The bursty dynamics of the Twitter information network

  • Abstract
  • References
  • Citations
  • Similar Papers
Abstract
Translate article icon Translate Article Star icon
Take notes icon Take Notes

In online social media systems users are not only posting, consuming, and resharing content, but also creating new and destroying existing connections in the underlying social network. While each of these two types of dynamics has individually been studied in the past, much less is known about the connection between the two. How does user information posting and seeking behavior interact with the evolution of the underlying social network structure? Here, we study ways in which network structure reacts to users posting and sharing content. We examine the complete dynamics of the Twitter information network, where users post and reshare information while they also create and destroy connections. We find that the dynamics of network structure can be characterized by steady rates of change, interrupted by sudden bursts. Information diffusion in the form of cascades of post re-sharing often creates such sudden bursts of new connections, which significantly change users' local network structure. We also explore the effect of the information content on the dynamics of the network and find evidence that the appearance of new topics and real-world events can lead to significant changes in edge creations and deletions. Lastly, we develop a model that quantifies the dynamics of the network and the occurrence of these bursts as a function of the information spreading through the network. The model can successfully predict which information diffusion events will lead to bursts in network dynamics.

CitationsShowing 10 of 155 papers
  • Open Access Icon
  • Research Article
  • 10.1527/tjsai.jwein-k
Cooperation-dominant Situations in SNS-norms Game on Complex and Facebook Networks
  • Jan 1, 2015
  • Transactions of the Japanese Society for Artificial Intelligence
  • Yuki Hirahara + 2 more

We propose an SNS-norms game to model behavioral strategies in social networking services (SNSs) and investigate the conditions required for the evolution of cooperation-dominant situations. SNSs such as Facebook and Google+ are indispensable social media for a variety of social communications ranging from personal chats to business and political campaigns, but we do not yet fully understand why they thrive and whether these currently popular SNSs will remain in the future. A number of studies have attempted to understand the conditions or mechanisms that keep social media thriving by using a meta-rewards game that is the dual form of a public goods game or by analyzing user roles. However, the meta-rewards game does not take into account the unique characteristics of current SNSs. Hence, in this work we propose an SNS-norms game that is an extension of Axelrod’s metanorms game, similar to meta-rewards games, but that considers the cost of commenting on an article and who is most likely to respond to it. We then experimentally investigated the conditions for a cooperation-dominant situation, by which we mean many users continuing to post articles on an SNS. Our results indicate that relatively large rewards compared to the cost of posting articles and comments are required to evolve cooperation-dominant situations, but optional responses with lower cost, such as “Like!” buttons, facilitate the evolution. This phenomenon is of interest because it is quite different from those shown in previous studies using meta-rewards games. We also confirmed the same phenomenon in an additional experiment using a network structure extracted from real-world SNS data.

  • Open Access Icon
  • PDF Download Icon
  • Research Article
  • Cite Count Icon 4
  • 10.1007/s13278-019-0576-8
Characterising and evaluating dynamic online communities from live microblogging user interactions
  • Jul 3, 2019
  • Social Network Analysis and Mining
  • Hugo Hromic + 1 more

Microblogging social media focuses on fast open real-time communication using short messages between users and their followers. These platforms generate large amounts of content, and community finding techniques are a suitable alternative for organising it. However, there is no clear agreement in the literature for a definition of user community for the microblogging use case, leading to unreliable ground-truth data and evaluation. In this work, we differentiate between functional and structural definitions of communities for microblogging. A functional community groups its users by a common independent social function, e.g. fans of the same football team, while in a structural community the members exclusively depend on their connectivity in a network, e.g. modularity. We build and characterise eight types of functional communities to be used as user-labelled ground-truth and five types of user interactions networks from Twitter. We then evaluate—in static and dynamic scenarios—thirteen popular structural community definitions using five different Twitter datasets, exploring their goodness and robustness for detecting the functional ground-truth under different perturbation strategies. Our results show that definitions based on internal connectivity, e.g. Triangle Participation Ratio, Fraction Over Median Degree or Conductance work best for the Twitter use case and are very robust. On the other hand, other scores such as Modularity are limited and do not perform well due to the sparsity and noise of microblogging. Furthermore, using user activity as basis to separate communities into active hotspots further improves the performance of community detection in microblogging.

