Spreading of social contagions without key players

Contagion models have been used to study the spread of social behavior among agents of a networked population. Examples include information diffusion, social influence, and participation in collective action (e.g., protests). Key players, which are typically agents characterized by structural properties of the underlying network (e.g., high degree, high core number or high centrality) are considered important for spreading social contagions. In this paper, we ask whether contagions can propagate through a population that is devoid of key players. We justify the use of Erd?s-Rényi random graphs as a representation of unstructured populations that lack key players, and investigate whether complex contagions—those requiring reinforcement—can spread on them. We demonstrate that two game-theoretic contagion models that utilize common knowledge for collective action can readily spread such contagions, thus differing significantly from classic complex contagion models. We compare contagion dynamics results on unstructured networks to those on more typically-studied, structured social networks to understand the role of network structure. We test the classic complex contagion and the two game-theoretic models with a total of 18 networks that range over five orders of magnitude in size and have different structural properties. The two common knowledge models are also contrasted to understand the effects of different modeling assumptions on dynamics. We show that under a wide range of conditions, these two models produce markedly different results. Finally, we demonstrate that the disparity between classic complex contagion and common knowledge models persists as network size increases.     

On optimal preference diffusion over social networks

It was well observed that a user’s preference over a product changes based on his/her friends’ preferences, and this phenomenon is called “preference diffusion”, and several models have been proposed for modeling the preference diffusion process. These models share an idea that the diffusion process involves many iterations, and in each iteration, each user has his/her preference affected by some other preferences (e.g., those of his/her friends). When computing users’ preferences after a certain number of iterations, these models use users’ preferences at the end of that iteration only, which we believe is not desirable since users’ preferences at the end of other iterations should also have some effects on users’ final preferences. Therefore, in this paper, we propose a new model for preference diffusion, which takes into consideration users’ preferences at each iteration for computing users’ final preferences. Under the new model, we study two problems for optimizing the preference diffusion process with respect to two different objectives. One is easy to solve for which we design an exact algorithm and the other is NP-hard for which we design a $(1-1∕e)$-factor approximate algorithm. We conducted extensive experiments on real datasets which verified our proposed model and algorithms.     

Determinants of virtual social networks diffusion: Insights from cross-country data

Utilizing Rogers' diffusion of innovation theory and Hofstede's typology of national culture as the guiding theoretical perspectives, this study examines the determinants of virtual social networks (VSNs) diffusion across countries. Specifically, this study proposes that VSN diffusion in a country is determined by the levels of its information infrastructure and human capital, which in turn are contingent on the national cultural dimension of uncertainty avoidance. By utilizing archival data from 56 countries, we examine (1) the direct effects of information infrastructure and human capital in a country on its VSN diffusion; and (2) the moderating effect of uncertainty avoidance on the relationships of information infrastructure and human capital in a country with its VSN diffusion. Our findings indicate that (1) information infrastructure and human capital in a country were positively associated with its VSN diffusion; and (2) uncertainty avoidance negatively moderated the relationships of information infrastructure and human capital in a country with its VSN diffusion. Our findings contribute to the knowledge base of VSNs by highlighting the contingent role of uncertainty avoidance, and provide indications to practice on managing VSN diffusion in a country by leveraging the effects of its information infrastructure and human capital.     

A note on competitive diffusion through social networks

We introduce a game-theoretic model of diffusion of technologies, advertisements, or influence through a social network. The novelty in our model is that the players are interested parties outside the network. We study the relation between the diameter of the network and the existence of pure Nash equilibria in the game. In particular, we show that if the diameter is at most two then an equilibrium exists and can be found in polynomial time, whereas if the diameter is greater than two then an equilibrium is not guaranteed to exist.     

