Széchenyi 2020
Budapesti Corvinus Egyetem ×

Laboratory for Networks, Technology & Innovation (NeTI Lab)

The economy is changing. Jobs are disappearing while creative careers are booming across the globe. Today’s economy is more turbulent, technological, and connected than ever before. It is centered on knowledge intense teams, global connections, andnew modes of work and life that are reshaping the way in which people source talent and do business.

Networks, Technology, & Innovation (NeTI) Lab is a group established in 2020 at the Corvinus Institute of Advanced Studies. Our research focuses on the impact of networks, innovation, and technologies on work andsociety.

Follow us on twitter @anetilabs
  1. Patterns of social connections in urban space. We analyze the relationship between commuting and social network ties combining geo-located twitter data from U.S. cities and demographic data from the 2012 American Community Survey.
  2. Organizational networks, skills and performance. We analyze the relationship between skills of employees, the network structure of their interactions, and firm performance. We combine survey data, social network (linkedIn) data and administrative data in this project. The first article of this project analyzes the relationship between local vs. distant connections and the skills of workers. The next considers the role network position of firms in firm growth. This project is in collaboration with Rikard Eriksson from Umea university.
  3. The role of relational embeddedness in the development and dissolution of firms’ supplier-buyer relationships. We analyze the development of structural embeddedness (presence of common partners), firm ownership networks, and personal connections of managers.We plan to use transaction data of firms using the VAT database – in collaboration with the Hungarian National Bank.
  4. Geographical mobility and social networks of early career researchers in Hungary. We are developing a survey and combine it with publication databases – in collaboration with the CERS Anet Lab, the HAS Young Scholar’s Academy and HAS Library Dept. Of Science Policy and Scientometrics.
CIAS Balazs Lengyel NeTI LAB

      Balázs Lengyel
      Senior Research Fellow

Balázs Lengyel is an economic geographer; he completed his PhD in economics at Budapest Universityof Technology and Economics in 2010 and holds a master degree in Economics from University of Szeged. He was a visiting scholar at the Human Mobility and Networks Lab of MIT in 2016-2017. He is a Lendület Research Grant holder and head of the ANET Lab at Research Centre for Economic and Regional Studies. His research focuses on the dynamics of social networks, spatial diffusion of innovation, and on the technological and economic development of regions and cities.

Selected publications:
1. Eriksson RH, Lengyel B (2019) Co-worker networks and agglomeration externalities. Economic Geography 95(1), 65-89.
2. Lengyel B, Eriksson RH (2017) Co-workernetworks, labour mobility and productivity growth in regions. Journal of Economic Geography 17(3), 635-660.
3. Lengyel B, Varga A, Ságvári B, Jakobi Á, Kertész J (2015) Geographies of an online social network. PLoS ONE 10(9)e0137248
Balazs Lengyel CV
CIAS Laszlo Lorincz NeTI Lab

      László Lőrincz
      Senior Research Fellow

László Lőrincz  is a Sociologist (Ph.D, Corvinus Univeristy, 2009) / Economist (M.Sc., Corvinus University, 2002). Since 2013 he is member of the Economics of Networks research research group at Research Centre for Economic and Regional Studies. Between 2016-2019 he worked as assistant professor at Centre for Labor Economics at Corvinus University.  His research portfolio includes adoption and collapse of online social networks, and network effects in labor mobility and migration. He taught courses on Social Network Analysis, Agent-Based Simulation, Labor Economics and Multivariate Statistical Analysis.

Selected publications:
1. Lőrincz, L., Koltai, J., Győr, A. F., & Takács, K. (2019). Collapse of an online social network: Burning social capitalto create it?. Social Networks, 57, 43-53.
2. Csáfordi, Z., Lőrincz, L., Lengyel, B., & Kiss, K. M. (2020). Productivity spillovers through labor flows:Productivity gap, multinational experience and industry relatedness. The Journal of Technology Transfer 45, 86–121.
3. Lőrincz, L. (2016). Interethnic dating preferences of Roma and non-Roma secondary school students. Journal of Ethnic and Migration Studies, 42(13), 2244-2262.
Laszlo Lorincz Publications List
Laszlo Lorincz CV
CIAS Eszter Bokanyi NeTI Lab

      Eszter Bokányi
      Postdoctoral Fellow

Eszter Bokányi studied Physics at Eötvös Loránd University, Budapest and Humbolt Universität zu Berlin specializing in statistical physics. She’s graduated at the Physics Doctoral School of Eötvös Loránd University in 2019, where topic of her thesis was studying how social phenomena can be captured through statistical physical methods by using various digital fingerprints or social networks of individuals.

Selected publications:
1. Bokányi, E., Szállási, Z., & Vattay, G. (2018). Universal scaling laws in metro area election results. PloS one, 13(2).
2. Bokányi, E., & Hannák, A. (2019). Ride-share matching algorithms generate income inequality. arXiv preprint arXiv:1905.12535.
Eszter Bokanyi CV
CIAS Sandor Juhasz NeTI Lab
      Sándor Juhász
      Postdoctoral Fellow
      twitter: @sandor_juhasz

Sándor Juhász has earned his PhD at Utrecht University, Faculty of Geosciences in 2019. His thesis focuses on the role of geography in the formation of network ties in various contexts. He is interested in data tricks, topics related to the geography of innovation, network dynamics and urban mobility research.

