Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models

The steady growth of graph data from social networks has resulted in wide-spread research in finding solutions to the influence maximization problem. In this paper, we propose a holistic solution to the influence maximization (IM) problem. (1) We introduce an opinion-cum-interaction (OI) model that closely mirrors the real-world scenarios. Under the OI model, we introduce a novel problem of Maximizing the Effective Opinion (MEO) of influenced users. We prove that the MEO problem is NP-hard and cannot be approximated within a constant ratio unless P=NP. (2) We propose a heuristic algorithm OSIM to efficiently solve the MEO problem. To better explain the OSIM heuristic, we first introduce EaSyIM - the opinion-oblivious version of OSIM, a scalable algorithm capable of running within practical compute times on commodity hardware. In addition to serving as a fundamental building block for OSIM, EaSyIM is capable of addressing the scalability aspect - memory consumption and running time, of the IM problem as well. Empirically, our algorithms are capable of maintaining the deviation in the spread always within 5% of the best known methods in the literature. In addition, our experiments show that both OSIM and EaSyIM are effective, efficient, scalable and significantly enhance the ability to analyze real datasets.Comment: ACM SIGMOD Conference 2016, 18 pages, 29 figure

Transforming Physical Therapy Education Through the Use of Social Network Analysis

PURPOSE Social Network Analysis (SNA) is a quantitative method to study the patterning and effect of relationships and how individual connections form into social structures that influence outcomes of the group. The purpose of this study is to explain the underpinnings of SNA and its application to PT education for student success and faculty development. This study is innovative and will be beneficial to the profession because there is currently no published literature exploring how PT education is influenced by social structures. METHODS/DESCRIPTION The aims of this study are to describe SNA and outline its potential to transform PT education. A literature search to identify studies that provide a theoretical framework and uses of SNA was performed using the following databases: CINAHL, Academic Search Complete, MEDLINE, Scopus and ProQuest. Studies in education, organizational management, and sociology were reviewed. RESULTS/OUTCOMES Only recently has SNA been identified as relevant in medical education despite wide use in the business and military sectors. Social Network Analysis focuses on relational data that explores 1) influences of direct and indirect ties, 2) structures and composition for enhancing or constraining information spread, and 3) impact of one’s position in the network. There is evidence network size is just as important as the depth and breadth of experiences each member-connection brings. Given that education and teaching are social in nature, opportunities for the use of SNA in PT education are abundant. It could shed light on the relationships between students, faculty and even entities on social media platforms. Early network analysis of a PT cohort could transform the PT educational experience through early identification to remediate students with ineffective networks for collaboration, information sharing and support. Another application includes measuring the flow of information and noting which students are brokering information that aids in maximizing the collaborations for team-based care. An effective network could also positively impact PT faculty and may reduce tension between the requirements of teaching, scholarly activity and service for progress toward goals of promotion and tenure. Network collaboration was shown by medicine faculty to provide vital knowledge and maximizing scholarly activity. Exploring aspects of PT faculty professional networks could lead to valuable information to balance the composition of network members’ expertise and leverage connections. CONCLUSIONS/RELEVANCE The value of SNA includes the ability to quantify relationships between people and explore how connections emerge as an asset or constraint. Adding measurement of relational factors to individual information could significantly increase the evidence to guide our understanding of actions for PT students and faculty success. Currently, SNA has not been reported in PT education literature but is a methodology that will produce substantial insights to transforming PT education. FUNDING SOURCE Funding provided by the College of Allied Health Professions, University of Nebraska Medical Center and Education Section of the American Physical Therapy Associatio

Towards Profit Maximization for Online Social Network Providers

Online Social Networks (OSNs) attract billions of users to share information and communicate where viral marketing has emerged as a new way to promote the sales of products. An OSN provider is often hired by an advertiser to conduct viral marketing campaigns. The OSN provider generates revenue from the commission paid by the advertiser which is determined by the spread of its product information. Meanwhile, to propagate influence, the activities performed by users such as viewing video ads normally induce diffusion cost to the OSN provider. In this paper, we aim to find a seed set to optimize a new profit metric that combines the benefit of influence spread with the cost of influence propagation for the OSN provider. Under many diffusion models, our profit metric is the difference between two submodular functions which is challenging to optimize as it is neither submodular nor monotone. We design a general two-phase framework to select seeds for profit maximization and develop several bounds to measure the quality of the seed set constructed. Experimental results with real OSN datasets show that our approach can achieve high approximation guarantees and significantly outperform the baseline algorithms, including state-of-the-art influence maximization algorithms.Comment: INFOCOM 2018 (Full version), 12 page

Exact Computation of Influence Spread by Binary Decision Diagrams

Evaluating influence spread in social networks is a fundamental procedure to estimate the word-of-mouth effect in viral marketing. There are enormous studies about this topic; however, under the standard stochastic cascade models, the exact computation of influence spread is known to be #P-hard. Thus, the existing studies have used Monte-Carlo simulation-based approximations to avoid exact computation. We propose the first algorithm to compute influence spread exactly under the independent cascade model. The algorithm first constructs binary decision diagrams (BDDs) for all possible realizations of influence spread, then computes influence spread by dynamic programming on the constructed BDDs. To construct the BDDs efficiently, we designed a new frontier-based search-type procedure. The constructed BDDs can also be used to solve other influence-spread related problems, such as random sampling without rejection, conditional influence spread evaluation, dynamic probability update, and gradient computation for probability optimization problems. We conducted computational experiments to evaluate the proposed algorithm. The algorithm successfully computed influence spread on real-world networks with a hundred edges in a reasonable time, which is quite impossible by the naive algorithm. We also conducted an experiment to evaluate the accuracy of the Monte-Carlo simulation-based approximation by comparing exact influence spread obtained by the proposed algorithm.Comment: WWW'1

Method maximizing the spread of influence in directed signed weighted graphs

We propose a new method for maximizing the spread of influence, based on the identification of significant factors of the total energy of a control system. The model of a socio-economic system can be represented in the form of cognitive maps that are directed signed weighted graphs with cause-and-effect relationships and cycles. Identification and selection of target factors and effective control factors of a system is carried out as a solution to the optimal control problem. The influences are determined by the solution to optimization problem of maximizing the objective function, leading to matrix symmetrization. The gear-ratio symmetrization is based on computing the similarity extent of fan-beam structures of the influence spread of vertices v_i and v_j to all other vertices. This approach provides the real computational domain and correctness of solving the optimal control problem. In addition, it does not impose requirements for graphs to be ordering relationships, to have a matrix of special type or to fulfill stability conditions. In this paper, determination of new metrics of vertices, indicating and estimating the extent and the ability to effectively control, are likewise offered. Additionally, we provide experimental results over real cognitive models in support