierce. Orlandi cautions, “When a large number of individuals vie for a limited number of positions, political decision-makers should view this as a red flag, as it can, at the very least, lead to heightened instability.”


“Unfortunately, the corrosive impact of rising inequality and diminishing opportunities develop over longer time scales that make them hard to recognize,” adds co-author and CSH Affiliated Researcher Daniel Hoyer, “let alone effectively combat within the short political cycles we see in many countries. Without long-term vision and targeted strategies to relieve these social pressures, many places are at risk of going the way of the Qing.”


“We aren’t prophets. Our primary aim is to comprehend social dynamics, which we can then leverage for making forecasts,” elucidates Orlandi. The effectiveness of this endeavor using the Structural Demographic Theory (SDT), a method co-developed by Peter Turchin that represents societies as complex interactive systems, has been demonstrated by researchers on multiple occasions. For instance, a study published in 2010 forecasted the 2020 instability in the USA.


The study “Structural-demographic analysis of the Qing Dynasty (1644–1912) collapse in China” by Georg Orlandi, Daniel Hoyer, Hongjun Zhao, James S. Bennett, Majid Benam, Kathryn Kohn and Peter Turchin was published in PLOS ONE (doi: 10.1371/journal.pone.0289748).

The 5-day event brings together professionals with different perspectives on data visualization 


How can complexity science researchers, programmers, information designers, data journalists, and visual artists work together? The Visualizing Complexity Science Workshop, starting today, August 28, at the Complexity Science Hub (CSH), aims to answer this question.


Liuhuaying Yang, CSH’s data visualization expert, hosts the 5-day workshop with Paul Kahn, an information designer who teaches at Northeastern University. 


“My belief is that by mixing and matching individuals from various categories, we can foster a dynamic exchange of perspectives. Each participant can contribute their unique insights and inspire others, resulting in a truly enriching and collaborative experience”, evaluates Yang.


Outside their comfort zone


“They have overlapping skills, but they generally work in very different worlds where their work serves different purposes. We are asking the workshop participants to think about kinds of presentations that may be outside their comfort zone or their experience,” adds Kahn. 


During the workshop, participants will work in teams on complexity science research datasets during hands-on sessions. “We hope that by working in teams, visualization concepts will emerge that will be new to everyone, the programmers and scientists as well as the artists, the designers as well as the journalists.”




Another key aspect of the workshop, according to Yang, is immersion. “While exchanging ideas and participating in brainstorming sessions to develop initial concepts is undoubtedly valuable, it’s crucial to recognize that this phase represents only half of the journey towards achieving our desired outcomes,” says the CSH data visualization expert.


“In my experience, data visualization work heavily relies on practical implementation and hands-on experience. Dreams and ideas are beautiful, but they don’t always come true. Every idea needs to be put to the test and validated using real data within the context of actual projects.”


In order to ensure an immersive experience, Yang has curated two CSH research projects for the workshop. The participants will be introduced to the projects and datasets by CSH researchers Dániel Kondor and Eddie Lee.


Collaboration and experimentation


Yang emphasizes that the workshop should be physical (in person) to facilitate collaboration and experimentation. “This way, participants can delve deep into the projects and gain practical insights that will significantly enrich their understanding and skills in data visualization.”


In addition to working sessions, the program includes lectures by the hosts and invited guests, and group discussions. A wide range of topics will be covered in the talks, from data visualization and interactive design in news storytelling in China to art and science collaboration.


Jen Christiansen, senior graphics editor at Scientific American; Samuel Huron, professor at the Institut Polytechnique de Paris; Meng Wei, head of the Caixin Vislab in China; Dirk Brockmann, professor at the Humboldt-University of Berlin; Robin Meier Wiratunga, artist and composer; Guy Amichay, a postdoc fellow at Northwestern University; and artist Alberto Pino will be the guest speakers. 


Learn more about the workshop: https://vis.csh.ac.at/vis-workshop-2023/ 

The statistical model identifies links between battles in Africa and can be applied to other armed conflicts, according to a study at the Complexity Science Hub 

Around the world, political violence increased by 27 percent last year, affecting 1.7 billion people. The numbers come from the Armed Conflict Location & Event Data Project (ACLED), which collects real-time data on conflict events worldwide.


