Empirical analysis of individual popularity and activity on an online music service system

Quantitative understanding of human behaviors supplies basic comprehension of the dynamics of many socio-economic systems. Based on the log data of an online music service system, we investigate the statistical characteristics of individual activity and popularity, and find that the distributions of both of them follow a stretched exponential form which interpolates between exponential and power law distribution. We also study the human dynamics on the online system and find that the distribution of interevent time between two consecutive listenings of music shows the fat tail feature. Besides, with the reduction of user activity the fat tail becomes more and more irregular, indicating different behavior patterns for users with diverse activities. The research results may shed some light on the in-depth understanding of collective behaviors in socio-economic systems.     

A Class of Rumor Spreading Models with Population Dynamics

Based on the characteristics of rumor spreading in online social networks, this paper proposes a new rumor spreading model. This is an improved SIS rumor spreading model in online social networks that combines the transmission dynamics and population dynamics with consideration of the impact of both of the changing number of online social network users and different levels of user activity. We numerically simulate the rumor spreading process. The results of numerical simulation show that the improved SIS model can successfully characterize the rumor spreading behavior in online social networks. We also give the effective strategies of curbing the rumor spreading in online social networks.     

Synchronization in complex networks

Synchronization processes in populations of locally interacting elements are the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understanding synchronization phenomena in natural systems now take advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also take an overview of the new emergent features coming out from the interplay between the structure and the function of the underlying patterns of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.     

Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish Presidential election as a case study

Transmitting messages in the most efficient way as possible has always been one of politicians' main concerns during electoral processes. Due to the rapidly growing number of users, online social networks have become ideal platforms for politicians to interact with their potential voters. Exploiting the available potential of these tools to maximize their influence over voters is one of politicians' actual challenges. To step in this direction, we have analyzed the user activity in the online social network Twitter, during the 2011 Spanish Presidential electoral process, and found that such activity is correlated with the election results. We introduce a new measure to study political sentiment in Twitter, which we call the relative support. We have also characterized user behavior by analyzing the structural and dynamical patterns of the complex networks emergent from the mention and retweet networks. Our results suggest that the collective attention is driven by a very small fraction of users. Furthermore, we have analyzed the interactions taking place among politicians, observing a lack of debate. Finally, we develop a network growth model to reproduce the interactions taking place among politicians.     

Self-organized patterns and traffic flow in Colonies of organisms: from bacteria and social insects to vertebrates

Flocks of birds and schools of fish are familiar examples of spatial patterns formed by living organisms. In contrast to the patterns on the skins of, say, zebras and giraffes, the patterns of our interest are transient although different patterns change over different timescales. The aesthetic beauty of these patterns has attracted the attention of poets and philosophers for centuries. Scientists from various disciplines, however, are in search of common underlying principles that give rise to the transient patterns in colonies of organisms. Such patterns are observed not only in colonies of organisms as simple as single-cell bacteria, but also in social insects like ants and termites. They are also observed in colonies of vertebrates as complex as birds and fish, and in human societies. In recent years, physicists have utilized the framework of statistical physics to understand these patterns. In this article, we present an overview emphasizing the common trends that rely on theoretical modeling of these systems using the so-called agent-based Lagrangian approach.     

Influence Cascades: Entropy-Based Characterization of Behavioral Influence Patterns in Social Media

Influence cascades are typically analyzed using a single metric approach, i.e., all influence is measured using one number. However, social influence is not monolithic; different users exercise different influences in different ways, and influence is correlated with the user and content-specific attributes. One such attribute could be whether the action is an initiation of a new post, a contribution to a post, or a sharing of an existing post. In this paper, we present a novel method for tracking these influence relationships over time, which we call influence cascades, and present a visualization technique to better understand these cascades. We investigate these influence patterns within and across online social media platforms using empirical data and comparing to a scale-free network as a null model. Our results show that characteristics of influence cascades and patterns of influence are, in fact, affected by the platform and the community of the users.     