  • Research Article
  • Cite Count Icon 39
  • 10.1016/j.neucom.2020.08.089
Information diffusion across cyber-physical-social systems in smart city: A survey
  • Jan 26, 2021
  • Neurocomputing
  • Xiaokang Zhou + 3 more

Information diffusion across cyber-physical-social systems in smart city: A survey

  • Conference Article
  • Cite Count Icon 5
  • 10.1145/2806416.2806559
DifRec
  • Oct 17, 2015
  • Hossein Vahabi + 3 more

Recommender systems used in current online social platforms make recommendations by only considering how relevant an item is to a specific user but they ignore the fact that, thanks to mechanisms like sharing or re-posting across the underlying social network, an item recommended to a user i propagates through the network and can reach another user j without needing to be explicitly recommended to j too. Overlooking this fact may lead to an inefficient use of the limited recommendation slots. These slots can instead be exploited more profitably by avoiding unnecessary duplicates and recommending other equally relevant items.

  • Open Access Icon
  • PDF Download Icon
  • Research Article
  • Cite Count Icon 1
  • 10.1007/s41109-019-0135-2
Impact of network density on cascade size and community growth
  • May 31, 2019
  • Applied Network Science
  • Kazumasa Oida

This paper addresses two critical questions related to (1) the co-evolutionary dynamics between information diffusion and network topology and (2) the relationship between viral content and social reinforcement. A recurrence relation model is developed to formulate the growth dynamics of a community centered on a dominant user using the tweet-retweet-follow (TRF) and exogenous link-creation events. This model illustrates several fundamental relations among parameters and quantities that are critical to answering the questions. The model reproduces social reinforcement and structural trapping effects, including empirical evidence that viral content does not require strong social reinforcement. In addition, the model demonstrates that the community growth rate is influenced not only by the rate of retweets but also by the network density of the community.

  • Research Article
  • Cite Count Icon 3
  • 10.1108/ijwis-03-2016-0012
Twitter user tagging method based on burst time series
  • Aug 15, 2016
  • International Journal of Web Information Systems
  • Shuhei Yamamoto + 3 more

Purpose Many Twitter users post tweets that are related to their particular interests. Users can also collect information by following other users. One approach clarifies user interests by tagging labels based on the users. A user tagging method is important to discover candidate users with similar interests. This paper aims to propose a new user tagging method using the posting time series data of the number of tweets. Design/methodology/approach Our hypothesis focuses on the relationship between a user’s interests and the posting times of tweets: as users have interests, they will post more tweets at the time when events occur compared with general times. The authors assume that hashtags are labeled tags to users and observe their occurrence counts in each timestamp. The authors extract burst timestamps using Kleinberg’s burst enumeration algorithm and estimate the burst levels. The authors manage the burst levels as term frequency in documents and calculate the score using typical methods such as cosine similarity, Naïve Bayes and term frequency (TF) in a document and inversed document frequency (IDF; TF-IDF). Findings From the sophisticated experimental evaluations, the authors demonstrate the high efficiency of the tagging method. Naïve Bayes and cosine similarity are particular suitable for the user tagging and tag score calculation tasks, respectively. Some users, whose hashtags were appropriately estimated by our methods, experienced higher the maximum value of the number of tweets than other users. Originality/value Many approaches estimate user interest based on the terms in tweets and apply such graph theory as following networks. The authors propose a new estimation method that uses the time series data of the number of tweets. The merits to estimating user interest using the time series data do not depend on language and can decrease the calculation costs compared with the above-mentioned approaches because the number of features is fewer.

