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This paper qualitatively looks at the relationship between Community relations and rural development with special reference to oil producing communities in the Niger-Delta. It points out the adverse effects of oil exploitation and exploration by the multinational oil companies on the oil producing rural communities of the Niger-Delta. The oil producing rural communities are faced with mass unemployment due to the problem of environmental degradation, ecological problems are not adequately addressed, roads, electricity, water, education and health facilities are in terrible condition, youth restiveness and all sorts of crimes due to little or no community relations; thereby making the condition of living not conducive for the local people. This negligence has left the operations of the multinational oil companies in chaos due to the activities of the restive youths. As checks to these developmental challenges the oil companies consciously carried out some developmental works in the oil producing rural communities yet the problems of pipeline bombing, vandalism, etc continues. The paper believes the multinational developmental efforts were not appreciated because the power structures of the oil producing rural communities were not consulted. The paper therefore stressed the need for consultation to determine the felt-needs of the local people to foster good neighbourliness between the oil companies and the oil producing communities.

At the end students will be able to:• Compare the differences between influence and lobbying in a democratic and digi-tal context,• Reveal the psychological dimensions of influence and its expansion to digital tools,• Identify the protagonists and their activities,• Elaborate strong arguments relying on data and expert opinions,• Describe the process for elaborating norms and laws,• Discover the public action process, in a parliament as well as in civil society,• Ask ethical and critical questions about lobbying regulations.Course requirements: basic notions on the most important theories of information and communication, and competences in sociology. A good knowledge of actuality is necessary. ; info:eu-repo/semantics/published

International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.

International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.

International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.

International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.

International audience ; Finding a set of users that can maximize the spread of information in a social network is an important problem in social media analysis-being a critical part of several realworld applications such as viral marketing, political advertising and epidemiology. Although influence maximization has been studied extensively in the past, the majority of works focus on the algorithmic aspect of the problem, overlooking several practical improvements that can be derived by data-driven observations or the inclusion of machine learning. The main challenges of realistic influence maximization is on the one hand the computational demand of the diffusion models' repetitive simulations, and on the other the accuracy of the estimated influence spread. In this work, we propose CELFIE, an influence maximization method that utilizes learnt influence representations from diffusion cascades to overcome the use of diffusion models. It comprises of two parts. The first is based on INF2VEC, an unsupervised learning model that embeds influence relationships between nodes from a set of diffusion cascades. We create a new version of the model, based on observations from influence analysis on a large scale dataset, to match the scalability needs and the purpose of influence maximization. The second part capitalizes on the learned representations to redefine the traditional live-edge model sampling for the computation of the marginal gain. For evaluation, we apply our method in the Sina Weibo and Microsoft Academic Graph datasets, two large scale networks accompanied by diffusion cascades. We observe that our algorithm outperforms various baseline methods in terms of seed set quality and speed. In addition, the proposed INF2VEC modification for influence maximization provides substantial computational advantages in the price of a minuscule loss in the influence spread.

This article considers how much outside influence matters to the constitutional analysis of state politics. It defends the political community principle applied in Bluman v. Federal Election Comm'n as an exception to the otherwise universal speaker-neutrality rule of Citizens United v. Federal Election Comm'n. It draws parallels between efforts to police national and state boundaries in politics, and the competing rights claims of outsiders to cross those boundaries and participate fully in domestic politics. The article suggests that the structural constitutional principle of political community supports certain state regulations of outside influence across a range of political activities. Part I reviews the structural and historical basis for the constitutional concern about outside influence. Part II considers the gnarled doctrinal roots of Bluman, and how they might help support state, as well as national, safeguards against outside influence. Part III proposes an important state interest in regulating, but not excluding, outside influence in state and local politics, and suggests some applications of the principle to outside influence in several spheres of political activity.

Presents letters to the editor reacting to Karel van Wolfersen's review of Pat Choate's book "Agents of Influence," which cites lobbying or influence peddling in both the Japanese and American political contexts.

with the syllabus, students will be able to:Compare the differences between influence and lobbying in a democratic and digital context,Reveal the psychological dimensions of influence and its expansion to digital tools,Identify the protagonists and their activities,• Elaborate strong arguments relying on data and expert opinions,Describe the process for elaborating norms and laws,Discover the public action process, in a parliament as well as in civil society,• Ask ethical and critical questions about lobbying regulations. ; info:eu-repo/semantics/published ; 1

This PhD research investigates de facto influence of EU agencies on the development of EU policies. Based on a case-study approach and expert interviews, the European Centre for Disease Prevention and Control as well as the European Chemicals Agency are analyzed. The main findings are that EU agencies, albeit to varying degrees, do influence the content of policy proposals developed by the European Commission. However, the Commission does fully exercise its formal power to decide on the content of proposals. Only under willing guidance by the Commission, do EU agencies increase influence. While European governance inevitably involves technocratic characteristics, it should not be directly interpreted as a deficit of democracy or legitimacy in the EU.

This report examines Iran's influence in Iraq post-Saddam. This is in large part because the dominant parties in Iraq have long-standing ideological, political, and religious sectarian ties to Tehran. A key U.S. concern is that Iran, seeking to ensure the political prospects of its proteges, supports Shiite militias that are committing sectarian violence. Since December 2006, the Administration has tried to reverse Iranian influence in Iraq while also engaging Iran diplomatically on Iraq.

International audience ; We study a stochastic model of influence where agents have "yes" or "no" inclinations on some issue, and opinions may change due to mutual influence among the agents. Each agent independently aggregates the opinions of the other agents and possibly herself. We study influence processes modeled by ordered weighted averaging operators, which are anonymous: they only depend on how many agents share an opinion. For instance, this allows to study situations where the influence process is based on majorities, which are not covered by the classical approach of weighted averaging aggregation. We find a necessary and sufficient condition for convergence to consensus and characterize outcomes where the society ends up polarized. Our results can also be used to understand more general situations, where ordered weighted averages are only used to some extent. Furthermore, we apply our results to fuzzy linguistic quantifiers, i.e., expressions like "most" or "at least a few".

International audience ; We study a stochastic model of influence where agents have "yes" or "no" inclinations on some issue, and opinions may change due to mutual influence among the agents. Each agent independently aggregates the opinions of the other agents and possibly herself. We study influence processes modeled by ordered weighted averaging operators, which are anonymous: they only depend on how many agents share an opinion. For instance, this allows to study situations where the influence process is based on majorities, which are not covered by the classical approach of weighted averaging aggregation. We find a necessary and sufficient condition for convergence to consensus and characterize outcomes where the society ends up polarized. Our results can also be used to understand more general situations, where ordered weighted averages are only used to some extent. Furthermore, we apply our results to fuzzy linguistic quantifiers, i.e., expressions like "most" or "at least a few".

International audience ; We study a stochastic model of influence where agents have "yes" or "no" inclinations on some issue, and opinions may change due to mutual influence among the agents. Each agent independently aggregates the opinions of the other agents and possibly herself. We study influence processes modeled by ordered weighted averaging operators, which are anonymous: they only depend on how many agents share an opinion. For instance, this allows to study situations where the influence process is based on majorities, which are not covered by the classical approach of weighted averaging aggregation. We find a necessary and sufficient condition for convergence to consensus and characterize outcomes where the society ends up polarized. Our results can also be used to understand more general situations, where ordered weighted averages are only used to some extent. Furthermore, we apply our results to fuzzy linguistic quantifiers, i.e., expressions like "most" or "at least a few".