Combining content and relation analysis for recommendation in social tagging systems

Social tagging is one of the most important ways to organize and index online resources. Recommendation in social tagging systems, e.g. tag recommendation, item recommendation and user recommendation, is used to improve the quality of tags and to ease the tagging or searching process. Existing works usually provide recommendations by analyzing relation information in social tagging systems, suffering a lot from the over sparse problem. These approaches ignore information contained in the content of resources, which we believe should be considered to improve recommendation quality and to deal with the over sparse problem. In this paper we propose a recommendation approach for social tagging systems that combines content and relation analysis in a single model. By modeling the generating process of social tagging systems in a latent Dirichlet allocation approach, we build a fully generative model for social tagging, leverage it to estimate the relation between users, tags and resources and achieve tag, item and user recommendation tasks. The model is evaluated using a CiteULike data snapshot, and results show improvements in metrics for various recommendation tasks.     

Growth instabilities of vapor deposited films: atomic size versus deflection effect

Two previously suggested, physically distinct mechanisms for a growth instability of vapor deposited films, the finite atomic size effect and the particle deflection effect due to interatomic attraction, are reconsidered, further analyzed, and compared. We substantiate why the instability caused by interatomic attraction must be considered as the truly underlying instability mechanism. We demonstrate that aspects of the structure zone model of Movchan and Demchishin can also be consistently explained using the growth instability induced by particle deflection instead of the instability arising from the atomic size effect. Most significantly we show that, for vapor deposited amorphous Zr₆₅Al_7.5Cu_27.5-films, the growth instability due to the atomic size effect cannot be present. Received 12 December 2001 Published online 6 June 2002     

A universal power law and proportionate change process characterize the evolution of metabolic networks

Biological and social systems have been found to possess a non-trivial underlying network structure of interacting components. An important current question concerns the nature of the evolutionary processes that have led to the observed structural patterns dynamically. By comparing the metabolic networks of evolutionarily closeby as well distant species, we present results on the evolution of these networks over short as well as long time scales. We observe that the amount of change in the reaction set of a metabolite across different species is proportional to the degree of the metabolite, thus providing empirical evidence for a `proportionate change' mechanism. We find that this evolutionary process is characterized by a power law with a universal exponent that is independent of the pair of species compared.     

Human behavior in online social systems

We present and study data concerning human behavior in four online social systems: (i) an Internet community of friends of over 10⁷ people, (ii) a music community website with over 10⁶ users, (iii) a gamers’ community server with over 5 × 10⁶ users and (iv) a booklovers’ website with over 2.5 × 10⁵ users. The purpose of those systems is different; however, their properties are very similar. We have found that the distribution of human activity (e.g., the sum of books read or songs played) has the form of a power law. Moreover, the relationship between human activity and time has a power-law form, too. We present a simple interest-driven model of the evolution of such systems which explains the emergence of two scaling regimes.     

Trail formation and condensation of trajectories in a direct walker model with feedback

A directed walker model with external memory is studied by numerical simulations and statistical approaches. The structure of the trail systems depends strongly on the microscopic realization of the feedback mechanism and on the general repulsive or attractive interaction between different paths. Especially, we find nonergodic behavior for kinetic attraction and an ergodic one for repulsive interaction. The strong attraction regime shows a pronounced condensation of trajectories to one common path.     

Users structure and behavior on an online social network during a political protest

Over the past years, new technologies and specially online social networks have penetrated into the world’s population at an accelerated pace. In this paper we analyze collected data from the web application Twitter, in order to describe the structure and dynamics of the emergent social networks, based on complexity science. We focused on a Venezuelan protest that took place exclusively by Twitter during December, 2010. We found a community structure with highly connected hubs and three different kinds of user behavior that determine the information flow dynamics. We noticed that even though online social networks appear to be a pure social environment, traditional media still holds loads of influence inside the network.     

Fluctuations in Wikipedia access-rate and edit-event data

Internet-based social networks often reflect extreme events in nature and society by drastic increases in user activity. We study and compare the dynamics of the two major complex processes necessary for information spread via the online encyclopedia ‘Wikipedia’, i.e., article editing (information upload) and article access (information viewing) based on article edit-event time series and (hourly) user access-rate time series for all articles. Daily and weekly activity patterns occur in addition to fluctuations and bursting activity. The bursts (i.e., significant increases in activity for an extended period of time) are characterized by a power-law distribution of durations of increases and decreases. For describing the recurrence and clustering of bursts we investigate the statistics of the return intervals between them. We find stretched exponential distributions of return intervals in access-rate time series, while edit-event time series yield simple exponential distributions. To characterize the fluctuation behavior we apply detrended fluctuation analysis (DFA), finding that most article access-rate time series are characterized by strong long-term correlations with fluctuation exponents α≈0.9$\alpha \approx 0.9$. The results indicate significant differences in the dynamics of information upload and access and help in understanding the complex process of collecting, processing, validating, and distributing information in self-organized social networks.     

