Observing Cascade Behavior Depending on the Network Topology and Transaction Costs
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Keywords:
Social Connectedness
Information cascade
Network Formation
Social network (sociolinguistics)
Social connectedness is an indicator of the extent to which people can realize various network benefits and is therefore a source of social capital. Using the case of Twitter, a theoretical model of social connectedness based on the functional and structural characteristics of people's communication behavior within an online social network is developed and tested. The study investigates how social presence, social awareness, and social connectedness influence each other, and when and for whom the effects of social presence and social awareness are most strongly related to positive outcomes in social connectedness. Specifically, the study looks at the concurrent direct and moderating effect of two structural constructs characterizing people's online social network: network size and frequency of usage. The research model is tested using data (n = 121) collected from two sources: (a) an online survey of Twitter users and (b) their usage data collected directly from Twitter. Results indicate that social awareness, social presence, and usage frequency have a direct effect on social connectedness, whereas network size has a moderating effect. Social presence is found to partially mediate the relationship between social awareness and social connectedness. The findings of the analysis are used to outline design implications for online social networks from a human–computer interaction perspective.
Social Connectedness
Social network (sociolinguistics)
Online participation
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Network has evolved from information network to social network.Information network is in the continual evolution driven by new technology forces and meets the hyper-Moore's law and tends to a combination of the triple nets.Social network relies on social software to build a new virtual world on the basis of the information network and to rebuild social relations.As a scale-free net,social network can form a small world,focusing on Unicom's theory.The polymerization of social network has provided valuable inspiration for education.
Social network (sociolinguistics)
Organizational network analysis
Network Formation
Dynamic network analysis
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Social networks have been a key subject of research in recent years, because they can help to understand the people's behavior such as information diffusion, decision making and social influence. This paper is devoted to the study of one large-scale social interaction network by carrying out a systematic analysis of network properties and evolution factors. The network properties include degree distribution, clustering coefficient, the stability of the social network to nodes removal. The study of network evolution is based on the changes of network size, probability of becoming one user of the social network on function of number of earlier neighbors, and the roles of the nodes. This social network is constructed based on communication records of users from one instant message network.
Social network (sociolinguistics)
Degree distribution
Clustering coefficient
Hierarchical network model
Dynamic network analysis
Organizational network analysis
Network Formation
Social Network Analysis
Evolving networks
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In this paper we study how the network of agents adopting a particular technology relates to the structure of the underlying network over which the technology adoption spreads. We develop a model and show that the network of agents adopting a particular technology may have characteristics that differ significantly from the social network of agents over which the technology spreads. For example, the network induced by a cascade may have a heavy-tailed degree distribution even if the original network does not.
This provides evidence that online social networks created by technology adoption over an underlying social network may look fundamentally different from social networks and indicates that using data from many online social networks may mislead us if we try to use it to directly infer the structure of social networks. Our results provide an alternate explanation for certain properties repeatedly observed in data sets, for example: heavy-tailed degree distribution, network densification, shrinking diameter, and network community profile. These properties could be caused by a sort of `sampling bias' rather than by attributes of the underlying social structure. By generating networks using cascades over traditional network models that do not themselves contain these properties, we can nevertheless reliably produce networks that contain all these properties.
An opportunity for interesting future research is developing new methods that correctly infer underlying network structure from data about a network that is generated via a cascade spread over the underlying network.
Social network (sociolinguistics)
Network Formation
Network Structure
Organizational network analysis
Degree distribution
Information cascade
Evolving networks
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Social connectedness is an indicator of the extent to which people can realize various network benefits and is therefore a source of social capital. Using the case of Twitter, we develop and test a theoretical model of social connectedness based on the functional and structural characteristics of peoples’ communication behavior within a online social network. We investigate how social presence, social awareness, and social connectedness influence each other, and when and for whom the effects of social presence and social awareness are most strongly related to positive outcomes in social connectedness. Specifically, we study the concurrent direct and moderating effect of two structural constructs characterizing peoples’ online social network: network size and frequency of usage. We test our research model using data (n=121) collected from two sources: (1) an online survey of Twitter users and (2) their usage data collected directly from Twitter. Our results indicate that social awareness, social presence, and usage frequency have a direct effect on social connectedness, while network size has a moderating effect. We find social presence partially mediating the relationship between social awareness and social connectedness. We use the findings of our analysis to outline design implications for online social networks from a human computer interaction perspective.