复杂网络中的在线社交网络演化模型

在线社交网络是一种广泛存在的社会网络,其节点度遵循幂率分布规律,但对于其结构演化模型方面的相关研究还不多。基于复杂网络理论研究在线社交网络内部结构特征,提出一种结合内增长、外增长及内部边更替的演化模型,借助平均场理论分析该模型的拓扑特性,实验和理论分析表明由该模型生成的网络,其度分布服从幂率分布,且通过调整参数,幂率指数在1~3,能较好地反映不同类型的真实在线社交网络的度分布特征,因此具有广泛适用性。     

Information diffusion through social networks: The case of an online petition

People regularly use online social networks due to their convenience, efficiency, and significant broadcasting power for sharing information. However, the diffusion of information in online social networks is a complex and dynamic process. In this research, we used a case study to examine the diffusion process of an online petition. The spread of petitions in social networks raises various theoretical and practical questions: What is the diffusion rate? What actions can initiators take to speed up the diffusion rate? How does the behavior of sharing between friends influence the diffusion process? How does the number of signatures change over time? In order to address these questions, we used system dynamics modeling to specify and quantify the core mechanisms of petition diffusion online; based on empirical data, we then estimated the resulting dynamic model. The modeling approach provides potential practical insights for those interested in designing petitions and collecting signatures. Model testing and calibration approaches (including the use of empirical methods such as maximum-likelihood estimation, the Akaike information criterion, and likelihood ratio tests) provide additional potential practices for dynamic modelers. Our analysis provides information on the relative strength of push (i.e., sending announcements) and pull (i.e., sharing by signatories) processes and insights about awareness, interest, sharing, reminders, and forgetting mechanisms. Comparing push and pull processes, we found that diffusion is largely a pull process rather than a push process. Moreover, comparing different scenarios, we found that targeting the right population is a potential driver in spreading information (i.e., getting more signatures), such that small investments in targeting the appropriate people have ‘disproportionate’ effects in increasing the total number of signatures. The model is fully documented for further development and replications.     

MODM: multi-objective diffusion model for dynamic social networks using evolutionary algorithm

A lot of research efforts have been made to model the diffusion process in social networks that varies from adoption of products in marketing strategies to disease and virus spread. Previously, a diffusion process is usually considered as a single-objective optimization problem, in which different heuristics or approximate algorithms are applied to optimize an objective of spreading single piece of information that captures the notion of diffusion. However, in real social networks individuals simultaneously receive several pieces of information during their communication. Single-objective solutions are inadequate for collective spread of several information pieces. Therefore, in this paper, we propose a Multi-Objective Diffusion Model (MODM) that allows the modeling of complex and nonlinear phenomena of multiple types of information exchange, and calculate the information worth of each individual from different aspects of information spread such as score, influence and diversity. We design evolutionary algorithm to achieve the multi-objectives in single diffusion process. Through extensive experiments on a real world data set, we have observed that MODM leads to a richer and more realistic class of diffusion model compared to a single objective. This signifies the correlation between the importance of each individual and his information processing capability. Our results indicate that some individuals in the network are naturally and significantly better connected in terms of receiving information irrespective of the starting position of the diffusion process.     

Learning agent influence in MAS with complex social networks

In complex open multi-agent systems (MAS), where there is no centralised control and individuals have equal authority, ensuring cooperative and coordinated behaviour is challenging. Norms and conventions are useful means of supporting cooperation in an emergent decentralised manner, however it takes time for effective norms and conventions to emerge. Identifying influential individuals enables the targeted seeding of desirable norms and conventions, which can reduce the establishment time and increase efficacy. Existing research is limited with respect to considering (i) how to identify influential agents, (ii) the extent to which network location imbues influence on an agent, and (iii) the extent to which different network structures affect influence. In this paper, we propose a methodology for learning a model for predicting the network value of an agent, in terms of the extent to which it can influence the rest of the population. Applying our methodology, we show that exploiting knowledge of the network structure can significantly increase the ability of individuals to influence which convention emerges. We evaluate our methodology in the context of two agent-interaction models, namely, the language coordination domain used by Salazar et al. (AI Communications 23(4): 357–372, 2010) and a coordination game of the form used by Sen and Airiau (in: Proceedings of the 20th International Joint Conference on Artificial Intelligence, 2007) with heterogeneous agent learning mechanisms, and on a variety of synthetic and real-world networks. We further show that (i) the models resulting from our methodology are effective in predicting influential network locations, (ii) there are very few locations that can be classified as influential in typical networks, (iii) four single metrics are robustly indicative of influence across a range of network structures, and (iv) our methodology learns which single metric or combined measure is the best predictor of influence in a given network.     

