Dynamic, small‐world social network generation through local agent interactions

Abstract

To model agent relationships in agent-based models, it is often necessary to incorporate a social network whose topology is commonly assumed to be "small-world". This is potentially problematic, as the classification is broad and covers a widerange of network statistics. Furthermore, real networks are often dynamic, in that edges and nodes can appear or disappear, and spatial, in that connections are influenced by an agent's position within a particular social space. These properties are difficult to achieve in current network formation tools. We have therefore developed a novel social network formation model, that creates and dynamically adjusts smallworld networks using local spatial interactions, whilst maintaining tunable global network statistics from across the broad space of possible small-world networks. It is therefore a useful tool for multi-agent simulations and diffusion processes, particularly those in which agents and edges die or are constrained in their movement within some social space. We also show, using a simple epidemiological diffusion model, that a range of networks can all satisfy the small-world criterion, but behave quite differently. This demonstrates that it is problematic to generalise results across the whole space of small-world networks.

From Agent-Based Models to Network Analysis (and Return): The Policy-Making Perspective

“…Hamill and Gilbert, 2009;De Caux et al, 2014) where the agents act to generate the network; in our context, agents are activated, following their internal rules and capabilities, by the events, and the network emerges as a side effect, as in the real world 7 .…”

“…While models using these methods are currently thriving, the attempts at applying them jointly are not very frequent (De Caux et al 2014, Hamill and Gilbert 2009, Edmonds and Chattoe 2005, Kirman and Vriend 2001, Weisbuch et al 2000. In the paper, we argue that the combination of the two methods can increase enormously the potential of complexity-based policies and we propose a model that operationalizes the merger of the two from an innovative perspective.…”

“…We stress this last sentence: we have interesting works on ABM and networks (Hamill and Gilbert, 2009;De Caux et al, 2014) where the agents act to generate the network; in our context, agent are activated, following their internal rules and capabilities, by the events, and the network emerges as a side effect, as in the real world. We can apply to our (C) structures exactly the same range (palette) of algorithms that we can apply to (D).…”

Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

“…The existing studies establish various social influence analysis models [6,7], but they just study the influence in the individual level and mostly ignore the existence of a common influence pattern from a community that includes multiple nodes. A large number of achievements have been obtained on individual-level influence, but most of the studies are based on static statistics method [8][9][10][11], link analysis algorithms [12][13][14], or probabilistic models [15][16][17]. These studies do not consider whether the user is willing to receive or diffuse information or what the role of social trust between users is or do not remove zombie fans.…”

Generalized Network Generation Method for Small-World Network and Scale-Free Network

“…They introduced small-world network model, which is highly clustered like regular lattice, yet has small average shortest path length like random network. It is known small-world network characterizes many real world networks such as the social network [7] , the biochemical network [8] , the Internet [2,9] , VoD service over IP network [10] , and etc. The other finding is scale-free network.…”