A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks

Our study concerns an important current problem, that of diffusion of information in social networks. This problem has received significant attention from the Internet research community in the recent times, driven by many potential applications such as viral marketing and sales promotions. In this paper, we focus on the target set selection problem, which involves discovering a small subset of influential players in a given social network, to perform a certain task of information diffusion. The target set selection problem manifests in two forms: 1) top-k nodes problem and 2) lambda-coverage problem. In the top-k nodes problem, we are required to find a set of k key nodes that would maximize the number of nodes being influenced in the network. The lambda-coverage problem is concerned with finding a set of k key nodes having minimal size that can influence a given percentage lambda of the nodes in the entire network. We propose a new way of solving these problems using the concept of Shapley value which is a well known solution concept in cooperative game theory. Our approach leads to algorithms which we call the ShaPley value-based Influential Nodes (SPINs) algorithms for solving the top-k nodes problem and the lambda-coverage problem. We compare the performance of the proposed SPIN algorithms with well known algorithms in the literature. Through extensive experimentation on four synthetically generated random graphs and six real-world data sets (Celegans, Jazz, NIPS coauthorship data set, Netscience data set, High-Energy Physics data set, and Political Books data set), we show that the proposed SPIN approach is more powerful and computationally efficient. Note to Practitioners-In recent times, social networks have received a high level of attention due to their proven ability in improving the performance of web search, recommendations in collaborative filtering systems, spreading a technology in the market using viral marketing techniques, etc. It is well known that the interpersonal relationships (or ties or links) between individuals cause change or improvement in the social system because the decisions made by individuals are influenced heavily by the behavior of their neighbors. An interesting and key problem in social networks is to discover the most influential nodes in the social network which can influence other nodes in the social network in a strong and deep way. This problem is called the target set selection problem and has two variants: 1) the top-k nodes problem, where we are required to identify a set of k influential nodes that maximize the number of nodes being influenced in the network and 2) the lambda-coverage problem which involves finding a set of influential nodes having minimum size that can influence a given percentage lambda of the nodes in the entire network. There are many existing algorithms in the literature for solving these problems. In this paper, we propose a new algorithm which is based on a novel interpretation of information diffusion in a social network as a cooperative game. Using this analogy, we develop an algorithm based on the Shapley value of the underlying cooperative game. The proposed algorithm outperforms the existing algorithms in terms of generality or computational complexity or both. Our results are validated through extensive experimentation on both synthetically generated and real-world data sets.

Closing the cultural distance gap: managing value-based knowledge in MNC by using MEP

Today more than ever, generating and managing knowledge is an essential source of competitive advantage for every organization, and particularly for Multinational corporations (MNC). However, despite the undisputed agreement about the importance of creating and managing knowledge, there are still a large number of corporations that act unethically or illegally. Clearly, there is a lack of attention in gaining more knowledge about the management of ethical knowledge in organizations. This paper refers to value-based knowledge, as the process of recognise and manage those values that stand at the heart of decision-making and action in organizations. In order to support MNCs in implementing value-based knowledge process, the managerial ethical profile (MEP) has been presented as a valuable tool to facilitate knowledge management process at both the intra-organizational network level and at the inter-organizational network level.

Using value-based partnerships to support community engaged learning : benefits, challenges, stakeholder perspectives

Increasingly, the not-for-profit sector, as an emerging contributor to the creative economy, is creating a context for engaging creative practitioners in developing solutions to complex problems, triggering a demand for skills and knowledge needed to address this complexity. Across the university and community contexts alternative models of engagement are emerging to support this dynamic. This paper presents a case study of a creative project in which a value-based approach is used to foster a collaborative partnership between community partners and a multidisciplinary team of final year Creative Industries students who in the course of the project developed a range of communication resources, including a social media campaign, an interactive game and a series of short films to support volunteer engagement and leadership initiatives. The paper considers the implications this values approach has for the design of service learning curriculum for multidisciplinary creative teams and the potential it has to support meaningful collaboration between creatives and the not-for-profit sector. It further explores how it impact on student and partner engagement, learning outcomes and the benefits for the partner organisation. The paper concludes that a value-based approach to university-community engagement has the potential to support and enable a greater degree of reciprocity, deeper engagement between stakeholders and greater relevance of the final outcome.