Topological analysis of AOCD-based agent networks and experimental results

Topological analysis of intelligent agent networks provides crucial information about the structure of agent distribution over a network. Performance analysis of agent network topologies helps multi-agent system developers to understand the impact of topology on system efficiency and effectiveness. Appropriate topology analysis enables the adoption of suitable frameworks for specific multi-agent systems. In this paper, we systematically classify agent network topologies and propose a novel hybrid topology for distributed multi-agent systems. We compare the performance of this topology with two other common agent network topologies—centralised and decentralised topologies—within a new multi-agent framework, called Agent-based Open Connectivity for DSS (AOCD). Three major aspects are studied for estimating topology performance, which include (i) transmission time for a set of requests; (ii) waiting time for processing requests; and (iii) memory consumption for storing agent information. We also conduct a set of AOCD topological experiments to compare the performance of hybrid and centralised agent network topologies and illustrate our experimental results in this paper.     

支持复杂社会网络演化的过程挖掘技术综述

复杂社会网络演化过程研究对于发现社会网络群体的隐含结构和演化规律,以及风险预测具有重要意义。首先梳理了过程挖掘技术的发展脉络,阐述复杂社会网络分析方法与过程挖掘技术相结合在复杂社会网络演化模式研究、组织结构发现中的应用现状,结合社会网络分析方法和大数据技术,运用服务工程思想,进而从社会和资源维度综述社会网络跨组织业务过程发现、动态社会网络演化过程发现、角色挖掘与服务挖掘等技术,指出现有复杂社会网络过程挖掘研究面对大数据质量和跨组织异构等研究方面的不足,对大规模社会网络过程挖掘领域的研究难点和发展趋势进行了讨论。     

D2SNet: Dynamics of diffusion and dynamic human behaviour in social networks

In this paper, we present an original and formal framework, the D2SNet model designed to combine both the social network evolution and the diffusion dynamics among individuals. We have conducted experiments on three social networks that show identical characteristics as real social networks. A formal definition of the model is provided and we describe its implementation in a simulation tool. We represent human behaviors and information dissemination strategies by standard and synthetic scheme. In a first step, we study the impact of network growing strategies only and we highlight important parameters such as the evolution speed and mainly the kind of strategies that favour or not the spread. Then we study a more complete evolution strategy that involves link creation and deletion. We provide a deep analysis on the impact of each parameter such as evolution speed, creation and deletion probabilities and dynamic human behaviors on the diffusion amplitude and coverage. Our study gives a novel and useful insight in the diffusion process in a dynamic context.     

An adaptive social network for information access: Theoretical and experimental results

We consider a social network of software agents who assist each other in helping their users find information. Unlike in most previous approaches, our architecture is fully distributed and includes agents who preserve the privacy and autonomy of their users. These agents learn models of each other in terms of expertise (ability to produce correct domain answers) and sociability (ability to produce accurate referrals). We study our framework experimentally to study how the social network evolves. Specifically, we find that under our multi-agent learning heuristic, the quality of the network improves with interactions: the quality is maximized when both expertise and sociability are considered; pivot agents further improve the quality of the network and have a catalytic effect on its quality even if they are ultimately removed. Moreover, the quality of the network improves when clustering decreases, reflecting the intuition that you need to talk to people outside your close circle to get the best information.     

CFIN: A community-based algorithm for finding influential nodes in complex social networks

Influence maximization (IM) problem, a fundamental algorithmic problem, is the problem of selecting a set of k users (refer as seed set) from a social network to maximize the expected number of influenced users (also known as influence spread). Due to the numerous applications of IM in marketing, IM has been studied extensively in recent years. Nevertheless, many algorithms do not take into consideration the impact of communities to influence maximization and some algorithms are non-scalable and time-consuming in practice. In this paper, we proposed a fast and scalable algorithm called community finding influential node (CFIN) that selects k users based on community structure, which maximizes the influence spread in the networks. The CFIN consists of two main parts for influence maximization: (1) seed selection and (2) local community spreading. The first part of CFIN is the extraction of seed nodes from communities which obtained the running of the community detection algorithm. In this part, to decrease computational complexity effectively and scatter seed nodes into communities, the meaningful communities are selected. The second part consists of the influence spread inside communities that are independent of each other. In this part, the final seed nodes entered to distribute the local spreading by the use of a simple path inside communities. To study the performance of the CFIN, several experiments have been conducted on some real and synthetic networks. The experimental simulations on the CFIN, in comparison with other algorithms, confirm the superiority of the CFIN in terms of influence spread, coverage ratio, running time, and Dolan-Moré performance profile.