  • Open Access Icon
  • Conference Article
  • Cite Count Icon 8
  • 10.1109/icde.2019.00102
Tracking Influential Nodes in Time-Decaying Dynamic Interaction Networks
  • Apr 1, 2019
  • Junzhou Zhao + 4 more

Identifying influential nodes that can jointly trigger the maximum influence spread in networks is a fundamental problem in many applications such as viral marketing, online advertising, and disease control. Most existing studies assume that social influence is static and they fail to capture the dynamics of influence in reality. In this work, we address the dynamic influence challenge by designing efficient streaming methods that can identify influential nodes from highly dynamic node interaction streams. We first propose a general time-decaying dynamic interaction network (TDN) model to model node interaction streams with the ability to smoothly discard outdated data. Based on the TDN model, we design three algorithms, i.e., SieveADN, BasicReduction, and HistApprox. SieveADN identifies influential nodes from a special kind of TDNs with efficiency. BasicReduction uses SieveADN as a basic building block to identify influential nodes from general TDNs. HistApprox significantly improves the efficiency of BasicReduction. More importantly, we theoretically show that all three algorithms enjoy constant factor approximation guarantees. Experiments conducted on various real interaction datasets demonstrate that our approach finds near-optimal solutions with speed at least $5$ to $15$ times faster than baseline methods.

  • Open Access Icon
  • PDF Download Icon
  • Research Article
  • Cite Count Icon 1
  • 10.5539/cis.v10n3p10
A Successful Advertising Strategy over Twitter
  • Jul 10, 2017
  • Computer and Information Science
  • Kyota Okubo + 1 more

Large information cascades over online social networks have attracted a great deal of attention. The life times of most cascades are quite short, whereas recent advertising campaigns sometimes generate long-lived ones by employing effective dissemination strategies (instant-win, reminders, etc.). This paper reports one such campaign, YOGUR STAND, on the Twitter network in Japan. The data analysis shows that the campaign popularity has two interesting features. (1) It shows elastic behavior in that the impact of the destructive earthquake on the popularity was quite temporary. (2) It exhibits stationary behavior in that the campaign account gained approximately 2,000 new followers every day.The analysis also demonstrates that there were communities in the campaign participants. The campaign was successful because about 2.4 million Twitter users received the campaign retweets every day and 10-15% of them received the retweets for the first time.

  • Book Chapter
  • 10.4018/978-1-7998-0417-8.ch040
Sentiment Based Information Diffusion in Online Social Networks
  • Jan 1, 2020
  • Mohammad Ahsan + 3 more

This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.

  • Research Article
  • Cite Count Icon 3
  • 10.1002/asmb.2495
The journey to engaged customer community: Evidential social CRM maturity model in Twitter
  • Dec 9, 2019
  • Applied Stochastic Models in Business and Industry
  • Inbal Yahav + 2 more

Abstract We investigate the company use of Twitter as a platform for social customer relations management (SCRM) to find that message type and follower growth follow an identifiable maturity model. Studying longitudinal Twitter data from 73 Standard and Poor's companies, we find that companies map into one of two distinct maturity stages as reflected in the content of company‐generated messages. The first maturity stage consists of branding, where the focus is on customer acquisition up to a certain volume, at the expense of their engagement. The second is social care, where the focus is on retaining customers and increased engagement. We further find that companies have relatively little control over their maturity stage and that investing in social care in the first stage, aiming to increase engagement, is not effective. Proper understanding and recognition of the Twitter SCRM maturity model will allow companies to assess their methods and processes according to SCRM best practices.

Similar Papers
  • Research Article
  • Cite Count Icon 24
  • 10.1007/s00521-004-0429-9
On three intelligent systems: dynamic neural, fuzzy, and wavelet networks for training trajectory
  • Sep 18, 2004
  • Neural Computing and Applications
  • Yasar Becerikli

