社会与信息网络
Hidden interactions and components in complex systems-ranging from covert actors in terrorist networks to unobserved brain regions and molecular regulators-often manifest only through indirect behavioral signals. Inferring the underlying…
Money laundering enables organized crime by moving illicit funds into the legitimate economy. Although trillions of dollars are laundered each year, detection rates remain low because launderers evade oversight, confirmed cases are rare,…
Understanding dynamic systems like disease outbreaks, social influence, and information diffusion requires effective modeling of complex networks. Traditional evaluation methods for static networks often fall short when applied to temporal…
Many networked systems require a central authority to enforce a global configuration against local peer influence. We study influence dynamics on finite weighted directed graphs with a distinguished hub node and binary vertex states…
We study the intricate relationships within healthcare systems, focusing on interactions among doctors, departments, and hospitals. Leveraging an evolutionary graph framework, the proposed model emphasizes both intra-layer and inter-layer…
Recently, graph contrastive learning (GCL) has emerged as one of the optimal solutions for node-level and supervised tasks. However, for structure-related and unsupervised tasks such as graph clustering, current GCL algorithms face…
Vital nodes usually play a key role in complex networks. Uncovering these nodes is an important task in protecting the network, especially when the network suffers intentional attack. Many existing methods have not fully integrated the node…
Bridging content that brings together individuals with opposing viewpoints on social media remains elusive, overshadowed by echo chambers and toxic exchanges. We propose that algorithmic curation could surface such content by considering…
Homophily, the tendency of individuals to connect with others who share similar attributes, is a defining feature of social networks. Understanding how groups interact, both within and across, is crucial for uncovering the dynamics of…
Source localization in graph information propagation is essential for mitigating network disruptions, including misinformation spread, cyber threats, and infrastructure failures. Existing deep generative approaches face significant…
Complex time-varying networks are prominent models for a wide variety of spatiotemporal phenomena. The functioning of networks depends crucially on their connectivity, yet reliable techniques for learning communities in time-evolving…
Scholars studying organizations often work with multiple datasets lacking shared identifiers or covariates. In such situations, researchers usually use approximate string ("fuzzy") matching methods to combine datasets. String matching,…
I study Hodge decomposition (HodgeRank) for urban traffic flow on two graph representations: dense origin--destination (OD) graphs and road-segment networks. Reproducing the method of Aoki et al., we observe that on dense OD graphs the curl…
Online discussion platforms, such as community Q&A sites and forums, have become important hubs where academic conference authors share and seek information about the peer review process and outcomes. However, these discussions involve only…
As the field of artificial intelligence (AI) and machine learning (ML) continues to prioritize fairness and the concern for historically marginalized communities, the importance of intersectionality in AI research has gained significant…
We study properties of opinion formation on Wikipedia Ising Networks. Each Wikipedia article is represented as a node and links are formed by citations of one article to another generating a directed network of a given language edition with…
Predicting the emergence of future research collaborations between authors in academic social networks (SNs) is a very effective example that demonstrates the link prediction problem. This problem refers to predicting the potential…
An increasing amount of attention has been devoted to the problem of "toxic" or antisocial behavior on social media. In this paper we analyze such behavior at very large scales: we analyze toxicity over a 14-year time span on nearly 500…
Previous research has demonstrated that various properties of infectious diseases can be inferred from online search behaviour. In this work we use time series of online search query frequencies to gain insights about the prevalence of…
We present PoliTok-DE, a large-scale multimodal dataset (video, audio, images, text) of TikTok posts related to the 2024 Saxony state election in Germany. The corpus contains over 195,000 posts published between 01.07.2024 and 30.11.2024,…