Information flow in social groups

Abstract

We present a study of information flow that takes into account the observation that an item relevant to one person is more likely to be of interest to individuals in the same social circle than those outside of it. This is due to the fact that the similarity of node attributes in social networks decreases as a function of the graph distance. An epidemic model on a scale-free network with this property has a finite threshold, implying that the spread of information is limited. We tested our predictions by measuring the spread of messages in an organization and also by numerical experiments that take into consideration the organizational distance among individuals.The problem of information flows in social organizations is relevant to issues of productivity, innovation and the sorting out of useful ideas out of the general chatter of a community. How information spreads determines the speed with which individuals can act and plan their future activities. In particular, email has become the predominant means of communication in the information society. It pervades business, social and scientific exchanges and as such it is a highly relevant area for research on communities and social networks. Not surprisingly, email has been established as an indicator of collaboration and knowledge exchange [1,2,3,4,5]. Email is also a good medium for research because it provides plentiful data on personal communication in an electronic form.Since individuals tend to organize both formally and informally into groups based on their common activities and interests, the way information spreads is affected by the topology of the interaction network, not unlike the spread of a disease among individuals. Thus one would expect that epidemic models on graphs are relevant to the study of information flow in organizations. In particular, recent work on epidemic propagation on scale free networks found that the threshold for an epidemic is zero, implying that a finite fraction of the graph becomes infected for arbitrarily low transmission probabilities [6,7,8]. The presence of additional network structure was found to further influence the spread of disease on scale-free graphs [9, 10, 11].There are, however, differences between information flows and the spread of viruses. While viruses tend to be indiscriminate, infecting any susceptible individual, information is selective and passed by its host only to individuals the host thinks would be interested in it.The information any individual is interested in depends strongly on their characteristics. Furthermore, individuals with similar characteristics tend to associate with one another, a phenomenon known as homophily [12,13,14]. Conversely, individuals many steps removed in a social network on average tend not to have as much in common, as shown in a study [15] of a network of Stanford student homepages and illustrated in Figure 1.We therefore introduce an epidemic model with decay in the transmission probability of a particular piece of in- formation as a function of the distance between t...

Information diffusion through blogspace

“…Another refinement is to modify the transmission model. Wu et al [30] consider the flow of information through real and synthetic email networks under a model in which the probability of infection decays as the distance to the initiator v0 increases. They observe that meme outbreaks under their model are typically limited in scope-unlike in the corresponding model without decay, where the epidemic threshold is zero-exactly as one observes in real data.…”

A Perspective on Blockchain Smart Contracts: Reducing Uncertainty and Complexity in Value Exchange

“…This smart contract also implements the formal, well-known SIR (Susceptible, Infected, Recovered) agent-based simulation model [11] used in epidemiology to describe the spread of infectious disease. The SIR model can also be used to describe the spread of information in a network of individuals [12]. Analogous to ModelChain, estimates for model parameters are calculated via aggregating the collected data in order to execute the SIR model.…”

Visualisation of the organisation knowledge structure evolution

“…In many organisations, knowledge sharing among members is primarily sustained through a range of asynchronous communication technologies such as email lists (Wu et al, 2004), news portals (Jones et al, 2004), and organisational discussion groups (Wasko and Faraj, 2005) among others. In such networks individuals congregate based on shared interest and use the asynchronous nature of the technology to overcome the same-place same-time limitation inherent in face-to-face settings, which is necessary for successful collaboration in global organisations.…”