Some armed conflicts occur between states, such as Russia’s invasion of Ukraine. There are, however, many more that take place within the borders of a single state. In Nigeria, violence, particularly from Boko Haram, has escalated in the past few years. In Somalia, populations remain at risk amidst conflict and attacks perpetrated by armed groups, particularly Al-Shabaab. 


To address the challenge of understanding how violent events spread, a team at the Complexity Science Hub (CSH) created a mathematical method that transforms raw data on armed conflicts into meaningful clusters by detecting causal links. 


“Our main question was: what is a conflict? How can we define it?,” says CSH scientist Niraj Kushwaha, one of the authors of the study published in the latest issue of PNAS Nexus. “It was important for us to find a quantitative and bias-free way to see if there were any correlations between different violent events, just by looking at the data.”


“We often tell multiple narratives about a single conflict, which depend on whether we zoom in on it as an example of local tension or zoom out from it and consider it as part of a geopolitical plot; these are not necessarily incompatible,” explains coauthor Eddie Lee, a postdoctoral fellow at CSH. “Our technique allows us to titrate between them and fill out a multiscale portrait of conflict.”


In order to investigate the many scales of political violence, the researchers turned to physics and biophysics for inspiration. The approach they developed is inspired by studies of stress propagation in collapsing materials and of neural cascades in the brain.


Kushwaha and Lee used data on violent battles in Africa between 1997 and 2019 from ACLED. In their analysis, they divided the geographic area into a grid of cells and time into sequential slices. The authors predicted when and where new battles would emerge by analyzing the presence or absence of battles in each cell over time. 


“If there’s a link between two cells, it means a conflict at one location can predict a conflict at another location,” explains Kushwaha. “By using this causal network, we can cluster different conflict events.”

Snow and sandpile avalanches

Observing the dynamics of the clusters, the scientists found that armed clashes spread like avalanches. “In a way evocative of snow or sandpile avalanches, a conflict originates in one place and cascades from there. There is a similar cascading effect in armed conflicts,” explains Kushwaha.


The team also identified a “mesoscale” for political violence —a time scale of a few days to months and a spatial scale of tens to hundreds of kilometers. Violence seems to propagate on these scales, according to Kushwaha and Lee.


Additionally, they found that their conflict statistics matched those from field studies such as in Eastern Nigeria, Somalia, and Sierra Leone. “We connected Fulani militia violence with Boko Haram battles in Nigeria, suggesting that these conflicts are related to one another,” details Kushwaha. The Fulani are an ethnic group living mainly in the Sahel and West Africa. 


Policymakers and international agencies could benefit from the approach, according to the authors. The model could help uncover unseen causal links in violent conflicts. Additionally, it could one day help forecast the development of a war at an early stage. “By using this approach, policy decisions could be made more effectively, such as where resources should be allocated,” notes Kushwaha.


The study “Discovering the mesoscale for chains of conflict” by Niraj Kushwaha and Eddie Lee appeared in PNAS Nexus.

Four intense days of conversation and interchange in Vienna. Around 850 scientists from all over the world attended NetSci 2023 from July 10 to 14 to discuss emerging topics in network science. This year’s edition was organized by the Complexity Science Hub (CSH) and the Central European University (CEU).


At the University of Vienna campus, in the heart of the Austrian capital, participants enjoyed a full program. This included expert talks, parallel sessions, satellites, panel discussions, lightning talks, poster sessions, and an international school.


During the conference, the seven keynote speakers provided insight into the interdisciplinary nature of network science research across physics, computer science, biology, the social sciences, and economics, among others.


The audience kept its eyes focused on Shlomo Havlin from Bar-Ilan University and an external faculty member at CSH; Mirta Galesic, a professor at the Santa Fe Institute and a resident scientist at CSH; Kathleen Carley, from Carnegie Mellon University; Renaud Lambiotte, from the University of Oxford; Natasa Przulj, from the Barcelona Supercomputing Cluster; Marta Sales-Pardo, from Rovira i Virgili University; and Vito Latora, from Queen Mary University of London and external faculty member at CSH.


The satellite and parallel sessions also covered a variety of topics, ranging from epidemic control to economic complexity and network inequality. Additionally, the program included panel discussions about academic writing, mental health and parenthood in academia. And for the first time, the flagship conference of the Network Science Society provided free childcare. This allowed working parents to fully engage at NetSci2023 without worrying about their little ones.


