Mitigating Misinformation Spreading in Social Networks Via Edge Blocking

The wide adoption of social media platforms has brought about numerous benefits for communication and information sharing. However, it has also led to the rapid spread of misinformation, causing significant harm to individuals, communities, and society at large. Consequently, there has been a growing interest in devising efficient and effective strategies to contain the spread of misinformation. One popular countermeasure is blocking edges in the underlying network. We model the spread of misinformation using the classical Independent Cascade model and study the problem of minimizing the spread by blocking a given number of edges. We prove that this problem is computationally hard, but we propose an intuitive community-based algorithm, which aims to detect well-connected communities in the network and disconnect the inter-community edges. Our experiments on various real-world social networks demonstrate that the proposed algorithm significantly outperforms the prior methods, which mostly rely on centrality measures

XFlow: Benchmarking Flow Behaviors over Graphs

The occurrence of diffusion on a graph is a prevalent and significant phenomenon, as evidenced by the spread of rumors, influenza-like viruses, smart grid failures, and similar events. Comprehending the behaviors of flow is a formidable task, due to the intricate interplay between the distribution of seeds that initiate flow propagation, the propagation model, and the topology of the graph. The study of networks encompasses a diverse range of academic disciplines, including mathematics, physics, social science, and computer science. This interdisciplinary nature of network research is characterized by a high degree of specialization and compartmentalization, and the cooperation facilitated by them is inadequate. From a machine learning standpoint, there is a deficiency in a cohesive platform for assessing algorithms across various domains. One of the primary obstacles to current research in this field is the absence of a comprehensive curated benchmark suite to study the flow behaviors under network scenarios. To address this disparity, we propose the implementation of a novel benchmark suite that encompasses a variety of tasks, baseline models, graph datasets, and evaluation tools. In addition, we present a comprehensive analytical framework that offers a generalized approach to numerous flow-related tasks across diverse domains, serving as a blueprint and roadmap. Drawing upon the outcomes of our empirical investigation, we analyze the advantages and disadvantages of current foundational models, and we underscore potential avenues for further study. The datasets, code, and baseline models have been made available for the public at: https://github.com/XGraphing/XFlo

Limiting concept spread in environments with interacting concepts

The propagation of concepts in a population of agents is a form of influence spread, which can be modelled as a cascade from an initial set of individuals. In real-world environments there may be many concepts spreading and interacting. Previous work does not consider utilising concept interactions to limit the spread of a concept. In this paper we present a method for limiting concept spread, in environments where concepts interact and do not block others from spreading. We define a model that allows for the interactions between any number of concepts to be represented and, using this model, develop a solution to the influence limitation problem, which aims to minimise the spread of a target concept through the use of a secondary inhibiting concept. We present a heuristic, called maximum probable gain, and compare its performance to established heuristics for manipulating influence spread in both simulated smallworld networks and real-world networks

Talking Politics on Facebook: Network Centrality and Political Discussion Practices in Social Media

This study examines the relationship between political discussion on Facebook and social network location. It uses a survey name generator to map friendship ties between students at a university and to calculate their centralities in that network. Social connectedness in the university network positively predicts more frequent political discussion on Facebook. But in political discussions, better connected individuals do not capitalize equally on the potential influence that stems from their more central network locations. Popular individuals who have more direct connections to other network members discuss politics more often but in politically safer interactions that minimize social risk, preferring more engaged discussion with like-minded others and editing their privacy settings to guard their political disclosures. Gatekeepers who facilitate connections between more pairs of otherwise disconnected network members also discuss politics more frequently, but are more likely to engage in risk-tolerant discussion practices such as posting political updates or attempting political persuasion. These novel findings on social connectedness extend research on offline political discussion into the social media sphere, and suggest that as social network research proliferates, analysts should consider how various types of network location shape political behavior

Inefficiencies in Digital Advertising Markets

Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research

Complexity of Government response to Covid-19 pandemic: A perspective of coupled dynamics on information heterogeneity and epidemic outbreak

This study aims at modeling the universal failure in preventing the outbreak of COVID-19 via real-world data from the perspective of complexity and network science. Through formalizing information heterogeneity and government intervention in the coupled dynamics of epidemic and infodemic spreading; first, we find that information heterogeneity and its induced variation in human responses significantly increase the complexity of the government intervention decision. The complexity results in a dilemma between the socially optimal intervention that is risky for the government and the privately optimal intervention that is safer for the government but harmful to the social welfare. Second, via counterfactual analysis against the COVID-19 crisis in Wuhan, 2020, we find that the intervention dilemma becomes even worse if the initial decision time and the decision horizon vary. In the short horizon, both socially and privately optimal interventions agree with each other and require blocking the spread of all COVID-19-related information, leading to a negligible infection ratio 30 days after the initial reporting time. However, if the time horizon is prolonged to 180 days, only the privately optimal intervention requires information blocking, which would induce a catastrophically higher infection ratio than that in the counter-factual world where the socially optimal intervention encourages early-stage information spread. These findings contribute to the literature by revealing the complexity incurred by the coupled infodemic-epidemic dynamics and information heterogeneity to the governmental intervention decision, which also sheds insight into the design of an effective early warning system against the epidemic crisis in the future.Comment: This version contains the full-resolution figures for the paper DOI: 10.1007/s11071-023-08427-