Intelligent systems cover a wide range of technologies related to hard sciences, such as modeling and control theory, and soft sciences, such as the artificial intelligence (AI). Intelligent systems, including neural networks (NNs), fuzzy logic (FL), and wavelet techniques, utilize the concepts of biological systems and human cognitive capabilities. These three systems have been recognized as a robust and attractive alternative to the some of the classical modeling and control methods. The application of classical NNs, FL, and wavelet technology to dynamic system modeling and control has been constrained by the non-dynamic nature of their popular architectures. The major drawbacks of these architectures are the curse of dimensionality, such as the requirement of too many parameters in NNs, the use of large rule bases in FL, the large number of wavelets, and the long training times, etc. These problems can be overcome with dynamic network structures, referred to as dynamic neural networks (DNNs), dynamic fuzzy networks (DFNs), and dynamic wavelet networks (DWNs), which have unconstrained connectivity and dynamic neural, fuzzy, and wavelet processing units, called “neurons”, “feurons”, and “wavelons”, respectively. The structure of dynamic networks are based on Hopfield networks. Here, we present a comparative study of DNNs, DFNs, and DWNs for non-linear dynamical system modeling. All three dynamic networks have a lag dynamic, an activation function, and interconnection weights. The network weights are adjusted using fast training (optimization) algorithms (quasi-Newton methods). Also, it has been shown that all dynamic networks can be effectively used in non-linear system modeling, and that DWNs result in the best capacity. But all networks have non-linearity properties in non-linear systems. In this study, all dynamic networks are considered as a non-linear optimization with dynamic equality constraints for non-linear system modeling. They encapsulate and generalize the target trajectories. The adjoint theory, whose computational complexity is significantly less than the direct method, has been used in the training of the networks. The updating of weights (identification of network parameters) is based on Broyden–Fletcher–Goldfarb–Shanno method. First, phase portrait examples are given. From this, it has been shown that they have oscillatory and chaotic properties. A dynamical system with discrete events is modeled using the above network structure. There is a localization property at discrete event instants for time and frequency in this example.

  • PDF Download Icon
  • Abstract
  • Cite Count Icon 1
  • 10.1186/1471-2202-12-s1-p202
Effects of local structure of neuronal networks on spiking activity in silico
  • Jul 18, 2011
  • BMC Neuroscience
  • Tuomo Mäki-Marttunen + 3 more

The structure of the neuronal network, including synaptic connectivity, is the basis for information transfer in the network. Various graph-theoretic measures such as degree distribution, mean geodesic path length, clustering coefficient and motif distribution exist for analysing the structure of networks [1], and each of them captures only one perspective of the properties that are crucial regarding the activity in the network. In this work, we vary the local structure of neuronal networks and observe changes in their activity in silico, i.e. in simulations where the activity of single neurons and their interaction is modeled. The local structure is analysed through the occurrence of different motifs, i.e. different patterns of connectivity. The effect of motifs on network dynamics has been widely studied in different types of networks: from the stability point of view in networks with unspecified dynamics [2], in artificial neural networks [3], and from synchronization point of view in spiking neuronal networks [4]. Our work focuses on noise-driven neuronal networks, where the activity can be characterised by spike trains of neurons in the network, and particularly by the bursting behaviour of the network. To study the local structure of networks we consider the occurrences of three separate connectivity patterns: (1) the bidirectional edges, (2) the loops of three nodes, and (3) the feed-forward motifs of triples of nodes. Networks with one of these three local connectivity patterns promoted are generated – we abbreviate these networks (L1), (L2) and (L3). In addition, different distance-dependent networks are generated, including networks with ring topology (RT) and biologically plausible topology, obtained by the NETMORPH [5] simulator (NM). All networks except for NM have binomially distributed in-degree, as is the case with the random networks (RN) that are widely used in neuronal activity simulations. Small illustrations of these network structures are shown in Figure ​Figure1.1. Neuronal activity in these types of networks of size N=100 is simulated using the model presented in [6]. The simulations show a difference in the activity of these networks. Preliminary results indicate, that network bursts occur more frequently in distance dependent networks RT and NM, especially in RT. Accordingly, the overall spiking frequency is high in these networks, but also in L3 networks. Figure 1 Different network structures. Illustration of different networks used in the present work. The red arrows and balls represent characteristics of each network, which are the following: Bidirectional edges in L1, loops of length 3 in L2, feed-forward triples ...

