How popular a topic or an opinion appears to be in a network can be very different from its actual popularity. For example, in an online network of a social media platform, the number of people who mention a topic in their posts—i.e., its global popularity—can be dramatically different from how people see it in their social feeds—i.e., its perceived popularity—where the feeds aggregate their friends’ posts. We trace the origin of this discrepancy to the friendship paradox in directed networks, which states that people are less popular than their friends (or followers) are, on average. We identify conditions on network structure that give rise to this perception bias, and validate the findings empirically using data from Twitter. Within messages posted by Twitter users in our sample, we identify topics that appear more frequently within the users’ social feeds, than they do globally, i.e., among all posts. In addition, we present a polling algorithm that leverages the friendship paradox to obtain a statistically efficient estimate of a topic’s global prevalence from biased perceptions of individuals. We characterize the bias of the polling estimate, provide an upper bound for its variance, and validate the algorithm’s efficiency through synthetic polling experiments on our Twitter data. Our paper elucidates the non-intuitive ways in which the structure of directed networks can distort social perceptions and resulting behaviors.

 

Friendship Paradox Biases Perceptions in Directed Networks
Nazanin Alipourfard, Buddhika Nettasinghe, Andres Abeliuk, Vikram Krishnamurthy, Kristina Lerman

Source: arxiv.org

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies.

 

Computational Socioeconomics

Jian Gao, Yi-Cheng Zhang, Tao Zhou

Source: arxiv.org

Exchange rates are important indicators of the economic power of countries, directly affected by the international trading patterns and relations. Since almost every pair of countries in the globalized world are economically and financially related, exchange rates can be evaluated as nodes of a global financial network to make meaningful inferences.

In this study, a financial network approach is conducted by evaluating the movements of the most traded 35 currencies against gold between years 2005 and 2017. Using graph theory and statistical methods, the analysis of economic relations between currencies is carried out, supported with geographical and cultural inferences. A risk map of currencies is generated through the portfolio optimization. Another approach of applying various threshold levels for correlations to determine connections between currencies is also employed. Results indicate that there exists a saddle point for correlation threshold as 0.9 which results in a robust network topology that is highly modular and clustered, also dominantly displaying small-world and scale-free properties.

Is the world small enough? — A view from currencies
Yusuf Yargi Baydilli and İlker Türker

International Journal of Modern Physics B Vol. 33, No. 12, 1950120 (2019)

Source: www.worldscientific.com

Transcriptional co-expression networks represent the concerted gene regulation programs by means of statistical inference of co-expression patterns. The rich phenomenology of transcriptional processes behind complex phenotypes such as cancer, is often captured (at least partially) in the connectivity structure of transcriptional co-expression networks. By analyzing the community structure of these networks, we may develop a deeper understanding of that phenomenology. We identified the modular structure of a transcriptional co-expression network obtained from breast cancer gene expression as well as a non-cancer adjacent breast tissue network as a control. We then analyzed the biological functions associated to the resulting communities by means of enrichment analysis. We also generated two projected networks for both, tumor and control networks: The first one is a projection to a network in which nodes are communities and edges represent topologically adjacent communities, indicating co-expression patterns between them. For the second projection, a bipartite network was generated containing a layer of modules and a layer of biological processes, with links between modules and the functions in which they are enriched; from this bipartite network, a projection to the community layer was obtained. From the analysis of the communities and projections, we were able to discern distinctive patterns of regulation between tumors and controls. Even though the connectivity structure of transcriptional co-expression networks is quite different, the topology of the projected networks is somehow similar, indicating functional compartmentalization, in both tumor and control conditions. However, the biological functions represented in the corresponding modules resulted notably different, with the tumor network comprising functional modules enriched for well-known hallmarks of cancer.

 

Functional and transcriptional connectivity of communities in breast cancer co-expression networks
Guillermo de Anda-Jáuregui, Sergio Antonio Alcalá-Corona, Jesús Espinal-Enríquez and Enrique Hernández-Lemus
Applied Network Science 2019 4:22
https://doi.org/10.1007/s41109-019-0129-0

Source: appliednetsci.springeropen.com