Research

2205 KiB

Open AccessArticle

Core-Based Dynamic Community Detection in Mobile Social Networks

by Hao Xu, Yanli Hu, Zhenwen Wang, Jianwei Ma and Weidong Xiao

Entropy 2013, 15(12), 5419-5438; https://doi.org/10.3390/e15125419 - 06 Dec 2013

Cited by 16 | Viewed by 6208

The topic of community detection in social networks has attracted a lot of attention in recent years. Existing methods always depict the relationship of two nodes using the snapshot of the network, but these snapshots cannot reveal the real relationships, especially when the [...] Read more.

The topic of community detection in social networks has attracted a lot of attention in recent years. Existing methods always depict the relationship of two nodes using the snapshot of the network, but these snapshots cannot reveal the real relationships, especially when the connection history among nodes is considered. The problem of detecting the stable community in mobile social networks has been studied in this paper. Community cores are considered as stable subsets of the network in previous work. Based on these observations, this paper divides all nodes into a few of communities due to the community cores. Meanwhile, communities can be tracked through incremental computing. Experimental results based on real-world social networks demonstrate that our proposed method performs better than the well-known static community detection algorithm in mobile social networks. Full article

(This article belongs to the Special Issue Social Networks and Information Diffusion)

► Show Figures

Graphical abstract

705 KiB

Open AccessArticle

Dynamical Structure of a Traditional Amazonian Social Network

by Paul L. Hooper, Simon DeDeo, Ann E. Caldwell Hooper, Michael Gurven and Hillard S. Kaplan

Entropy 2013, 15(11), 4932-4955; https://doi.org/10.3390/e15114932 - 13 Nov 2013

Cited by 24 | Viewed by 11163

Abstract

Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in [...] Read more.

Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha; Tsimane’: shocdye’) drinking events in a Tsimane’ village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days) is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone. Full article

(This article belongs to the Special Issue Social Networks and Information Diffusion)

► Show Figures

Figure 1

311 KiB

Open AccessArticle

From Observable Behaviors to Structures of Interaction in Binary Games of Strategic Complements

by Tomás Rodríguez Barraquer

Entropy 2013, 15(11), 4648-4667; https://doi.org/10.3390/e15114648 - 29 Oct 2013

Cited by 2 | Viewed by 5211

Abstract

Consider a setting in which agents can take one of two ordered actions and in which the incentive to take the high action increases in the number of other agents taking it. Furthermore, assume that we do not know anything else about the [...] Read more.

Consider a setting in which agents can take one of two ordered actions and in which the incentive to take the high action increases in the number of other agents taking it. Furthermore, assume that we do not know anything else about the game being played. What can we say about the details of the interaction between actions and incentives when we observe a set or a subset of all possible equilibria? In this paper, we study this question by exploring three nested classes of games: (a) binary games of strategic complements; (b) games in (a) that admit a network representation; and (c) games in (b) in which the network is complete. Our main results are the following: It has long been established in the literature that the set of pure strategy Nash equilibria of any binary game of strategic complements among a set, N, of agents can be seen as a lattice on the set of all subsets of N under the partial order defined by the set inclusion relation (C). If the game happens to be strict in the sense that agents are never indifferent among outcomes (games in (a)), then the resulting lattice of equilibria satisfies a straightforward sparseness condition. (1) We show that, in fact, for each such lattice, L, there is a game in (a), such that its set of equilibria is L (we say that such a game expresses L); (2) We show that there exists a game in (b), whose set of equilibria contains a given collection, C, of subsets of N, if and only C satisfies the sparseness condition, and the smallest game in (a) expressing C is trade robust; (3) We show that there exists a game on the complete graph (games in (c)), whose set of equilibria coincides with some collection, C, if and only if C is a chain satisfying the sparseness condition. Full article

(This article belongs to the Special Issue Social Networks and Information Diffusion)

► Show Figures

Figure 1

2464 KiB

Open AccessArticle

Diffusion Dynamics with Changing Network Composition

by Raquel A. Baños, Javier Borge-Holthoefer, Ning Wang, Yamir Moreno and Sandra González-Bailón

Entropy 2013, 15(11), 4553-4568; https://doi.org/10.3390/e15114553 - 24 Oct 2013

Cited by 10 | Viewed by 9412

Abstract

We analyze information diffusion using empirical data that tracks online communication around two instances of mass political mobilization that took place in Spain in 2011 and 2012. We also analyze protest-related communications during the year that elapsed between those protests. We compare the [...] Read more.