NetSci 2023 also featured a great deal of social gathering, which culminated in a gala dinner held at Vienna City Hall. We are already looking forward to NetSci 2024 in Québec City!

Early malnutrition increases the risk of type 2 diabetes and other diseases later in life. In a recent study, researchers at the Complexity Science Hub and Medical University of Vienna showed this to be true.

A number of studies have already shown that malnutrition during pregnancy may increase the risk of developing type 2 diabetes later on in life. According to a 2013 study by Peter Klimek and his team, people born during a famine have more than twice the risk of diabetes compared to those born one year earlier or later. 




Now, for the first time, Klimek and his team have succeeded in measuring not only the total number of diabetes cases (prevalence), but also the incidence, or the number of new cases, in a recent study. 


 “Among men born during the two most severe famine periods, 1939 and 1946/1947, the rate of new cases of diabetes is up to 78 percent higher in 2013 to 2017 than in comparable years, and up to 59 percent higher among women,” explains Klimek, from the Complexity Science Hub and the Medical University of Vienna. The effect is strongest in those born in 1939. 


The incidence rate rose from 3.9 percent to 6.9 percent among men and from 3.4 percent to 5.4 percent among women. Additionally, both groups have an increased incidence of concomitant conditions such as heart failure, arterial hypertension, chronic obstructive pulmonary disease (COPD), and kidney disease.




Scientists believe this is a result of genetic programming that occurs during pregnancy, which increases the risk of these diseases. As a result of deficiency, the unborn child’s metabolism adjusts to a nutritionally poor environment. If this does not prove true later in life, a maladaptation occurs that leads to increased metabolic and cardiovascular diseases in these birth groups.




“One strength of our study is the new, large dataset on which it is based,” says Klimek. This covers 99.9 percent of the Austrian population between 2012 and 2017, and all insured patients aged over 50 and under 100 were examined. Of these approximately 3.5 million people, 746,184 were treated for diabetes. The comprehensive dataset allowed researchers to measure age-specific and regional incidence rates directly for the entire population, without additional assumptions that would be required for modeling. 


“Our results clearly demonstrate that public health efforts to address diabetes should not focus solely on lifestyle factors. The importance of reproductive health, as well as adequate nutrition during pregnancy and in the early postnatal period, must also be considered,” Klimek said.





The study “Diabetes incidence in Austria: The role of famines on diabetes and related NCDs” was recently published in the journal Heliyon.

Some of the most prominent women in network science are taking the stage at NetSci 2023. This year’s edition is organized by CSH and CEU

Women are still underrepresented in STEM (science, technology, engineering and mathematics) fields. They make up only 21% of computer science majors in the US. Physics has a female rate of 24%. Additionally, the STEM workforce is dominated by men with only 34% of female workers, according to a National Girls Collaborative Project’s report.


But, as women scientists break the glass ceiling in network science, they are producing innovative and creative research. Some of them are taking the stage at NetSci, the flagship conference of the Network Science Society. 

Fariba Karimi

Fariba Karimi

Karimi runs a research group at the Complexity Science Hub dedicated to understanding the causes and effects of network inequality in the real world. By using methods from statistical physics and network theory, she developed new types of network models that are better suited to study pressing societal issues such as visibility of minorities and structural marginalization in society and algorithms. For instance, she recently publisehd a study to discover why women are discriminated against in physics. Karimi is leading a section at Netsci international school and presenting metrics, models, and algorithms related to network inequality.

Mirta Galesic © private

Mirta Galesic 

Galesic, a professor at the Santa Fe Institute and a resident faculty member at the Complexity Science Hub, is striving to understand complex social phenomena. She’ll be one of the keynote speakers at NetSci and will discuss how internal dynamics and external influences shape our beliefs, and why some people and groups change their beliefs more readily than others. Galesic will present a few examples of networks of beliefs on both a collective and an individual level, on different topics ranging from politics to vaccinations.

Other top scientists


Other top scientists who happen to be women will present and discuss their work during Netsci. The conference will also feature keynote speakers Nataša Pržulj, from the Barcelona Supercomputing Center; Marta Sales-Pardo, from Rovira i Virgili University; and Kathleen Carley, from Carnegie Mellon University. 