  • Book Chapter
  • Cite Count Icon 1
  • 10.1007/978-3-319-92058-0_11
Sampling Community Structure in Dynamic Social Networks
  • Jan 1, 2018
  • Humphrey Mensah + 1 more

When studying dynamic networks, it is often of interest to understand how the community structure of the network changes. However, before studying the community structure of dynamic social networks, one must first collect appropriate network data. In this paper we present a network sampling technique to crawl the community structure of dynamic networks when there is a limitation on the number of nodes that can be queried. The process begins by obtaining a sample for the first time step. In subsequent time steps, the crawling process is guided by community structure discoveries made in the past. Experiments conducted on the proposed approach and certain baseline techniques reveal the proposed approach has at least 35% performance increase in cases when the total query budget is fixed over the entire period and at least 8% increase in cases when the query budget is fixed per time step.

  • PDF Download Icon
  • Research Article
  • Cite Count Icon 6
  • 10.3390/math11132979
Dynamic Complex Network, Exploring Differential Evolution Algorithms from Another Perspective
  • Jul 4, 2023
  • Mathematics
  • Yifei Yang + 4 more

Complex systems provide an opportunity to analyze the essence of phenomena by studying their intricate connections. The networks formed by these connections, known as complex networks, embody the underlying principles governing the system’s behavior. While complex networks have been previously applied in the field of evolutionary computation, prior studies have been limited in their ability to reach conclusive conclusions. Based on our investigations, we are against the notion that there is a direct link between the complex network structure of an algorithm and its performance, and we demonstrate this experimentally. In this paper, we address these limitations by analyzing the dynamic complex network structures of five algorithms across three different problems. By incorporating mathematical distributions utilized in prior research, we not only generate novel insights but also refine and challenge previous conclusions. Specifically, we introduce the biased Poisson distribution to describe the algorithm’s exploration capability and the biased power-law distribution to represent its exploitation potential during the convergence process. Our aim is to redirect research on the interplay between complex networks and evolutionary computation towards dynamic network structures, elucidating the essence of exploitation and exploration in the black-box optimization process of evolutionary algorithms via dynamic complex networks.

  • Research Article
  • Cite Count Icon 2
  • 10.1007/s11432-016-9070-4
Learning dynamic dependency network structure with time lag
  • Aug 30, 2017
  • Science China Information Sciences
  • Sizhen Du + 3 more

Characterizing and understanding the structure and the evolution of networks is an important problem for many different fields.While in the real-world networks, especially the spatial networks, the influence from one node to another tends to vary over both space and time due to the different space distances and propagation speeds between nodes. Thus the time lag plays an essential role in interpreting the temporal causal dependency among nodes and also brings a big challenge in network structure learning.However most of the previous researches aiming to learn the dynamic network structure only treat the time lag as a predefined constant, which may miss important information or include noisy information if the time lag is set too small or too large.In this paper, we propose a dynamic Bayesian model with adaptive lags (DBAL) which simultaneously integrates two usually separate tasks, i.e., learning the dynamic dependency network structure and estimating time lags, within one unified framework.Specifically, we propose a novel weight kernel approach for time series segmenting and sampling via leveraging samples from adjacent segments to avoid thesample scarcity. Besides, an effective Bayesian scheme cooperated with reversible jump Markov chainMonte Carlo (RJMCMC) and expectation propagation (EP) algorithm is proposed for parameter inference.Extensive empirical evaluations are conducted on both synthetic and two real-world datasets, and the results demonstrate that our proposed model is superior to the traditional methods in learning the network structure and the temporal dependency.