We analyze information diffusion using empirical data that tracks online communication around two instances of mass political mobilization that took place in Spain in 2011 and 2012. We also analyze protest-related communications during the year that elapsed between those protests. We compare the global properties of the topological and dynamic networks through which communication took place, as well as local changes in network composition. We show that changes in network structure underlie aggregated differences on how information diffused: an increase in network hierarchy is accompanied by a reduction in the average size of cascades. The increasing hierarchy affects not only the underlying communication topology but also the more dynamic structure of information exchange; the increase is especially noticeable amongst certain categories of nodes (or users). Our findings suggest that the relationship between the structure of networks and their function in diffusing information is not as straightforward as some theoretical models of diffusion in networks imply. Full article

(This article belongs to the Special Issue Social Networks and Information Diffusion)

► Show Figures

Graphical abstract

525 KiB

Open AccessArticle

Modeling Dynamics of Diffusion Across Heterogeneous Social Networks: News Diffusion in Social Media

by Minkyoung Kim, David Newth and Peter Christen

Entropy 2013, 15(10), 4215-4242; https://doi.org/10.3390/e15104215 - 08 Oct 2013

Cited by 32 | Viewed by 9244

Abstract

Diverse online social networks are becoming increasingly interconnected by sharing information. Accordingly, emergent macro-level phenomena have been observed, such as the synchronous spread of information across different types of social media. Attempting to analyze the emergent global behavior is impossible from the examination [...] Read more.

Diverse online social networks are becoming increasingly interconnected by sharing information. Accordingly, emergent macro-level phenomena have been observed, such as the synchronous spread of information across different types of social media. Attempting to analyze the emergent global behavior is impossible from the examination of a single social platform, and dynamic influences between different social networks are not negligible. Furthermore, the underlying structural property of networks is important, as it drives the diffusion process in a stochastic way. In this paper, we propose a macro-level diffusion model with a probabilistic approach by combining both the heterogeneity and structural connectivity of social networks. As real-world phenomena, we explore instances of news diffusion across different social media platforms from a dataset that contains over 386 million web documents covering a one-month period in early 2011. We find that influence between different media types is varied by the context of information. News media are the most influential in the arts and economy categories, while social networking sites (SNS) and blog media are in the politics and culture categories, respectively. Furthermore, controversial topics, such as political protests and multiculturalism failure, tend to spread concurrently across social media, while entertainment topics, such as film releases and celebrities, are more likely driven by interactions within single social platforms. We expect that the proposed model applies to a wider class of diffusion phenomena in diverse fields and that it provides a way of interpreting the dynamics of diffusion in terms of the strength and directionality of influences among populations. Full article

(This article belongs to the Special Issue Social Networks and Information Diffusion)

► Show Figures

Figure 1

472 KiB

Open AccessArticle

Low-Temperature Behaviour of Social and Economic Networks

by Diego Garlaschelli, Sebastian E. Ahnert, Thomas M. A. Fink and Guido Caldarelli

Entropy 2013, 15(8), 3148-3169; https://doi.org/10.3390/e15083238 - 05 Aug 2013

Cited by 10 | Viewed by 5758

Abstract

Real-world social and economic networks typically display a number of particular topological properties, such as a giant connected component, a broad degree distribution, the small-world property and the presence of communities of densely interconnected nodes. Several models, including ensembles of networks, also known [...] Read more.

Real-world social and economic networks typically display a number of particular topological properties, such as a giant connected component, a broad degree distribution, the small-world property and the presence of communities of densely interconnected nodes. Several models, including ensembles of networks, also known in social science as Exponential Random Graphs, have been proposed with the aim of reproducing each of these properties in isolation. Here, we define a generalized ensemble of graphs by introducing the concept of graph temperature, controlling the degree of topological optimization of a network. We consider the temperature-dependent version of both existing and novel models and show that all the aforementioned topological properties can be simultaneously understood as the natural outcomes of an optimized, low-temperature topology. We also show that seemingly different graph models, as well as techniques used to extract information from real networks are all found to be particular low-temperature cases of the same generalized formalism. One such technique allows us to extend our approach to real weighted networks. Our results suggest that a low graph temperature might be a ubiquitous property of real socio-economic networks, placing conditions on the diffusion of information across these systems. Full article