Selected publications:
1. Juhász, S., Tóth, G., & Lengyel, B. (2020). Brokering the core and the periphery: Creative success and collaboration networks in the film industry. PloS one, 15(2),e0229436.
2. Juhász, S. (2019). Spinoffs and tie formation in cluster knowledge networks. Small Business Economics, 1-20.
3. Juhász, S., & Lengyel, B. (2018). Creation and persistence of ties in cluster knowledge networks. Journal of Economic Geography, 18(6), 1203-1226.
Rebeka O Szabo

Rebeka O. Szabó
Postdoctoral Fellow

Rebeka O. Szabó is a sociologist-network scientist. She is a finishing Ph.D. candidate at the Department of Network and Data Science at Central European University. Before that, she earned her master degree at Universiteit Van Amsterdam in Sociology (Comparative Organizations and Labor Studies). She was a visiting research fellow at Kellogg School of Management and The Northwestern Institute on Complex Systems of Northwestern University in 2020. Her main scientific interests are social networks, teams, organizational behavior, development, and change.

Kapcsolódó dokumentumok

Rebeka O. Szabo CV
César A. Hidalgo

Professor Hidalgo is a world renowned expert in the fields of economic complexity,international development, network science and data science. He holds an ANITI Chair on Natural and Artificial Intelligence at the University of Toulouse. He is a honorary professor at Manchester University and a professor of practice at Harvard University. Prior to joining the University of Toulouse, Professor Hidalgo was the director of the Collective Learning group at MIT (2010-2019) and a faculty associate at Harvard (2008-2012). He is the author of multiplehighly cited publications and books, including Why Information Grows (Basic Books, 2015) and The Atlas of Economic Complexity (MIT Press, 2014).

Personal website:

We have maximum 2 vacancies for full-time Ph.D student positions in collaboration with the Doctoral School of Sociology. In addition to their studies, successful applicants participate in the research activities of the lab that as (paid) research interns.

The research projects proposed by NeTI lab can be found here.

Details of the admission process can be found here.

Deadline for the 2021/22 academic year is 10 january for non-EU applicants applying for Stipendium Hungaricum scholarship, and later (not yet announced) for EU or Hungarian applicants.

Currently we don’t have any open Post-Doctoral Fellow positions.
1. Juhász S, Boschma R, Broekel T (2020) Explaining dynamics of relatedness: the role of co-location and complexity. Papers in Regional Science.

Relatedness has become a key concept for studying the diversification of firms, regions and countries. However, studies tend to treat relatedness as being time‐invariant or, alternatively, consider its evolution as exogenously given. This study argues that relatedness is inherently dynamic and endogenous to technological and economic developments. Using patent data, we test the extent to which relatedness between technologies developed along co‐location and differences in technological complexity in 1980–2010. Our results show that co‐located technologies are more likely to become related over time. Moreover, our results suggest that co‐location and complexity of technologies are conducive to the intensification of relatedness over time.

(a) The density of all the possible technological combinations on a complexity‐complexity plot (1980–2010), (b) The density of all observed technological combinations on a complexity‐complexity plot (1980–2010).

2. Lengyel B, Bokányi E, Di Clemente R, Kertész J, González MC (2020) The role of geography in the complex diffusion of innovation. Scientific Reports.

The urban–rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. Although both model versions can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales in all model specifications.

Adoption peak prediction on local scales with the Bass model. (A) The Bass DE model estimates on the monthly adoption trend and a smoothed empirical adoption trend (3-month moving average) are compared. (B) Times of adoption peaks vary across towns. (C) Estimated pi and qi result in same adoption peak with fixed pi, except in early adoption cases when qi is high. (D) Estimated peaks of adoption correlate with empirical peaks of adoption (p=0.74). (E) Prediction Error in town i. (F) Dots are point estimates of linear univariate regressions and bars depict standard errors. Dependent variable is scaled with its maximum value and independent variables are log-transformed with base 10.

3. Lőrincz L, Da Silva GKC, Hannák A, Takács D, Lengyel B, Eriksson R (2020) Global connections and the structure of skills in local co-worker networks. Applied Network Science.

Social connections that reach distant places are advantageous for individuals, firms and cities, providing access to new skills and knowledge. However, systematic evidence on how firms build global knowledge access is still lacking. In this paper, we analyse how global work connections relate to differences in the skill composition of employees within companies and local industry clusters. We gather survey data from 10% of workers in a local industry in Sweden, and complement this with digital trace data to map co-worker networks and skill composition. This unique combination of data and features allows us to quantify global connections of employees and measure the degree of skill similarity and skill relatedness to co-workers. We find that workers with extensive local networks typically have skills related to those of others in the region and to those of their co-workers. Workers with more global ties typically bring in less related skills to the region. These results provide new insights into the composition of skills within knowledge-intensive firms by connecting the geography of network contacts to the diversity of skills accessible through them.

Geography of respondents’ national (a) and global (b) networks. Node size represents number of connections. Links tend to connect the region to major cities or countries, but geographic proximity also matters.

4. Csomós Gy, Vida Zs, Lengyel B (2020) Science cities seek new connections. Nature Index.

We analyze high-impact publications from global leading science centers. We show that most high-impact articles are born in emerging Asian cities. At the same time, the proportions of US and EU centers can produce more high-impact articles.

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