  • Research Article
  • Cite Count Icon 24
  • 10.1016/j.artint.2015.12.001
Exploiting local and repeated structure in Dynamic Bayesian Networks
  • Dec 10, 2015
  • Artificial Intelligence
  • Jonas Vlasselaer + 3 more

Exploiting local and repeated structure in Dynamic Bayesian Networks

  • Research Article
  • 10.5075/epfl-thesis-4976
Information Processing and Structure of Dynamical Networks
  • Jan 1, 2011
  • Ali Ajdari Rad

Information Processing and Structure of Dynamical Networks

  • Research Article
  • Cite Count Icon 5
  • 10.1080/02533839.2020.1838949
Learning Dynamic Bayesian Networks structure based on a new hybrid K2-Bat learning algorithm
  • Nov 19, 2020
  • Journal of the Chinese Institute of Engineers
  • Yu-Jing Deng + 3 more

The temporal dimension makes it difficult and complex to learn the Dynamic Bayesian Networks structure for huge search space. We propose a new hybrid K2-Bat algorithm to learn the structure of Dynamic Bayesian Networks. This work contains two optimal strategies: an ordering-based algorithm INOK2 to learn initial network structure and an adaptive binary bat algorithm to learn transition network structure. Based on the requirement of K2 algorithm for prior knowledge, a fitness function is built to quantitatively score node order in INOK2. The initial population is generated by the node block sequence constructed by directional support tree. A dynamic learning factor, inverted mutation s`node sequence to improve the global searching ability and the convergence speed. Then, the optimal initial network structure can be obtained. In addition, an improved binary bat algorithm is proposed to improve the development behavior of bat algorithm by using dynamic selection strategy in transitional network learning. Finally, experiments on four well-known benchmark problems are performed. The results show that the proposed algorithm can successfully learn the structure of Dynamic Bayesian Networks without prior knowledge, and balance solutions quality and computational effort.

  • Research Article
  • 10.1007/s10072-024-07506-8
Alterations in spatiotemporal characteristics of dynamic networks in juvenile myoclonic epilepsy.
  • May 4, 2024
  • Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
  • Ming Ke + 5 more

Juvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients' and controls' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels. Our findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN. Our findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease.

  • Research Article
  • Cite Count Icon 12
  • 10.1002/ece3.2749
Ecological consequences of colony structure in dynamic ant nest networks.
  • Jan 24, 2017
  • Ecology and Evolution
  • Samuel Ellis + 2 more

Access to resources depends on an individual's position within the environment. This is particularly important to animals that invest heavily in nest construction, such as social insects. Many ant species have a polydomous nesting strategy: a single colony inhabits several spatially separated nests, often exchanging resources between the nests. Different nests in a polydomous colony potentially have differential access to resources, but the ecological consequences of this are unclear. In this study, we investigate how nest survival and budding in polydomous wood ant (Formica lugubris) colonies are affected by being part of a multi‐nest system. Using field data and novel analytical approaches combining survival models with dynamic network analysis, we show that the survival and budding of nests within a polydomous colony are affected by their position in the nest network structure. Specifically, we find that the flow of resources through a nest, which is based on its position within the wider nest network, determines a nest's likelihood of surviving and of founding new nests. Our results highlight how apparently disparate entities in a biological system can be integrated into a functional ecological unit. We also demonstrate how position within a dynamic network structure can have important ecological consequences.

  • Research Article
  • Cite Count Icon 6
  • 10.7498/aps.69.20190830
Node influence of the dynamic networks
  • Jan 1, 2020
  • Acta Physica Sinica
  • Zhuo-Ming Ren

Crucial to the physicists’ strong interest in the field is the fact that such macroscopic properties typically arise as the result of a myriad of interactions between the system constituents. Network science aims at simplifying the study of a given complex system by representing it as a network, a collection of nodes and edges interconnecting them. Nowadays, it is widely recognized that some of the structural traits of networks are in fact ubiquitous properties in real systems. The identification and prediction of node influence are of great theoretical and practical significance to be known as a hot research field of complex networks. Most of current research advance is focused on static network or a snapshot of dynamic networks at a certain moment. However, in practical application scenarios, mostly complex networks extracted from society, biology, information, technology are evolving dynamically. Therefore, it is more meaningful to evaluate the node's influence in the dynamic network and predict the future influence of the node, especially before the change of the network structure. In this summary, we contribute on reviewing the improvement of node influence in dynamical networks, which involves three tasks: algorithmic complexity and time bias in growing networks; algorithmic applicability in time varying networks; algorithmic robustness in a dynamical network with small or sharp perturbation. Furthermore, we overview the framework of economic complexity based on dynamical network structure. Lastly, we point out the forefront as well as critical challenges of the field.