(This article belongs to the Special Issue Social Networks and Information Diffusion)

► Show Figures

Figure 1

340 KiB

Open AccessArticle

What Do Leaders Know?

by Giacomo Livan and Matteo Marsili

Entropy 2013, 15(8), 3031-3044; https://doi.org/10.3390/e15083031 - 26 Jul 2013

Cited by 6 | Viewed by 5083

Abstract

The ability of a society to make the right decisions on relevant matters relies on its capability to properly aggregate the noisy information spread across the individuals of which it is made. In this paper, we study the information aggregation performance of a [...] Read more.

The ability of a society to make the right decisions on relevant matters relies on its capability to properly aggregate the noisy information spread across the individuals of which it is made. In this paper, we study the information aggregation performance of a stylized model of a society, whose most influential individuals—the leaders—are highly connected among themselves and uninformed. Agents update their state of knowledge in a Bayesian manner by listening to their neighbors. We find analytical and numerical evidence of a transition, as a function of the noise level in the information initially available to agents, from a regime where information is correctly aggregated, to one where the population reaches consensus on the wrong outcome with finite probability. Furthermore, information aggregation depends in a non-trivial manner on the relative size of the clique of leaders, with the limit of a vanishingly small clique being singular. Full article

(This article belongs to the Special Issue Social Networks and Information Diffusion)

► Show Figures

Figure 1

250 KiB

Open AccessArticle

Exploring the Characteristics of Innovation Adoption in Social Networks: Structure, Homophily, and Strategy

by Yongli Li, Chong Wu, Peng Luo and Wei Zhang

Entropy 2013, 15(7), 2662-2678; https://doi.org/10.3390/e15072662 - 10 Jul 2013

Cited by 19 | Viewed by 7489

Abstract

Exploration of the characteristics of innovation adoption in the context of social network will add new insights beyond the traditional innovation models. In this paper, we establish a new agent-based model to simulate the behaviors of agents in terms of innovation adoption. Specifically, [...] Read more.

Exploration of the characteristics of innovation adoption in the context of social network will add new insights beyond the traditional innovation models. In this paper, we establish a new agent-based model to simulate the behaviors of agents in terms of innovation adoption. Specifically, we examine the effects of the network structure, homophily and strategy, among which homophily is a new topic in this field of innovation adoption. The experiments illustrate six important findings involving five aspects and their influences on the innovation adoption. The five aspects are initial conditions, homophily, network topology, rules of updating and strategy, respectively. This paper also compares the different cases within one aspect or across several aspects listed above. Accordingly, some management advices and future work are provided in the last part of this paper. Full article

(This article belongs to the Special Issue Social Networks and Information Diffusion)

► Show Figures

Figure 1

537 KiB

Open AccessArticle

Study on the Stability of an Artificial Stock Option Market Based on Bidirectional Conduction

by Hai-Jun Yang and Gui-Ping Sun

Entropy 2013, 15(2), 700-720; https://doi.org/10.3390/e15020700 - 22 Feb 2013

Cited by 2 | Viewed by 5247

Abstract

Although stock option markets have grown dramatically over the past several decades, the relation between an option and its underlying asset, especially bidirectional conduction, is not particularly clear. So far, there have been many debates about this topic. We try to investigate this [...] Read more.

Although stock option markets have grown dramatically over the past several decades, the relation between an option and its underlying asset, especially bidirectional conduction, is not particularly clear. So far, there have been many debates about this topic. We try to investigate this problem from a novel angle: an artificial stock market including a stock option is constructed in this paper. The model includes two parts, one is a stock trade module based on the Santa Fe Institute Artificial Stock Market (SFI-ASM), and the other is an option trade module. In the latter module, three types of option traders are employed. The results show that the model is effective, and experiments illustrate that option markets have a remarkable effect on stock markets. Furthermore, by appending options, the model replicates some stylized properties, such as volatility clustering and GARCH effect, which can be observed in real financial markets. Full article

(This article belongs to the Special Issue Social Networks and Information Diffusion)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Social Networks and Information Diffusion

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI