社会与信息网络
The Multi-platform Aggregated Dataset of Online Communities (MADOC) is a comprehensive dataset that facilitates computational social science research by providing FAIR-compliant standardized access to cross-platform analysis of online…
Although Graph Neural Networks (GNNs) have become the dominant approach for graph representation learning, their performance on link prediction tasks does not always surpass that of traditional heuristic methods such as Common Neighbors and…
Understanding how emotions diffuse through social networks is central to computational social science. Recently, large language models (LLMs) have been increasingly used to simulate social media interactions, raising the question of whether…
The COVID-19 pandemic has created many problems, especially in people's social lives. There has been increasing isolation and economic hardships since the beginning of the pandemic for people all over the world. Quarantines and lockdowns…
The 2024 U.S. Presidential Election unfolded within an information environment of unprecedented volatility, challenging citizens to navigate a torrent of rapidly evolving, often contradictory information while determining what to believe.…
Leveraging a validated set of reconstructed Lightning Network topology snapshots spanning five years (2019-2023), we computed 47 computationally intensive metrics and network attributes, enabling a comprehensive analysis of the network's…
Graphs are pervasive in our everyday lives, with relevance to biology, the internet, and infrastructure, as well as numerous other applications. It is thus necessary to have an understanding as to how quickly a graph disintegrates, whether…
The spread of toxic content on online platforms presents complex challenges that call for both theoretical insight and practical tools to test intervention strategies. In this novel research paper, we introduce a simulation-based framework…
Crowd-sourced fact-checking provides social media platforms with a promising method of managing misinformation at scale. However, the success of fact-checking programs like X's Community Notes requires the participation of a critical mass…
AI companion chatbots, such as those offered by Replika and CharacterAI, increasingly function as always-available companions that provide empathy, validation, and support. While these systems appear to meet basic needs for connection,…
In the era of widespread online content consumption, effective detection of coordinated efforts is crucial for mitigating potential threats arising from information manipulation. Despite advances in isolating inauthentic and automated…
Community detection, which uncovers closely connected vertex groups in networks, is vital for applications in social networks, recommendation systems, and beyond. Real-world networks often have bipartite structures (vertices in two disjoint…
Designing networks to optimize robustness and other performance metrics is a well-established problem with applications ranging from electrical engineering to communication networks. Many such performance measures rely on the Laplacian…
Profit Maximization is one of the key objectives for social media marketing, where the task is to choose a limited number of highly influential nodes such that their initial activation leads to maximum profit. In this paper, we introduce a…
Although the automation and digitisation of anti-financial crime investigation has made significant progress in recent years, detecting insider trading remains a unique challenge, partly due to the limited availability of labelled data. To…
The relationship between crime and the media has long been a focal point of academic research, with traditional media playing a significant role in shaping public perceptions of safety and community well-being. However, the advent of social…
Cyberbullying continues to grow in prevalence and its impact is felt by thousands worldwide. This study seeks a network science perspective on cyberbullying interaction patterns on the popular photo and video-sharing platform, Instagram.…
This paper asks whether promotional Twitter/X bots form behavioural families and whether members evolve similarly. We analyse 2,798,672 tweets from 2,615 ground-truth promotional bot accounts (2006-2021), focusing on complete years 2009 to…
Recently, neighbor-based contrastive learning has been introduced to effectively exploit neighborhood information for clustering. However, these methods rely on the homophily assumption-that connected nodes share similar class labels and…
Traffic congestion propagation poses significant challenges to urban sustainability, disrupting spatial accessibility. The cascading effect of traffic congestion propagation can cause large-scale disruptions to networks. Existing studies…