SEIR Model of Rumor Spreading in Online Social Network with Varying Total Population Size

Based on the infectious disease model with disease latency, this paper proposes a new model for the rumor spreading process in online social network. In this paper what we establish an SEIR rumor spreading model to describe the online social network with varying total number of users and user deactivation rate. We calculate the exact equilibrium points and reproduction number for this model. Furthermore, we perform the rumor spreading process in the online social network with increasing population size based on the original real world Facebook network. The simulation results indicate that the SEIR model of rumor spreading in online social network with changing total number of users can accurately reveal the inherent characteristics of rumor spreading process in online social network.     

Statistics of co-occurring keywords in confined text messages on Twitter

Online social media such as the micro-blogging site Twitter has become a rich source of real-time data on online human behaviors. Here we analyze the occurrence and co-occurrence frequency of keywords in user posts on Twitter. From the occurrence rate of major international brand names, we provide examples of predictions of brand-user behaviors. From the co-occurrence rates, we further analyze the user-perceived relationships between international brand names and construct the corresponding relationship networks. In general the user activity on Twitter is highly intermittent and we show that the occurrence rate of brand names forms a highly correlated time signal.     

Responses of Hodgkin-Huxley Neuronal Systems to Spike-Train Inputs

We investigate responses of the Hodgkin-Huxley globally neuronal systems to periodic spike-train inputs. The firing activities of the neuronal networks show different rhythmic patterns for different parameters. These rhyth- mic patterns can be used to explain cycles of firing in real brain. These activity patterns, average activity and coherence measure are affected by two quantities such as the percentage of excitatory couplings and stimulus intensity, in which the percentage of excitatory couplings is more important than stimulus intensity since the transition phenomenon of average activity comes about.     

Effects of saving and spending patterns on holding time distribution

The effects of saving and spending patterns on holding time distribution of money are investigated based on the ideal gas-like models. We show the steady-state distribution obeys an exponential law when the saving factor is set uniformly, and a power law when the saving factor is set diversely. The power distribution can also be obtained by proposing a new model where the preferential spending behavior is considered. The association of the distribution with the probability of money to be exchanged has also been discussed.Received: 4 September 2003, Published online: 19 November 2003PACS: 89.65.Gh Economics; econophysics, financial markets, business and management - 87.23.Ge Dynamics of social systems - 05.10.-a Computational methods in statistical physics and nonlinear dynamics - 02.50.-r Probability theory, stochastic processes, and statistics     

Eden growth model for aggregation of charged particles

The stochastic Eden model of charged particles aggregation in two-dimensional systems is presented. This model is governed by the following two parameters: screening length of electrostatic interaction, , and short-range attraction energy, E. Different patterns of finite and infinite aggregates are observed. They are of the following morphology types: linear or linear with bending, worm-like, DBM (dense-branching morphology), DBM with nucleus, and compact Eden-like. The transition between the different modes of growth is studied and phase diagram of the growth structures is obtained in co-ordinates. The detailed aggregate structure analysis, including analysis of their scaling properties, is presented. The scheme of the internal inhomogeneous structure of aggregates is proposed. Received 2 September 1998 and Received in final form 15 January 1999     

基于复杂网络理论的微博用户关系网络演化模型研究

微博给人们提供便利的同时也产生了较大的负面影响.为获取微博谣言的传播规律,进而采取有效措施防控其传播,本文基于复杂网络理论研究微博用户关系网络的内部特征,提出一种微博用户关系网络演化模型,借助于平均场理论,分析该演化模型的拓扑统计特性,以及谣言在该演化模型上的传播动力学行为.理论分析和仿真实验表明,由该模型演化生成的微博用户关系网络具有无标度特性.度分布指数不仅与反向连接概率有关,而且还取决于节点的吸引度分布.研究还发现,与指数分布和均匀分布相比,当节点吸引度满足幂律分布时,稳态时的谣言传播程度较大.此外,随着反向连接概率或节点初始连边数量的增加,谣言爆发的概率以及网络中最终接受谣言的节点数量都会明显增大.