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Search Results: 1 - 10 of 462094 matches for " Przemyslaw A. Grabowicz "
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Heterogeneity shapes groups growth in social online communities
Przemyslaw A. Grabowicz,Victor M. Eguiluz
Computer Science , 2011, DOI: 10.1209/0295-5075/97/28002
Abstract: Many complex systems are characterized by broad distributions capturing, for example, the size of firms, the population of cities or the degree distribution of complex networks. Typically this feature is explained by means of a preferential growth mechanism. Although heterogeneity is expected to play a role in the evolution it is usually not considered in the modeling probably due to a lack of empirical evidence on how it is distributed. We characterize the intrinsic heterogeneity of groups in an online community and then show that together with a simple linear growth and an inhomogeneous birth rate it explains the broad distribution of group members.
Opinion Dynamics with Hopfield Neural Networks
Dietrich Stauffer,Przemyslaw A. Grabowicz,Janusz A. Holyst
Physics , 2007,
Abstract: In Hopfield neural networks with up to 10^8 nodes we store two patterns through Hebb couplings. Then we start with a third random pattern which is supposed to evolve into one of the two stored patterns, simulating the cognitive process of associative memory leading to one of two possible opinions. With probability p each neuron independently, instead of following the Hopfield rule, takes over the corresponding value of another network, thus simulating how different people can convince each other. A consensus is achieved for high p.
Dynamics in online social networks
Przemyslaw A. Grabowicz,Jose J. Ramasco,Victor M. Eguiluz
Computer Science , 2012, DOI: 10.1007/978-1-4614-6729-8_1
Abstract: An increasing number of today's social interactions occurs using online social media as communication channels. Some online social networks have become extremely popular in the last decade. They differ among themselves in the character of the service they provide to online users. For instance, Facebook can be seen mainly as a platform for keeping in touch with close friends and relatives, Twitter is used to propagate and receive news, LinkedIn facilitates the maintenance of professional contacts, Flickr gathers amateurs and professionals of photography, etc. Albeit different, all these online platforms share an ingredient that pervades all their applications. There exists an underlying social network that allows their users to keep in touch with each other and helps to engage them in common activities or interactions leading to a better fulfillment of the service's purposes. This is the reason why these platforms share a good number of functionalities, e.g., personal communication channels, broadcasted status updates, easy one-step information sharing, news feeds exposing broadcasted content, etc. As a result, online social networks are an interesting field to study an online social behavior that seems to be generic among the different online services. Since at the bottom of these services lays a network of declared relations and the basic interactions in these platforms tend to be pairwise, a natural methodology for studying these systems is provided by network science. In this chapter we describe some of the results of research studies on the structure, dynamics and social activity in online social networks. We present them in the interdisciplinary context of network science, sociological studies and computer science.
Fast filtering and animation of large dynamic networks
Przemyslaw A. Grabowicz,Luca Maria Aiello,Filippo Menczer
Computer Science , 2013, DOI: 10.1140/epjds/s13688-014-0027-8
Abstract: Detecting and visualizing what are the most relevant changes in an evolving network is an open challenge in several domains. We present a fast algorithm that filters subsets of the strongest nodes and edges representing an evolving weighted graph and visualize it by either creating a movie, or by streaming it to an interactive network visualization tool. The algorithm is an approximation of exponential sliding time-window that scales linearly with the number of interactions. We compare the algorithm against rectangular and exponential sliding time-window methods. Our network filtering algorithm: i) captures persistent trends in the structure of dynamic weighted networks, ii) smoothens transitions between the snapshots of dynamic network, and iii) uses limited memory and processor time. The algorithm is publicly available as open-source software.
Social Features of Online Networks: The Strength of Intermediary Ties in Online Social Media
Przemyslaw A. Grabowicz, José J. Ramasco, Esteban Moro, Josep M. Pujol, Victor M. Eguiluz
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0029358
Abstract: An increasing fraction of today's social interactions occur using online social media as communication channels. Recent worldwide events, such as social movements in Spain or revolts in the Middle East, highlight their capacity to boost people's coordination. Online networks display in general a rich internal structure where users can choose among different types and intensity of interactions. Despite this, there are still open questions regarding the social value of online interactions. For example, the existence of users with millions of online friends sheds doubts on the relevance of these relations. In this work, we focus on Twitter, one of the most popular online social networks, and find that the network formed by the basic type of connections is organized in groups. The activity of the users conforms to the landscape determined by such groups. Furthermore, Twitter's distinction between different types of interactions allows us to establish a parallelism between online and offline social networks: personal interactions are more likely to occur on internal links to the groups (the weakness of strong ties); events transmitting new information go preferentially through links connecting different groups (the strength of weak ties) or even more through links connecting to users belonging to several groups that act as brokers (the strength of intermediary ties).
Entangling Mobility and Interactions in Social Media
Przemyslaw A. Grabowicz, José J. Ramasco, Bruno Gon?alves, Víctor M. Eguíluz
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0092196
Abstract: Daily interactions naturally define social circles. Individuals tend to be friends with the people they spend time with and they choose to spend time with their friends, inextricably entangling physical location and social relationships. As a result, it is possible to predict not only someone’s location from their friends’ locations but also friendship from spatial and temporal co-occurrence. While several models have been developed to separately describe mobility and the evolution of social networks, there is a lack of studies coupling social interactions and mobility. In this work, we introduce a model that bridges this gap by explicitly considering the feedback of mobility on the formation of social ties. Data coming from three online social networks (Twitter, Gowalla and Brightkite) is used for validation. Our model reproduces various topological and physical properties of the networks not captured by models uncoupling mobility and social interactions such as: i) the total size of the connected components, ii) the distance distribution between connected users, iii) the dependence of the reciprocity on the distance, iv) the variation of the social overlap and the clustering with the distance. Besides numerical simulations, a mean-field approach is also used to study analytically the main statistical features of the networks generated by a simplified version of our model. The robustness of the results to changes in the model parameters is explored, finding that a balance between friend visits and long-range random connections is essential to reproduce the geographical features of the empirical networks.
