We introduce a fast simulation technique for modeling epidemics on adaptive networks. Our rejection-based algorithm efficiently simulates the co-evolution of the network structure and the epidemic dynamics. We extend the classical SIS model by incorporating stochastic rules that allow for the association of susceptible nodes and the dissociation of infected nodes. The method outperforms standard baselines in terms of computational efficiency while revealing new emergent patterns in epidemic spread. Code is made available at github.com/GerritGr/icon.
Many organizations describe their processes as consensus-driven, but there is no consensus on the definition of consensus. Qualitative definitions of consensus prioritize social phenomena like "unity" that are not necessarily measurable. Quantitative definitions of consensus derive from numbers of votes and can be realized in software. When unity and cooperation become unobtainable for any reason, measuring consensus as a quantity (an amount of agreement) is a reasonable adaptation to alleviate gridlock and possibly avoid escalation of conflicts. This article investigates the metrology of social consensus.
In this paper, we propose a model enabling the creation of a social graph corresponding to real society. The procedure uses data describing the real social relations in the community, like marital status or number of kids. Results show the power-law behavior of the distribution of links and, typical for small worlds, the independence of the clustering coefficient on the size of the graph.
The chapter aims to explore the application of graph theory and networks in the recommendation domain, encompassing the mathematical models that form the foundation for the algorithms and recommendation systems developed based on them. The initial section of the chapter provides a concise overview of the recommendation field, with a particular focus on the types of recommendation solutions and the mathematical description of the problem. Subsequently, the chapter delves into the models and techniques for utilizing graphs and networks, along with illustrative examples of algorithms constructed on their basis.
The papers appraised the Nigeria Data Protection Regulation wit the aim of exposing the institutional propositions contained in the regulation. The aim of the paper is to address how the institutional propositions positions organizations in Nigeria to implement data protections regulations.
Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient spectral graph algorithms, and its development starts in September 2022. We have so far finished the component on local graph clustering, and this technical report presents a user's guide to STAG, showcase studies, and several technical considerations behind our development.
This paper aims to build an actionable framework for permissible online content moderation to combat misinformation. Often strong content moderation policies are invoked when misinformation causes harm. By adopting Mill's ethical framework, I show the complexities involved in permissible content moderation. The conclusion will be that, besides invoking the notion of harm, we should also introduce the idea of cognitive autonomy and adopt useful tools, such as cognitive nudging, to promote a healthier epistemic environment online.
In this paper, we propose a pubic opinion model with incorporation of asymmetric cognitive bias: confirmation bias and negativity bias. We then investigate the generic modeling guidance of capturing asymmetric confirmation bias and negativity bias. A numerical examples is provided to demonstrate the correctness of asymmetric cognitive bias model.
In this work, we analyse character networks in the cult TV show Twin Peaks. In the small-scale community network of Twin Peaks we discovered a new storytelling network phenomenon we called the Dale Cooper Effect, a phase transition in network structure. It is a sharp demarcation between the two statistically and topologically distinct networks of characters, where the point of demarcation is the protagonist himself (Special Agent Dale Cooper) introduced as a median character.
This work is devoted to the clustering of check-in sequences from a geosocial network. We used the mixture Markov chain process as a mathematical model for time-dependent types of data. For clustering, we adjusted the Expectation-Maximization (EM) algorithm. As a result, we obtained highly detailed communities (clusters) of users of the now defunct geosocial network, Weeplaces.
We perform a cross-platform analysis in which we study how does linking YouTube content on Reddit conspiracy forum impact language used in user comments on YouTube. Our findings show a slight change in user language in that it becomes more similar to language used on Reddit.
The global COVID-19 pandemic has led to the online proliferation of health-, political-, and conspiratorial-based misinformation. Understanding the reach and belief in this misinformation is vital to managing this crisis, as well as future crises. The results from our global survey finds a troubling reach of and belief in COVID-related misinformation, as well as a correlation with those that primarily consume news from social media, and, in the United States, a strong correlation with political leaning.
This paper introduces a temporal framework for detecting and clustering emergent and viral topics on social networks. Endogenous and exogenous influence on developing viral content is explored using a clustering method based on the a user's behavior on social network and a dataset from Twitter API. Results are discussed by introducing metrics such as popularity, burstiness, and relevance score. The results show clear distinction in characteristics of developed content by the two classes of users.
Hierarchical graph clustering is a common technique to reveal the multi-scale structure of complex networks. We propose a novel metric for assessing the quality of a hierarchical clustering. This metric reflects the ability to reconstruct the graph from the dendrogram, which encodes the hierarchy. The optimal representation of the graph defines a class of reducible linkages leading to regular dendrograms by greedy agglomerative clustering.
We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides excellent results as compared with state of the art community detection algorithms.
AbstractCs8‐xGa8‐ySi38+y (I) is obtained from a stoichiometric reaction of the elements in a CsCl flux, whereas K8Zn3.5Si42.5 (II) and Rb7.9Zn3.6Si42.4 (III) are prepared using a combined KBr or RbCl /Zn flux (alumina crucible sealed in stainless steel ampoule, 1173 K, 1 h; cooling to 923 K with a rate of 0.3 K/min).
In this paper, we study the relationship between the network-based inference method and global ranking method in personal recommendation. By some theoretical analysis, we prove that the recommendation result under the global ranking method is the limit of applying network-based inference method with infinity times.
Through mathematical analysis and simulations, online ratings and their impact on businesses are characterized through two parameters: an inherent and objective restaurant quality factor, and the accuracy of customers' gut feeling about a business. Within this model, it is found that online ratings are seldom accurate mainly because of the low or high accuracy in customers' gut feelings.
We describe a Bayesian model for social learning of a random variable in which agents might observe each other over a directed network. The outcomes produced are compared to those from a model in which observations occur randomly over a complete graph. In both cases we observe a nontrivial level of observation which maximizes learning, though individuals have strong incentive to defect from the societal optimum. The implications of such competition over information commons are discussed.
This paper presents an extensive data-based analysis of the non-profit democratic hospitality exchange service bewelcome.org. We hereby pursuit the goal of determining the factors influencing its growth. It also provides general insights on internet-based hospitality exchange services. The other investigated services are hospitalityclub.org and couchsurfing.org. Communities using the three services are interconnected -- comparing their data provides additional information.