A limitation of the Graph Convolutional Networks (GCNs) is that it assumes at a particular @math layer of the neural network model only the @math order neighbourhood nodes of a social network are influential. Furthermore, the GCN has been evaluated on citation and knowledge graphs, but not extensively on friendship-based social graphs. The drawback associated with the dependencies between layers and the order of node neighbourhood for the GCN can be more prevalent for friendship-based graphs. The evaluation of the full potential of the GCN on friendship-based social network requires openly available datasets in larger quantities. However, most available social network datasets are not complete. Also, the majority of the available social network datasets do not contain both the features and ground truth labels. In this work, firstly, we provide a guideline on simulating dynamic social networks, with ground truth labels and features, both coupled with the topology. Secondly, we introduce an open-source Python-based simulation library. We argue that the topology of the network is driven by a set of latent variables, termed as the social DNA (sDNA). We consider the sDNA as labels for the nodes. Finally, by evaluating on our simulated datasets, we propose four new variants of the GCN, mainly to overcome the limitation of dependency between the order of node-neighbourhood and a particular layer of the model. We then evaluate the performance of all the models and our results show that on 27 out of the 30 simulated datasets our proposed GCN variants outperform the original model.
provide a guideline on the agent-based modelling of social networks. In the paper by , it is argued that the interplay between the micro and macro level characteristics is complex, and the macro level characteristics are not emergent solely from the simple aggregation of micro level characteristics or low level entities such as social network users @cite_14 . Instead, micro and macro level behaviour or characteristics form a feedback loop, resulting in a nonlinear interaction. From a social network point of view, the graph level and node level characteristics could be thought of as macro and micro level characteristics of the network respectively.
@cite_14
2096934547 2167951823 2761043131 1980722028
This paper develops a simple model in which a social hierarchy emerges endogenously when agents form a network for complementary interaction ( activity''). Specifically, we assume that agents are ex ante identical and their best response activity, as well as their value function, increases (strictly) concavely in the total activity of their neighbours in the network. There exists a unique and stable positive activity equilibrium on exogenous networks under mild conditions. When we endogenise network formation, equilibria become strongly structured: more active players have more neighbours, i.e. a higher degree, but tend to sponsor fewer links. Additionally, in strict equilibria agents separate themselves into groups characterised by the symmetric activity of their members. The characteristic activity decreases in group size and the network is a complete multipartite graph. ■ Abstract Sociologists often model social processes as interactions among variables. We review an alternative approach that models social life as interactions among adaptive agents who influence one another in response to the influence they receive. These agent-based models (ABMs) show how simple and predictable local interactions can generate familiar but enigmatic global patterns, such as the diffusion of information, emergence of norms, coordination of conventions, or participation in collective action. Emergent social patterns can also appear unexpectedly and then just as dramatically transform or disappear, as happens in revolutions, market crashes, fads, and feeding frenzies. ABMs provide theoretical leverage where the global patterns of interest are more than the aggregation of individual attributes, but at the same time, the emergent pattern cannot be understood without a bottom up dynamical model of the microfoundations at the relational level. We begin with a brief historical sketch of the shift from “factors” to “actors” in computational sociology that shows how agent-based modeling differs fundamentally from earlier sociological uses of computer simulation. We then review recent contributions focused on the emergence of social structure and social order out of local interaction. Although sociology has lagged behind other social sciences in appreciating this new methodology, a distinctive sociological contribution is evident in the papers we review. First, theoretical interest focuses on dynamic social networks that shape and are shaped by agent interaction. Second, ABMs are used to perform virtual experiments that test macrosociological theories by manipulating structural factors like network topology, social stratification, or spatial mobility. We conclude our review with a series of recommendations for realizing the rich sociological potential of this approach. Agent-based modeling of zapping behavior of viewers, television commercial allocation, and advertisement markets by Hiroyuki Kyan and Jun-ichi Inoue.- Agent based modeling of Housing asset bubble: A simple utility function based investigation by Kausik Gangopadhyay and Kousik Guhathakurta.- Urn model-based adaptive Multi-arm clinical trials: A stochastic approximation approach by Sophie Laruelle and Gilles Pages.- Logistic modeling of a Religious Sect features by Marcel Ausloos.- Characterizing financial crisis by means of the three states random field Ising model by Mitsuaki Murota and Jun-ichi Inoue.- Themes and applications of kinetic exchange models: Redux by Asim Ghosh, Anindya S. Chakrabarti, Anjan Kumar Chandra and Anirban Chakraborti.- Kinetic exchange opinion model: solution in the single parameter map limit by Krishanu Roy Chowdhury, Asim Ghosh, Soumyajyoti Biswas and Bikas K. Chakrabarti.- An overview of the new frontiers of Economic Complexity by Matthieu Cristelli, Andrea Tacchella, Luciano Pietronero.- Jan Tinbergen's legacy for economic networks: from the gravity model to quantum statistics by Tiziano Squartini and Diego Garlaschelli.- A macroscopic order of consumer demand due to heterogenous consumer behaviors on Japanese household demand tested by the random matrix theory by Yuji Aruka,Yuichi Kichikawa and Hiroshi Iyetomi.- Uncovering the network structure of the world currency market: Cross-correlations in the fluctuations of daily exchange rates by Sitabhra Sinha and Uday Kovur.- Systemic risk in Japanese credit network by Hideaki Aoyama.- Pricing of goods with Bandwagon properties: The curse of coordination by Mirta B. Gordon, Jean-Pierre Nadal, Denis Phan and Viktoriya Semeshenko.- Evolution of Econophysics by Kishore C. Dash.- Econophysics and sociophysics: Problems and prospects by Asim Ghosh and Anindya S. Chakrabarti.- A discussion on Econophysics by Hideaki Aoyama. We study the detailed growth of a social networking site with full temporal information by examining the creation process of each friendship relation that can collectively lead to the macroscopic properties of the network. We first study the reciprocal behavior of users, and find that link requests are quickly responded to and that the distribution of reciprocation intervals decays in an exponential form. The degrees of inviters accepters are slightly negatively correlative with reciprocation time. In addition, the temporal feature of the online community shows that the distributions of intervals of user behaviors, such as sending or accepting link requests, follow a power law with a universal exponent, and peaks emerge for intervals of an integral day. We finally study the preferential selection and linking phenomena of the social networking site and find that, for the former, a linear preference holds for preferential sending and reception, and for the latter, a linear preference also holds for preferential acceptance, creation, and attachment. Based on the linearly preferential linking, we put forward an analyzable network model which can reproduce the degree distribution of the network. The research framework presented in the paper could provide a potential insight into how the micro-motives of users lead to the global structure of online social networks.