Scalable Preference Aggregation in Social Networks

In social choice theory, preference aggregation refers to computing an aggregate preference over a set of alternatives given individual preferences of all the agents. In real-world scenarios, it may not be feasible to gather preferences from all the agents. Moreover, determining the aggregate preference is computationally intensive. In this paper, we show that the aggregate preference of the agents in a social network can be computed efficiently and with sufficient accuracy using preferences elicited from a small subset of critical nodes in the network. Our methodology uses a model developed based on real-world data obtained using a survey on human subjects, and exploits network structure and homophily of relationships. Our approach guarantees good performance for aggregation rules that satisfy a property which we call expected weak insensitivity. We demonstrate empirically that many practically relevant aggregation rules satisfy this property. We also show that two natural objective functions in this context satisfy certain properties, which makes our methodology attractive for scalable preference aggregation over large scale social networks. We conclude that our approach is superior to random polling while aggregating preferences related to individualistic metrics, whereas random polling is acceptable in the case of social metrics.

Optimal incentive timing strategies for product marketing on social networks

We consider the problem of devising incentive strategies for viral marketing of a product. In particular, we assume that the seller can influence penetration of the product by offering two incentive programs: a) direct incentives to potential buyers (influence) and b) referral rewards for customers who influence potential buyers to make the purchase (exploit connections). The problem is to determine the optimal timing of these programs over a finite time horizon. In contrast to algorithmic perspective popular in the literature, we take a mean-field approach and formulate the problem as a continuous-time deterministic optimal control problem. We show that the optimal strategy for the seller has a simple structure and can take both forms, namely, influence-and-exploit and exploit-and-influence. We also show that in some cases it may optimal for the seller to deploy incentive programs mostly for low degree nodes. We support our theoretical results through numerical studies and provide practical insights by analyzing various scenarios.

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the `feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.

A Multi-phase Approach for Improving Information Diffusion in Social Networks

For maximizing influence spread in a social network, given a certain budget on the number of seed nodes, we investigate the effects of selecting and activating the seed nodes in multiple phases. In particular, we formulate an appropriate objective function for two-phase influence maximization under the independent cascade model, investigate its properties, and propose algorithms for determining the seed nodes in the two phases. We also study the problem of determining an optimal budget-split and delay between the two phases.

QoS Performance Analysis of Routing Protocols in Vehicular Ad-hoc Networks

Vehicular Ad-hoc Networks (VANET), is a type of wireless ad-hoc network that aims to provide communication among vehicles. A key characteristic of VANETs is the very high mobility of nodes that result in a frequently changing topology along with the frequent breakage and linkage of the paths among the nodes involved. These characteristics make the Quality of Service (QoS) requirements in VANET a challenging issue. In this paper we characterize the performance available to applications in infrastructureless VANETs in terms of path holding time, path breakage probability and per session throughput as a function of various vehicle densities on road, data traffic rate and number of connections formed among vehicles by making use of table-driven and on-demand routing algorithms. Several QoS constraints in the applications of infrastructureless VANETs are observed in the results obtained.

Accelerating Information Diffusion in Social Networks Under the Susceptible-Infected-Susceptible Epidemic Model

Standard Susceptible-Infected-Susceptible (SIS) epidemic models assume that a message spreads from the infected to the susceptible nodes due to only susceptible-infected epidemic contact. We modify the standard SIS epidemic model to include direct recruitment of susceptible individuals to the infected class at a constant rate (independent of epidemic contacts), to accelerate information spreading in a social network. Such recruitment can be carried out by placing advertisements in the media. We provide a closed form analytical solution for system evolution in the proposed model and use it to study campaigning in two different scenarios. In the first, the net cost function is a linear combination of the reward due to extent of information diffusion and the cost due to application of control. In the second, the campaign budget is fixed. Results reveal the effectiveness of the proposed system in accelerating and improving the extent of information diffusion. Our work is useful for devising effective strategies for product marketing and political/social-awareness/crowd-funding campaigns that target individuals in a social network.

Campaigning in Heterogeneous Social Networks: Optimal Control of SI Information Epidemics

We study the optimal control problem of maximizing the spread of an information epidemic on a social network. Information propagation is modeled as a susceptible-infected (SI) process, and the campaign budget is fixed. Direct recruitment and word-of-mouth incentives are the two strategies to accelerate information spreading (controls). We allow for multiple controls depending on the degree of the nodes/individuals. The solution optimally allocates the scarce resource over the campaign duration and the degree class groups. We study the impact of the degree distribution of the network on the controls and present results for Erdos-Renyi and scale-free networks. Results show that more resource is allocated to high-degree nodes in the case of scale-free networks, but medium-degree nodes in the case of Erdos-Renyi networks. We study the effects of various model parameters on the optimal strategy and quantify the improvement offered by the optimal strategy over the static and bang-bang control strategies. The effect of the time-varying spreading rate on the controls is explored as the interest level of the population in the subject of the campaign may change over time. We show the existence of a solution to the formulated optimal control problem, which has nonlinear isoperimetric constraints, using novel techniques that is general and can be used in other similar optimal control problems. This work may be of interest to political, social awareness, or crowdfunding campaigners and product marketing managers, and with some modifications may be used for mitigating biological epidemics.

