- Conceptual Dynamics in Social Networks: From Models to Data;
- Predictability of human movement;
- Testing of qubo formulations for kernel-peripheral partitioning on a quantum annealing furnace;
- Botometer 101: Computational Social Scientists' Attempts at Social Robots;
- Systematic evaluation of the fit quality of empirical network random block models;
Conceptual Dynamics in Social Networks: From Model to Data
原文标题： Opinion dynamics in social networks: From models to data
Address: http://arxiv.org/abs/2201.01322
作者: Antonio F. Peralta, János Kertész, Gerardo Iñiguez
Ideas are an integral part of how we see the world and each other. They shape collective action and play a role in democratic processes, normative evolution and cultural change. For decades, researchers in the social and natural sciences have tried to describe how shifts in personal perspective and social communication have led to archetypal states of public opinion, such as consensus and polarization. Here, we review some of the many contributions to the field, focusing on idealized models of opinion dynamics and attempting to validate them with observational data and controlled sociological experiments. By further narrowing the gap between models and data, these efforts can help us understand how to address current challenges that require large numbers of people to agree in complex situations such as economic inequality, climate change, and the continued fracture of socio-political landscapes.
The predictability state of human movement
Predictability states in human mobility
Address: http://arxiv.org/abs/2201.01376
( ): Diogo Pacheco, Marcos Oliveira, Zexun Chen, Hugo Barbosa, Brooke Foucault-Welles, Gourab Ghoshal, Ronaldo Menezes
Abstract: Space-time constraints combined with social structure have the potential to create mobility predictability for human mobility patterns. Thus, the predictability of human flow is non-monotonous and varies according to this spatio-temporal context. Here, we propose that the predictability of human flow is a state, not a static feature of the individual. First, we show that time (in the middle of the week) explains people's whereabouts better than the sequence of locations they visit. We then show that predictability depends not only on time, but also on the type of activity individuals engage in, thus determining the importance of the environment in human mobility.
Test the QUBO recipe for the kernel-peripheral partition on a quantum annealing furnace
原文标题： Testing a QUBO Formulation of Core-periphery Partitioning on a Quantum Annealer
Address: http://arxiv.org/abs/2201.01543
作者： Catherine F. Higham, Desmond J. Higham, Francesco Tudisco
Abstract: We propose a new kernel that quantifies the success of the core peripheral partitioning task of computing a directed network. Finding the optimal division associated can be expressed in the form of a quadratic unconstrained binary optimization (QUBO) problem, which can be applied to the most advanced quantum annealer. Therefore, we utilize the new objective function to (a) judge the performance of the quantum annealer, and (b) compare this method to the existing heuristic core-peripheral partitioning method. Quantum annealing is performed on commercially available D-Wave machines. Even if the underlying network is sparse, the QUBO problem involves a complete matrix. Therefore, we developed and tested a sparse version of the original QUBO, which added the usable problem dimension of the quantum annealer. Providing results on both synthetic and real datasets, we conclude that the QUBO/quantum annealing method provides benefits in optimizing this new number of interest.
Botometer 101: Computational Social Scientists' Social Robot Attempts
Botometer 101: Social bot practicum for computational social scientists
Address: http://arxiv.org/abs/2201.01608
Written by Kai-Cheng Yang, Emilio Ferrara, Filippo Menczer
Social bots have become an important part of online social media. Deceptive bots, in particular, can manipulate online discussions on important issues ranging from elections to public health, threatening a constructive exchange of information. Their ubiquity makes them an interesting research topic and requires researchers to handle them correctly when conducting research using social media data. Therefore, it is important for researchers to obtain reliable and easy-to-use robotic inspection tools. This article aims to provide readers who are new to programming and machine learning an introductory tutorial to Botometer, a common tool for robot detection on Twitter. We explain how Botometer works, the different ways users can access it, and provide a case study as a demonstration. Readers can use the case study code as a template for their own research. We also discussed recommended practices for using Botometer.
Systematic evaluation of the fit quality of the stochastic block model of an empirical network
原文标题： Systematic assessment of the quality of fit of the stochastic block model for empirical networks
Address: http://arxiv.org/abs/2201.01658
: Felipe Vaca-Ramírez, Tiago P. Peixoto
Abstract:We systematically analyze the quality of fit of a random block model (SBM) of 275 empirical networks spanning a wide range of domains and magnitudes. We employ posterior predictive model checks as a criterion for assessing the quality of fit, which involves comparing the network generated by the inferred model with the empirical network based on a set of network descriptors. We observed that SBM was able to provide an accurate description of most of the networks under consideration, but did not meet all modeling requirements. In particular, networks with large diameters and slow mixed random walks tend to be poorly described by SBMs. However, contrary to the usual assumptions, in many cases SBM can describe a network with a large number of triangles very well. We demonstrate that simple network descriptors can be used to assess whether an SBM can provide a sufficiently accurate representation that may point to possible model extensions that can systematically improve the expressiveness of such models.
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