Social Connectedness
Social network (sociolinguistics)
Social heuristics
Online participation
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In this paper we study how the network of agents adopting a particular technology relates to the structure of the underlying network over which the technology adoption spreads. We develop a model and show that the network of agents adopting a particular technology may have characteristics that differ significantly from the social network of agents over which the technology spreads. For example, the network induced by a cascade may have a heavy-tailed degree distribution even if the original network does not. This provides evidence that online social networks created by technology adoption over an underlying social network may look fundamentally different from social networks and indicates that using data from many online social networks may mislead us if we try to use it to directly infer the structure of social networks. Our results provide an alternate explanation for certain properties repeatedly observed in data sets, for example: heavy-tailed degree distribution, network densification, shrinking diameter, and network community profile. These properties could be caused by a sort of `sampling bias' rather than by attributes of the underlying social structure. By generating networks using cascades over traditional network models that do not themselves contain these properties, we can nevertheless reliably produce networks that contain all these properties. An opportunity for interesting future research is developing new methods that correctly infer underlying network structure from data about a network that is generated via a cascade spread over the underlying network.
Social network (sociolinguistics)
Network Formation
Information cascade
Network Structure
Organizational network analysis
Degree distribution
Evolving networks
Dynamic network analysis
Hierarchical network model
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Much research suggests that social networks affect individual and organizational success. However, a strong assumption underlying this research is that network structure is not reducible to the individual attributes of social actors. In this article, we test this assumption by examining whether interacting with random peers causes exogenous growth of a person’s network. Using three years of network data for students at an Indian college, we evaluate the effect of peers on network growth. We find strong evidence that interacting with random, but well-connected, roommates causes significant growth of a focal student’s network. Further, we find that this growth also implies an increase in how close an actor moves to a network’s center and whether that actor is likely to serve as a network bridge. Fundamentally, our results demonstrate that exogenous factors beyond individual agency—i.e., random peers—can shape network structure. Our results also provide a useful model for causally identifying the determinants of network structure and dynamics. This paper was accepted by Gérard Cachon, organizations.
Social network (sociolinguistics)
Network Formation
Affect
Network Structure
Network Dynamics
Bridge (graph theory)
Peer Effects
Network model
Social Network Analysis
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Social networks are created by the underlying behavior of the actors involved in them. Each actor has interactions with other actors in the network and these interactions decide whether a social relationship should develop between them. Such interactions may occur due to meeting processes such as chance-based meetings or network-based (choice) meetings. Depending upon which of these two types of interactions plays a greater role in creation of links, a social network shall evolve accordingly. This evolution shall result in the social network obtaining a suitable structure and certain unique features. The aim of this work is to determine the relative ratio of the meeting processes that exist between different actors in a social network and their importance in understanding the procedure of network formation. This is achieved by selecting a suitable network genesis model. For this purpose, different models for network genesis are discussed in detail and their differences are highlighted through experimental results. Network genesis models are compared and contrasted with other approaches available in the literature, such as simulation-based models and block models. Performance measures to compare the results of the network genesis models with baselines are statistics of networks recreated using the models. The socially generated networks studied here belong to various domains like e- commerce, electoral processes, social networking websites, peer to peer file-sharing websites, and Internet graphs. The insights obtained after analyzing these datasets by network genesis models are used for prescribing measures that could ensure continuous growth of these social networks and improve the benefits for the actors involved in them.
Social network (sociolinguistics)
Network Formation
Social Network Analysis
Network model
Dynamic network analysis
Organizational network analysis
Preferential attachment
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Social integration is crucial for the overall well-being of the elderly who are more prone to social exclusion because of the natural aging process. We propose online social networking as means to enhance social connectedness and social support – two aspects of social networks that have significant implications for the well-being of elderly. While prior research investigating the benefits of online social networking has primarily focused on user groups such as teenagers and college students, there is less understanding on how online social networks can be used to support and strengthen social ties among elderly. This study intends to investigate means of increasing social connectedness and social support among elderly through participation in online social networks, and the resulting implications on overall satisfaction with life. Our aim is to identify features of online social networks that cater to the specific social connectedness and support related requirements of elderly users.
Social Connectedness
Social network (sociolinguistics)
Online participation
Social Engagement
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Estimating the impact of network effects on content production and friendship formation on social network sites (SNS) is of key importance to the platforms owners and online advertisers. However, past research on modeling network effects using observational data is limited by their inability to separate the effects of network formation from network influence. In the current study, we adapt an actor-based continuous-time model to jointly estimate the co-evolution of the users' social network and their content production behavior using a Markov Chain Monte Carlo (MCMC) based approach. Our analysis on a dataset of university students reveals that: 1) users tend to connect with others with similar posting behavior, 2) however, after connecting, they gradually diverge from their peers, and 3) the network effects are moderated by the level of the posting behavior. Our findings offer useful insights about the role of network effects to platform owners and social network researchers.
Social network (sociolinguistics)
Network Formation
Social Network Analysis
Network model
Content (measure theory)
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