Cost effective campaigning in social networks

Campaigners are increasingly using online social networking platforms for promoting products, ideas and information. A popular method of promoting a product or even an idea is incentivizing individuals to evangelize the idea vigorously by providing them with referral rewards in the form of discounts, cash backs, or social recognition. Due to budget constraints on scarce resources such as money and manpower, it may not be possible to provide incentives for the entire population, and hence incentives need to be allocated judiciously to appropriate individuals for ensuring the highest possible outreach size. We aim to do the same by formulating and solving an optimization problem using percolation theory. In particular, we compute the set of individuals that are provided incentives for minimizing the expected cost while ensuring a given outreach size. We also solve the problem of computing the set of individuals to be incentivized for maximizing the outreach size for given cost budget. The optimization problem turns out to be non trivial; it involves quantities that need to be computed by numerically solving a fixed point equation. Our primary contribution is, that for a fairly general cost structure, we show that the optimization problems can be solved by solving a simple linear program. We believe that our approach of using percolation theory to formulate an optimization problem is the first of its kind. (C) 2016 Elsevier B.V. All rights reserved.

PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach

Online Social Networks (OSNs) facilitate to create and spread information easily and rapidly, influencing others to participate and propagandize. This work proposes a novel method of profiling Influential Blogger (IB) based on the activities performed on one's blog documents who influences various other bloggers in Social Blog Network (SBN). After constructing a social blogging site, a SBN is analyzed with appropriate parameters to get the Influential Blog Power (IBP) of each blogger in the network and demonstrate that profiling IB is adequate and accurate. The proposed Profiling Influential Blogger (PIB) Algorithm survival rate of IB is high and stable. (C) 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Turnout Intention and Social Networks

How can networking affect the turnout in an election? We present a simple model to explain turnout as a result of a dynamic process of formation of the intention to vote within Erdös-Renyi random networks. Citizens have fixed preferences for one of two parties and are embedded in a given social network. They decide whether or not to vote on the basis of the attitude of their immediate contacts. They may simply follow the behavior of the majority (followers) or make an adaptive local calculus of voting (Downsian behavior). So they either have the intention of voting when the majority of their neighbors are willing to vote too, or they vote when they perceive in their social neighborhood that elections are "close". We study the long run average turnout, interpreted as the actual turnout observed in an election. Depending on the combination of values of the two key parameters, the average connectivity and the probability of behaving as a follower or in a Downsian fashion, the system exhibits monostability (zero turnout), bistability (zero turnout and either moderate or high turnout) or tristability (zero, moderate and high turnout). This means, in particular, that for a wide range of values of both parameters, we obtain realistic turnout rates, i.e. between 50% and 90%.

Prosocial norms and degree heterogeneity in social networks

We provide empirical evidence to support the claims that social diversity promotes prosocial behavior. We elicit a real-life social network and its members’ adherence to a social norm, namely inequity aversion. The data reveal a positive relationship between subjects’ prosociality and several measures of centrality. This result is in line with the theoretical literature that relates the evolution of social norms to the structure of social interactions and argues that central individuals are crucial for the emergence of prosocial behavior.

Networks for Knowledge: Social Media enthuses students of Early Years Education

Networks for Knowledge (n4k Ltd) are a Work Based Learning organisation which specialises in Early Years Education. Training and Development Manager, Elaine Wareing has developed the use of Facebook and Twitter to promote peer learning and interaction beyond the classroom. It also allows trainers to discuss ideas and challengers with a wider group of learners. This has allowed practitioners across a wide geographical area to share their thoughts and ideas together on some of the subjects relating to early years practice.

Multifuncionalidad rural y percepción social en el País Vasco: un reconocimiento pendiente

[ES] Los planes de desarrollo rural del País Vasco se apoyan en gran medida en sus potencialidades naturales, paisajísticas y productivas dirigidas a un urbano que vive en la proximidad. La apreciación de la calidad del entorno rural por parte del ciudadano, por tanto, es fundamental para el progreso de las zonas rurales y supone implícitamente el reconocimiento de la multifuncionalidad de este medio. El urbano, sin embargo, aunque aprecia el entorno natural de los espacios rurales desconoce la función del agricultor en la conservación de este medio siendo la productiva la única aportación que liga a su actividad. El desarrollo rural ha de pasar por el reconocimiento social y para ello es necesario informar y educar al ciudadano.

Envejecimiento, caserío y atención social: la familia asume el cuidado del mayor

[ES] La desestructuración familiar que en España ha supuesto un éxodo rural intensivo queda en evidencia varias décadas después, una vez que la población que decidió continuar residiendo en el medio rural ha envejecido. El anciano que habita en el medio rural carece a menudo del apoyo familiar necesario para hacer frente a las barreras físicas y sociales que la edad y el aislamiento le imponen mientras los servicios públicos no se adecúan a los caracteres propios de este medio. El habitante del caserío vasco, dada la proximidad del medio urbano, ha sufrido en menor medida las consecuencias del éxodo rural, manteniéndose más íntegra la red de apoyo familiar o, en todo caso, viéndose completada con la presencia del que acudió al medio urbano. Como consecuencia, es la familia la que asume el cuidado del mayor, cumpliendo éste su deseo de residir en casa hasta el final de su vida.

Identifying social determinants of urban low carbon transitions: the case of energy transition in Bilbao, Basque Country

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