Cryptocurrency network analysis consists of applying the tools and methods of social network analysis to transactional data issued from cryptocurrencies. The main difference with most online social networks is that users do not exchange textual content but instead value -- in systems designed mainly as cryptocurrency, such as Bitcoin -- or digital items and services in more permissive systems based on smart contracts such as Ethereum.
This study presents a dual-mode interface design concept for social media platforms aimed at reducing social comparison in health-related content among Korean MZ (Millennials and Gen-Z) users. The proposed "Inspiration" and "Reality" modes allow users to toggle between curated, idealized posts and more realistic, candid content. This approach aims to alleviate negative psychological effects, such as decreased self-esteem and body dissatisfaction. The pre-study outlines the design framework and discusses potential implications for user satisfaction, perceived authenticity, and mental well-being.
In this paper we explore the PageRank of temporal networks on both discrete and continuous time scales in the presence of personalization vectors that vary over time. Also the underlying interplay between the discrete and continuous settings arising from discretization is highlighted. Additionally, localization results that set bounds to the estimated influence of the personalization vector on the ranking of a particular node are given. The theoretical results are illustrated by means of some real and synthetic examples.
This article explores the importance of examining the solution space in community detection, highlighting its role in achieving reliable results when dealing with real-world problems. A Bayesian framework is used to estimate the stability of the solution space and classify it into categories Single, Dominant, Multiple, Sparse or Empty. By applying this approach to real-world networks, the study highlights the importance of considering multiple solutions rather than relying on a single partition. This ensures more reliable results and efficient use of computational resources in community detection analysis.
Together with illustrator Javi de Castro, María Hernández Martí published Que no, que no me muero in 2016, giving a fictional rendition of her breast cancer experience through the character of Lupe in this breast cancer graphic narrative. Hernández Martí and de Castro employ Lupe to break with certain myths and stereotypes surrounding mainstream breast cancer culture, specifically targeting what Barbara Ehrenreich’s terms its “relentless brightsiding”. Que no, que no me muero with its somber tone and colors proposes an alternate story of survivorship that rebels against the pathologizing gaze. Hernández Martí y de Castro’s text is not an uplifting or cheerful breast cancer graphic narrative, but one that is defiant and disobedient of mainstream breast cancer culture.
 RESUMEN
 Junto con el ilustrador Javi de Castro, María Hernández Martí publicó Que no, que no me muero en 2016, brindando una interpretación ficticia de su experiencia con el cáncer de mama a través del personaje de Lupe en esta narrativa gráfica sobre el cáncer de mama. Hernández Martí y de Castro emplean a Lupe para romper con ciertos mitos y estereotipos que rodean la cultura dominante sobre el cáncer de mama, y se enfocan específicamente en lo que Barbara Ehrenreich llama su "relentless brightsiding". Que no, que no me muero con su tono y colores sombríos propone un relato alternativo de supervivencia que se rebela contra la mirada patologizante. La obra de Hernández Martí y de Castro no es una narrativa gráfica edificante o alegre sobre el cáncer de mama, sino una que es desafiante y desobediente a la cultura dominante del cáncer de mama.
Community detection is an important problem in unsupervised learning. This paper proposes to solve a projection matrix approximation problem with an additional entrywise bounded constraint. Algorithmically, we introduce a new differentiable convex penalty and derive an alternating direction method of multipliers (ADMM) algorithm. Theoretically, we establish the convergence properties of the proposed algorithm. Numerical experiments demonstrate the superiority of our algorithm over its competitors, such as the semi-definite relaxation method and spectral clustering.
In this paper, we explore the nature of influence in a network. The concept of participant-invariant influence is derived from an influence matrix M specifically designed to explore this phenomenon. Through nonnegative matrix factorization approximation, we managed to extract a participant-invariant matrix H representing a shared pattern that all participants must obey. The acquired H is highly field-related and can be further utilized to cluster factual networks. Our discovery of the unveiled participant-independent influence within network dynamics opens up new avenues for further research on network behavior and its implications.
The propagation of a rumor (unverified information) on a social network is subject to several factors mainly related to the content of this information and especially to the behaviors (profiles) of the actors on this network that retransmit. This state of affairs may vary this propagation as the case may be, and this is what we call the depth of the rumor. This project is tackling this problem. From a real case of the spread of a rumor on Twitter, this contribution proposes an academic approach to quantify the depth of a rumor on social networks and this, for use and interpretation, by specialists concerned by the nature of this information and its auditor.