  • Conference Article
  • Cite Count Icon 4
  • 10.1109/sitis.2012.111
On the Structure of Changes in Dynamic Contact Networks
  • Nov 1, 2012
  • V Neiger + 2 more

We present a methodology to investigate the structure of dynamic networks in terms of concentration of changes in the network. We handle dynamic networks as series of graphs on a fixed set of nodes and consider the changes occurring between two consecutive graphs in the series. We apply our methodology to various dynamic contact networks coming from different contexts and we show that changes in these networks exhibit a non-trivial structure: they are not spread all over the network but are instead concentrated around a small fraction of nodes. We compare our observations on real-world networks to three classical dynamic network models and show that they do not capture this key property.

  • Research Article
  • 10.4018/jats.2011010105
Simulating Tolerance in Dynamic Social Networks
  • Jan 1, 2011
  • International Journal of Agent Technologies and Systems
  • Kristen Lund + 1 more

This paper studies the concept of tolerance in dynamic social networks where agents are able to make and break connections with neighbors to improve their payoffs. This problem was initially introduced to the authors by observing resistance (or tolerance) in experiments run in dynamic networks under the two rules that they have developed: the Highest Rewarding Neighborhood rule and the Highest Weighted Reward rule. These rules help agents evaluate their neighbors and decide whether to break a connection or not. They introduce the idea of tolerance in dynamic networks by allowing an agent to maintain a relationship with a bad neighbor for some time. In this research, the authors investigate and define the phenomenon of tolerance in dynamic social networks, particularly with the two rules. The paper defines a mathematical model to predict an agent’s tolerance of a bad neighbor and determine the factors that affect it. After defining a general version of tolerance, the idea of optimal tolerance is explored, providing situations in which tolerance can be used as a tool to affect network efficiency and network structure.

  • Conference Article
  • Cite Count Icon 79
  • 10.1145/1830252.1830269
Meaningful selection of temporal resolution for dynamic networks
  • Jul 24, 2010
  • Rajmonda Sulo + 2 more

The understanding of dynamics of data streams is greatly affected by the choice of temporal resolution at which the data are discretized, aggregated, and analyzed. Our paper focuses explicitly on data streams represented as dynamic networks. We propose a framework for identifying meaningful resolution levels that best reveal critical changes in the network structure, by balancing the reduction of noise with the loss of information. We demonstrate the applicability of our approach by analyzing various network statistics of both synthetic and real dynamic networks and using those to detect important events and changes in dynamic network structure.

  • Research Article
  • Cite Count Icon 6
  • 10.1007/s10015-011-0943-7
A learning method for dynamic Bayesian network structures using a multi-objective particle swarm optimizer
  • Dec 1, 2011
  • Artificial Life and Robotics
  • Kousuke Shibata + 2 more

In this article, we present a multi-objective discrete particle swarm optimizer (DPSO) for learning dynamic Bayesian network (DBN) structures. The proposed method introduces a hierarchical structure consisting of DPSOs and a multi-objective genetic algorithm (MOGA). Groups of DPSOs find effective DBN sub-network structures and a group of MOGAs find the whole of the DBN network structure. Through numerical simulations, the proposed method can find more effective DBN structures, and can obtain them faster than the conventional method.

Save Icon
Up Arrow
Open/Close
  • Ask R Discovery Star icon
  • Chat PDF Star icon

AI summaries and top papers from 250M+ research sources.

Search IconWhat is the difference between bacteria and viruses?
Open In New Tab Icon
Search IconWhat is the function of the immune system?
Open In New Tab Icon
Search IconCan diabetes be passed down from one generation to the next?
Open In New Tab Icon