Distinguishing Topical and Social Groups Based on Common Identity and Bond Theory
Przemyslaw A. Grabowicz,Luca Maria Aiello,Víctor M. Eguíluz,Alejandro Jaimes
Computer Science , 2013, DOI: 10.1145/2433396.2433475
Abstract: Social groups play a crucial role in social media platforms because they form the basis for user participation and engagement. Groups are created explicitly by members of the community, but also form organically as members interact. Due to their importance, they have been studied widely (e.g., community detection, evolution, activity, etc.). One of the key questions for understanding how such groups evolve is whether there are different types of groups and how they differ. In Sociology, theories have been proposed to help explain how such groups form. In particular, the common identity and common bond theory states that people join groups based on identity (i.e., interest in the topics discussed) or bond attachment (i.e., social relationships). The theory has been applied qualitatively to small groups to classify them as either topical or social. We use the identity and bond theory to define a set of features to classify groups into those two categories. Using a dataset from Flickr, we extract user-defined groups and automatically-detected groups, obtained from a community detection algorithm. We discuss the process of manual labeling of groups into social or topical and present results of predicting the group label based on the defined features. We directly validate the predictions of the theory showing that the metrics are able to forecast the group type with high accuracy. In addition, we present a comparison between declared and detected groups along topicality and sociality dimensions.
An experimental study of opinion influenceability
Przemyslaw A. Grabowicz,Francisco Romero-Ferrero,Theo Lins,Gonzalo G. de Polavieja,Fabrício Benevenuto,Krishna P. Gummadi
Computer Science , 2015,
Abstract: Humans, like many other animal species, often make choices under social influence. Experiments in ants and fishes have shown that individuals choose according to estimations of which option to take given private and social information. Principled approaches based on probabilistic estimations by agents give mathematical formulas explaining experiments in these species. Here we test whether the same principled approaches can explain social influence in humans. We conduct a large online field experiment in which we measure opinion influenced by public comments about short movies in the most popular video-sharing website. We show that the basic principles of social influence in other species also apply to humans, with the added complexity that humans are heterogenous. We infer influenceability of each participant of the experiment, finding that individuals prone to social influence tend to agree with social feedback, read less comments, and are less educated than the persons who resist influence. We believe that our results will help to build a mathematical theory of social influence rooted in probabilistic reasoning that show commonalities and differences between humans and other species.
MMpred: functional miRNA – mRNA interaction analyses by miRNA expression prediction
Stempor Przemyslaw A,Cauchi Michael,Wilson Paul
BMC Genomics , 2012, DOI: 10.1186/1471-2164-13-620
Abstract: Background MicroRNA (miRNA) directed gene repression is an important mechanism of posttranscriptional regulation. Comprehensive analyses of how microRNA influence biological processes requires paired miRNA-mRNA expression datasets. However, a review of both GEO and ArrayExpress repositories revealed few such datasets, which was in stark contrast to the large number of messenger RNA (mRNA) only datasets. It is of interest that numerous primary miRNAs (precursors of microRNA) are known to be co-expressed with coding genes (host genes). Results We developed a miRNA-mRNA interaction analyses pipeline. The proposed solution is based on two miRNA expression prediction methods – a scaling function and a linear model. Additionally, miRNA-mRNA anti-correlation analyses are used to determine the most probable miRNA gene targets (i.e. the differentially expressed genes under the influence of up- or down-regulated microRNA). Both the consistency and accuracy of the prediction method is ensured by the application of stringent statistical methods. Finally, the predicted targets are subjected to functional enrichment analyses including GO, KEGG and DO, to better understand the predicted interactions. Conclusions The MMpred pipeline requires only mRNA expression data as input and is independent of third party miRNA target prediction methods. The method passed extensive numerical validation based on the binding energy between the mature miRNA and 3’ UTR region of the target gene. We report that MMpred is capable of generating results similar to that obtained using paired datasets. For the reported test cases we generated consistent output and predicted biological relationships that will help formulate further testable hypotheses.
On the character of hydrodynamic gradient expansion in gauge theory plasma
Heller, Michal P.;Janik, Romuald A.;Witaszczyk, Przemyslaw
High Energy Physics - Phenomenology , 2013,
Abstract: We utilize the fluid-gravity duality to investigate large order behavior of hydrodynamic gradient expansion of the dynamics of a gauge theory plasma system. This corresponds to the inclusion of dissipative terms and transport coefficients of very high order. Using the dual gravity description, we calculate numerically the form of the stress tensor for a boost-invariant flow in a hydrodynamic expansion up to terms with 240 derivatives. We observe a factorial growth of gradient contributions at large orders, which indicates a zero radius of convergence of the hydrodynamic series. Furthermore, we identify the leading singularity in the Borel transform of the hydrodynamic energy density with the lowest non-hydrodynamic excitation corresponding to a `non-hydrodynamic' quasinormal mode on the gravity side.
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