Also attending NetSci will be Tina Eliassi-Rad, who recently received the Lagrange – CRT Foundation Prize, one of the most prestigious awards in complexity science; and Ágnes Horvát, from Northwestern University.


July 10-14, 2023 (8:30-18:30), Universitätsring 1, 1010 Vienna | Go to the conference program.

The transfer of pigs from one place to another poses the risk of spreading infectious diseases.

Knowing how holdings (e.g., farms, markets, etc.) are connected is therefore of crucial importance. In a study by the Complexity Science Hub, the University of Veterinary Medicine Vienna, and the Austrian Agency for Health and Food Safety (AGES), researchers are now drawing a map of the Austrian pig trade network for the first time.

Every year, around 250,000 transfers of pigs take place in Austria. Each of these transfers carries a certain risk of spreading swine infectious diseases. To identify possible risks of disease spread and develop targeted preventive measures, it is necessary to know how individual holdings are interconnected.


“We used anonymized daily movement data of live pigs traded in Austria – from birth to slaughterhouses – from 2015 to 2021,” explains Gavrila A. Puspitarani, a researcher at the Complexity Science Hub and the University of Veterinary Medicine Vienna. Based on this, the scientists created a network that maps domestic trades between holdings in Austria. 

“With these insights into the pig trade, we can provide valuable support to veterinarians and other stakeholders in developing data-driven approaches for controlling diseases during outbreaks and facilitating preventive actions,” explains Amélie Desvars-Larrive of the Complexity Science Hub and the University of Veterinary Medicine Vienna. Outputs provided in this study can also serve as valuable inputs for developing predictive epidemiological models that simulate the transmission of diseases between farms. 


By analyzing the network structure, the scientists identified the most significant risks. Typically, this risk is highest in areas with high animal density and frequent transfers. In Austria, there are major differences identified between the federal states. According to the study, the greatest risk arises in Upper Austria and Styria, as these regions account for nearly half (46 percent) of all holdings with significant trading activity. “If an infectious disease outbreak occurs there, it might spread faster than in Vorarlberg, for example, where pig and farm density are much lower,” says Desvars-Larrive. 

Austria's pig trade network © Complexity Science Hub and vetMed
Spatial density of pig farms with recorded movements in Austria in 2021. The black lines represent the administrative borders of the federal states.


The study also shows that the vast majority of pig movements occur within each federal state and rarely across federal states. This increases the chance of controlling infectious diseases quickly and regionally before they propagate across the country. 


Moreover, the pig trade network in Austria is not well-connected, which means that the trade frequency between holdings is relatively low, similar to the pig trade networks in Georgia or northern Macedonia. In contrast, pig holdings in Germany or France have more dense connections with some trades covering long distances, which might favor large-scale and long-distance disease spread. 

Austria's pig trade network © Complexity Science Hub and VetMed
Maps of the detected trade community based on the yearly aggregated networks of pig movements in Austria, 2015–2021. Blue lines represent the district administrative boundaries; black dashed lines represent federal state boundaries. Colors represent communities. We used the InfoMap algorithm67 that allows no overlap, i.e. a district can belongs to one community only.

Imports and exports play a minor role in Austria. The self-sufficiency rate of pork production is 103 percent, and only about one to two percent of pigs came from abroad or were exported abroad. 


The majority of farms in Austria are relatively small, with around 60 percent of them keeping fewer than five pigs. At the other end of the spectrum, there are a limited number of significantly large farms. For instance, in 2021, the largest farm housed more than 15,000 pigs.


The analysis shows that the network in Austria is topologically very stable over time.  At the same time, it highlights the important role of certain super-connected holdings as “super-receivers” (receiving a lot of pigs) or “super-spreaders” (sending a lot of pigs) that could be used as “sentinels” for disease detection, for example.


“Austria, therefore, shows favorable conditions for the establishment of consistent monitoring and prevention strategies that can be utilized over the long term,” explains Puspitarani. And this study can contribute significantly to that. 


“Moreover, this research shows the potential of interdisciplinary collaboration, integrating knowledge from multiple disciplines – such as complexity science and veterinary medicine – to address practical issues,” says Puspitarani. 



The paper “Network analysis of pig movement data as an epidemiological tool: an Austrian case study” has been published in the journal Scientific Reports (doi: 10.1038/s41598-023-36596-1).