Influence limitation in multi-campaign social networks: A Shapley value based approach

We investigate the problem of influence limitation in the presence of competing campaigns in a social network. Given a negative campaign which starts propagating from a specified source and a positive/counter campaign that is initiated, after a certain time delay, to limit the the influence or spread of misinformation by the negative campaign, we are interested in finding the top k influential nodes at which the positive campaign may be triggered. This problem has numerous applications in situations such as limiting the propagation of rumor, arresting the spread of virus through inoculation, initiating a counter-campaign against malicious propaganda, etc. The influence function for the generic influence limitation problem is non-submodular. Restricted versions of the influence limitation problem, reported in the literature, assume submodularity of the influence function and do not capture the problem in a realistic setting. In this paper, we propose a novel computational approach for the influence limitation problem based on Shapley value, a solution concept in cooperative game theory. Our approach works equally effectively for both submodular and non-submodular influence functions. Experiments on standard real world social network datasets reveal that the proposed approach outperforms existing heuristics in the literature. As a non-trivial extension, we also address the problem of influence limitation in the presence of multiple competing campaigns.

A whole-life value based assessment and optimisation model for high-performance glazed facades'

Façade design is a complex and multi-disciplinary process. One major barrier to devising optimal façade solutions is the lack of a systematic way of evaluating the true social, economic and environmental impacts of a design. Another barrier is the lack of automated design aids to assist decision-making. In this paper, we present our on-going study in developing a whole-life value based multi-objective optimisation model for high-performance façades. The principal outcome of this paper is a multi-objective optimisation model for early-stage façade design. The optimisation technique coupled with other 3rd party software and/or specially developed scripts provide façade designers with an integrated design tool of wide applicability.

value-based multiple software projects scheduling with genetic algorithm

Chinese Acad Sci, ISCAS Lab Internet Software Technologies

Attentional Biases in Value-Based Decision-Making

Humans make decisions in highly complex physical, economic and social environments. In order to adaptively choose, the human brain has to learn about- and attend to- sensory cues that provide information about the potential outcome of different courses of action. Here I present three event-related potential (ERP) studies, in which I evaluated the role of the interactions between attention and reward learning in economic decision-making. I focused my analyses on three ERP components (Chap. 1): (1) the N2pc, an early lateralized ERP response reflecting the lateralized focus of visual; (2) the feedback-related negativity (FRN), which reflects the process by which the brain extracts utility from feedback; and (3) the P300 (P3), which reflects the amount of attention devoted to feedback-processing. I found that learned stimulus-reward associations can influence the rapid allocation of attention (N2pc) towards outcome-predicting cues, and that differences in this attention allocation process are associated with individual differences in economic decision performance (Chap. 2). Such individual differences were also linked to differences in neural responses reflecting the amount of attention devoted to processing monetary outcomes (P3) (Chap. 3). Finally, the relative amount of attention devoted to processing rewards for oneself versus others (as reflected by the P3) predicted both charitable giving and self-reported engagement in real-life altruistic behaviors across individuals (Chap. 4). Overall, these findings indicate that attention and reward processing interact and can influence each other in the brain. Moreover, they indicate that individual differences in economic choice behavior are associated both with biases in the manner in which attention is drawn towards sensory cues that inform subsequent choices, and with biases in the way that attention is allocated to learn from the outcomes of recent choices.

Does Cost-Effectiveness Analysis Have a Role in US Managed Care Drug Formularies? An Empirical Study of Utilization, Costs, Outcomes, and Elasticity of Demand of a Value-Based Formulary

Thesis (Ph.D.)--University of Washington, 2015

Modeling and illustrating requirement prioritization in public e-service development from a value-based perspective

A major problem in e-service development is the prioritization of the requirements of different stakeholders. The main stakeholders are governments and their citizens, all of whom have different and sometimes conflicting requirements. In this paper, the prioritization problem is addressed by combining a value-based approach with an illustration technique. This paper examines the following research question: How can multiple stakeholder requirements be illustrated from a value-based perspective in order to be prioritizable? We used an e-service development case taken from a Swedish municipality to elaborate on our approach. Our contributions are: 1) a model of the relevant domains for requirement prioritization for government, citizens, technology, finances and laws and regulations; and 2) a requirement fulfillment analysis tool (RFA) that consists of a requirement-goal-value matrix (RGV), and a calculation and illustration module (CIM). The model reduces cognitive load, helps developers to focus on value fulfillment in e-service development and supports them in the formulation of requirements. It also offers an input to public policy makers, should they aim to target values in the design of e-services.