We applied complex network analysis to ~27,000 tweets posted by the 2016 presidential election's principal participants in the USA. We identified the stages of the election campaigns and the recurring topics addressed by the candidates. Finally, we revealed the leader-follower relationships between the candidates. We conclude that Secretary Hillary Clinton's Twitter performance was subordinate to that of Donald Trump, which may have been one factor that led to her electoral defeat.
We propose evolution rules of the multiagent network and determine statistical patterns in life cycle of agents - information messages. The main discussed statistical pattern is connected with the number of likes and reposts for a message. This distribution corresponds to Weibull distribution according to modeling results. We examine proposed model using the data from Twitter, an online social networking service.
Understanding fluctuation of users help stakeholders to provide a better support to communities. Below we present an experiment where we detect communities, their evolution and based on the data characterize users that stay, leave or join a community. Using a resulted feature set and logistic regression we operate with models of users that are joining and users that are staying in a community. In the related work we emphasize a number of features we will include in our future experiments to enhance train accuracy. This work represents a first from a series of experiments devoted to user fluctuation in communities.
Collaborative ranking is an emerging field of recommender systems that utilizes users' preference data rather than rating values. Unfortunately, neighbor-based collaborative ranking has gained little attention despite its more flexibility and justifiability. This paper proposes a novel framework, called SibRank that seeks to improve the state of the art neighbor-based collaborative ranking methods. SibRank represents users' preferences as a signed bipartite network, and finds similar users, through a novel personalized ranking algorithm in signed networks.
This work analyzes friendship network from a Massively Multiplayer Online Role-Playing Game (MMORPG). The network is based on data from a private server that was active from 2007 until 2011. The work conducts a standard analysis of the network and then divides players according to different groups based on their activity. Work checks how friendship network can be correlated to the clan (a self-organized group of players who often form a league and play on the same side in a match) network. Main part of the work is the recommendation method for players that are not part of any clan and it is based on communities of friendship network.
We introduce a novel algorithm of community detection that maintains dynamically a community structure of a large network that evolves with time. The algorithm maximizes the modularity index thanks to the construction of a randomized hierarchical clustering based on a Monte Carlo Markov Chain (MCMC) method. Interestingly, it could be seen as a dynamization of Louvain algorithm (see Blondel et Al, 2008) where the aggregation step is replaced by the hierarchical instrumental probability.
We present a descriptive analysis of Twitter data. Our study focuses on extracting the main side effects associated with HIV treatments. The crux of our work was the identification of personal tweets referring to HIV. We summarize our results in an infographic aimed at the general public. In addition, we present a measure of user sentiment based on hand-rated tweets.
This paper explores textual production in interaction networks, with special emphasis on its relation to topological measures. Four email lists were selected, in which measures were taken from the texts participants wrote. Peripheral, intermediary and hub sectors of these networks were observed to have discrepant linguistic elaborations. For completeness of exposition, correlation of textual and topological measures were observed for the entire network and for each connective sector. The formation of principal components is used for further insights of how measures are related.
The discriminant power of centrality indices for the degree, eigenvector, closeness, betweenness and subgraph centrality is analyzed. It is defined by the number of graphs for which the standard deviation of the centrality of its nodes is zero. On the basis of empirical analysis it is concluded that the subgraph centrality displays better discriminant power than the rest of centralities. We also propose some new conjectures about the types of graphs for which the subgraph centrality does not discriminate among nonequivalent nodes.
This work uses crowdsourcing to obtain motion capture data from video recordings. The data is obtained by information workers who click repeatedly to indicate body configurations in the frames of a video, resulting in a model of 2D structure over time. We discuss techniques to optimize the tracking task and strategies for maximizing accuracy and efficiency. We show visualizations of a variety of motions captured with our pipeline then apply reconstruction techniques to derive 3D structure.
Wikipedia and open source software projects have been cited as canonical examples of collectively intelligent organizations. Both organizations rely on large crowds of contributors to create knowledge goods. The crowds that emerge in both cases are not flat, but form a core-periphery network in which a few leaders contribute a large portion of the production and coordination work. This paper explores the social network processes by which leaders emerge from crowd-based organizations.