     

Estimation algorithm for counting periodic orbits in complex social networks

Complex networks can store information in form of periodic orbits (cycles) existing in the network. This cycle-based approach although computationally intensive, it provided us with useful information about the behavior and connectivity of the network. Social networks in most works are treated like any complex network with minimal sociological features modeled. Hence the cycle distribution will suggest the true capacity of this social network to store information. Counting cycles in complex networks is an NP-hard problem. This work proposed an efficient algorithm based on statistical mechanical based Belief Propagation (BP) algorithm to compute cycles in different complex networks using a phenomenological Gaussian distribution of cycles. The enhanced BP algorithm was applied and tested on different networks and the results showed that our model accurately approximated the cycles distribution of those networks, and that the best accuracy was obtained for the random network. In addition, a clear improvement was achieved in the cycles computation time. In some cases the execution time was reduced by up to 88 % compared to the original BP algorithm.     

社交网络影响的不良信息扩散建模及最优控制策略

针对已有不良信息传播模型没有考虑不同社交网络间信息扩散情况,利用图论中的连通性原理,建立了多个社交网络间不良信息扩散的动力学模型,并且将优化控制理论应用到模型中。通过最优控制原理,证明了最优控制策略的存在性,进一步得到了不良信息扩散的优化控制模型。实验结果表明,引入优化控制措施可以有效抑制不良信息扩散规模,而且控制策略的强度可以根据需要进行动态调整。另外,通过模拟不同社交网络间是否有信息相互传递,发现社交网络间的信息传递会增大不良信息扩散的规模和速度。     

SLPA-based parallel overlapping community detection approach in large complex social networks

Multimedia Tools and Applications - Performance improvement of community detection is an NP problem in large social networks analysis where by integrating the overlapped communities’...     

Using leg muscles as shock absorbers: theoretical predictions and experimental results of drop landing performance

The use of muscles as power dissipators is investigated in this study, both from the modellistic and the experimental points of view. Theoretical predictions of the drop landing manoeuvre for a range of initial conditions have been obtained by accounting for the mechanical characteristics of knee extensor muscles, the limb geometry and assuming maximum neural activation. Resulting dynamics have been represented in the phase plane (vertical displacement versus speed) to better classify the damping performance. Predictions of safe landing in sedentary subjects were associated to dropping from a maximum (feet) height of 1.6-2.0 m (about 11 m on the moon). Athletes can extend up to 2.6-3.0 m, while for obese males (m = 100 kg, standard stature) the limit should reduce to 0.9-1.3 m. These results have been calculated by including in the model the estimated stiffness of the ‘global elastic elements’ acting below the squat position. Experimental landings from a height of 0.4, 0.7, 1.1 m (sedentary males (SM) and male (AM) and female (AF) athletes from the alpine ski national team) showed dynamics similar to the model predictions. While the peak power (for a drop height of about 0.7 m) was similar in SM and AF (AM shows a + 40% increase, about 33 W/kg), AF stopped the downward movement after a time interval (0.219±0.030 s) from touch-down 20% significantly shorter than SM. Landing strategy and the effect of anatomical constraints are discussed in the paper.     

Local adaptive mechanism and hierarchic social entropy in opinion formation on complex networks

In this paper we study the opinion formation using co-evolution model, in which network's structure interacts with the nodes' opinion. A local adaptive model is proposed to investigate the effects of local information on the opinion formation, including local rewiring and influencing mechanism. The results show that under the local adaptive mechanism, systems could reach steady state of consensus or fragmentation. Considering the local influencing factor only, we find that transition occurs under proper condition and local parameter affects the transition point. At last, the diversity of opinions is considered, and hierarchic social entropy is used as a macroscopic measurement which is proved to be well.