arXiv subjects

Yasaman Asgari

Publications and source records attributed to Yasaman Asgari.

5 recordsLinked to original sources

arXiv2025

Arab Spring's Impact on Science through the Lens of Scholarly Attention, Funding, and Migration

The 2010-2011 Arab Spring reverberated far beyond politics, reshaping how the Middle East and North Africa region (MENA) is studied. Analyzing 3.7 million Scopus-indexed articles published between 2002 and 2019, we find that mentions of ten of these countries in titles or abstracts rose significantly after 2011 relative to the global baseline, with Egypt receiving the greatest attention in the region. We link this surge to two intertwined mechanisms: an increase in research funding directed at the MENA region and the emigration of researchers who continued publishing on their countries of origin. Our analysis reveals that Saudi Arabia has emerged as a regional hub for studying the affected countries, attracting funding and scholars, and thereby playing a significant role in shaping the scientific narrative on the region. These findings demonstrate how political upheaval can reshape global knowledge flows by altering who studies whom, with what resources, and in which disciplines.

cs.DL↗

arXiv2024

Exploring Relationships Between Cryptocurrency News Outlets and Influencers' Twitter Activity and Market Prices

Academics increasingly acknowledge the predictive power of social media for a wide variety of events and, more specifically, for financial markets. Anecdotal and empirical findings show that cryptocurrencies are among the financial assets that have been affected by news and influencers' activities on Twitter. However, the extent to which Twitter crypto influencer's posts about trading signals and their effect on market prices is mostly unexplored. In this paper, we use LLMs to uncover buy and not-buy signals from influencers and news outlets' Twitter posts and use a VAR analysis with Granger Causality tests and cross-correlation analysis to understand how these trading signals are temporally correlated with the top nine major cryptocurrencies' prices. Overall, the results show a mixed pattern across cryptocurrencies and temporal periods. However, we found that for the top three cryptocurrencies with the highest presence within news and influencer posts, their aggregated LLM-detected trading signal over the preceding 24 hours granger-causes fluctuations in their market prices, exhibiting a lag of at least 6 hours. In addition, the results reveal fundamental differences in how influencers and news outlets cover cryptocurrencies.

cs.SI↗

arXiv2024

The overlooked need for Ethics in Complexity Science: Why it matters

Complexity science, despite its broad scope and potential impact, has not kept pace with fields like artificial intelligence, biotechnology and social sciences in addressing ethical concerns. The field lacks a comprehensive ethical framework, leaving us, as a community, vulnerable to ethical challenges and dilemmas. Other areas have gone through similar experiences and created, with discussions and working groups, their guides, policies and recommendations. Therefore, here we highlight the critical absence of formal guidelines, dedicated ethical committees, and widespread discussions on ethics within the complexity science community. Drawing on insights from the disciplines mentioned earlier, we propose a roadmap to enhance ethical awareness and action. Our recommendations include (i) initiating supportive mechanisms to develop ethical guidelines specific to complex systems research, (ii) creating open-access resources, and (iii) fostering inclusive dialogues to ensure that complexity science can responsibly tackle societal challenges and achieve a more inclusive environment. By initiating this dialogue, we aim to encourage a necessary shift in how ethics is integrated into complexity research, positioning the field to address contemporary challenges more effectively.

physics.soc-ph↗

arXiv2024

Longitudinal Modularity, a Modularity for Link Streams

Temporal networks are commonly used to model real-life phenomena. When these phenomena represent interactions and are captured at a fine-grained temporal resolution, they are modeled as link streams. Community detection is an essential network analysis task. Although many methods exist for static networks, and some methods have been developed for temporal networks represented as sequences of snapshots, few works can handle link streams. This article introduces the first adaptation of the well-known Modularity quality function to link streams. Unlike existing methods, it is independent of the time scale of analysis. After introducing the quality function, and its relation to existing static and dynamic definitions of Modularity, we show experimentally its relevance for dynamic community evaluation.

cs.SI↗

arXiv2023

Mosaic benchmark networks: Modular link streams for testing dynamic community detection algorithms

Community structure is a critical feature of real networks, providing insights into nodes' internal organization. Nowadays, with the availability of highly detailed temporal networks such as link streams, studying community structures becomes more complex due to increased data precision and time sensitivity. Despite numerous algorithms developed in the past decade for dynamic community discovery, assessing their performance on link streams remains a challenge. Synthetic benchmark graphs are a well-accepted approach for evaluating static community detection algorithms. Additionally, there have been some proposals for slowly evolving communities in low-resolution temporal networks like snapshots. Nevertheless, this approach is not yet suitable for link streams. To bridge this gap, we introduce a novel framework that generates synthetic modular link streams with predefined communities. Subsequently, we evaluate established dynamic community detection methods to uncover limitations that may not be evident in snapshots with slowly evolving communities. While no method emerges as a clear winner, we observe notable differences among them.

cs.SI↗