1,021 research outputs found
Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency
Given a social network G and a constant k, the influence maximization problem
asks for k nodes in G that (directly and indirectly) influence the largest
number of nodes under a pre-defined diffusion model. This problem finds
important applications in viral marketing, and has been extensively studied in
the literature. Existing algorithms for influence maximization, however, either
trade approximation guarantees for practical efficiency, or vice versa. In
particular, among the algorithms that achieve constant factor approximations
under the prominent independent cascade (IC) model or linear threshold (LT)
model, none can handle a million-node graph without incurring prohibitive
overheads.
This paper presents TIM, an algorithm that aims to bridge the theory and
practice in influence maximization. On the theory side, we show that TIM runs
in O((k+\ell) (n+m) \log n / \epsilon^2) expected time and returns a
(1-1/e-\epsilon)-approximate solution with at least 1 - n^{-\ell} probability.
The time complexity of TIM is near-optimal under the IC model, as it is only a
\log n factor larger than the \Omega(m + n) lower-bound established in previous
work (for fixed k, \ell, and \epsilon). Moreover, TIM supports the triggering
model, which is a general diffusion model that includes both IC and LT as
special cases. On the practice side, TIM incorporates novel heuristics that
significantly improve its empirical efficiency without compromising its
asymptotic performance. We experimentally evaluate TIM with the largest
datasets ever tested in the literature, and show that it outperforms the
state-of-the-art solutions (with approximation guarantees) by up to four orders
of magnitude in terms of running time. In particular, when k = 50, \epsilon =
0.2, and \ell = 1, TIM requires less than one hour on a commodity machine to
process a network with 41.6 million nodes and 1.4 billion edges.Comment: Revised Sections 1, 2.3, and 5 to remove incorrect claims about
reference [3]. Updated experiments accordingly. A shorter version of the
paper will appear in SIGMOD 201
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
Inefficiencies in Digital Advertising Markets
Digital advertising markets are growing and attracting increased scrutiny. This article explores four market inefficiencies that remain poorly understood: ad effect measurement, frictions between and within advertising channel members, ad blocking, and ad fraud. Although these topics are not unique to digital advertising, each manifests in unique ways in markets for digital ads. The authors identify relevant findings in the academic literature, recent developments in practice, and promising topics for future research
Fairness-aware Competitive Bidding Influence Maximization in Social Networks
Competitive Influence Maximization (CIM) has been studied for years due to
its wide application in many domains. Most current studies primarily focus on
the micro-level optimization by designing policies for one competitor to defeat
its opponents. Furthermore, current studies ignore the fact that many
influential nodes have their own starting prices, which may lead to inefficient
budget allocation. In this paper, we propose a novel Competitive Bidding
Influence Maximization (CBIM) problem, where the competitors allocate budgets
to bid for the seeds attributed to the platform during multiple bidding rounds.
To solve the CBIM problem, we propose a Fairness-aware Multi-agent Competitive
Bidding Influence Maximization (FMCBIM) framework. In this framework, we
present a Multi-agent Bidding Particle Environment (MBE) to model the
competitors' interactions, and design a starting price adjustment mechanism to
model the dynamic bidding environment. Moreover, we put forward a novel
Multi-agent Competitive Bidding Influence Maximization (MCBIM) algorithm to
optimize competitors' bidding policies. Extensive experiments on five datasets
show that our work has good efficiency and effectiveness.Comment: IEEE Transactions on Computational Social Systems (TCSS), 2023, early
acces
Corporate Social Responsibility: Organization's Pull And Push Strategy
Corporate Social Responsibility, though not a new concept to both foreign and indigenous organizations operating within the shore of Nigeria and as well as researchers in the management field, however, the strategy been utilized is not gaining required recognition hence the failure of the practice to deliver its intended benefits. Despite the various economic challenges like electricity, security and corruption bedeviling the country leading to exodus of many manufacturing organizations out of Nigeria, one of the major organizations weathering storms and becoming stronger is Dangote Cement. The CSR initiatives of the firm is seen as one of its secret hence the study of the strategy been used. A synthesis of push and pull strategy that is robust and all stakeholder inclusive is adopted by it, pushing its product to the market while also attracting the people to it, hence its success story in the sector. Keywords: Corporate Social Responsibility, Organizations, Pull and Push strategy, Sustainability, Marketing Mix, Situation Response. DOI: 10.7176/EJBM/11-6-1
Branding in a Hyperconnected World: Refocusing Theories and Rethinking Boundaries
Technological advances have resulted in a hyperconnected world, requiring a reassessment of branding research from the perspectives of firms, consumers, and society. Brands are shifting away from single ownership to shared ownership, as heightened access to information and people is allowing more stakeholders to cocreate brand meanings and experiences alongside traditional brand owners and managers. Moreover, hyperconnectivity has allowed existing brands to expand their geographic reach and societal roles, while new types of branded entities (ideas, people, places, and organizational brands) are further stretching the branding space. To help establish a new branding paradigm that accounts for these changes, the authors address the following questions: (1) What are the roles and functions of brands?, (2) How is brand value (co)created?, and (3) How should brands be managed? Throughout the article, the authors also identify future research issues that require scholarly attention, with the aim of aligning branding theory and practice with the realities of a hyperconnected world
Data Analytics for Crisis Management: A Case Study of Sharing Economy Services in the COVID-19 Pandemic
This dissertation study aims to analyze the role of data-driven decision-making in sharing economy during the COVID-19 pandemic as a crisis management tool. In the twenty-first century, when applying analytical tools has become an essential component of business decision-making, including operations on crisis management, data analytics is an emerging field. To carry out corporate strategies, data-driven decision-making is seen as a crucial component of business operations. Data analytics can be applied to benefit-cost evaluations, strategy planning, client engagement, and service quality. Data forecasting can also be used to keep an eye on business operations and foresee potential risks. Risk Management and planning are essential for allocating the necessary resources with minimal cost and time and to be ready for a crisis. Hidden market trends and customer preferences can help companies make knowledgeable business decisions during crises and recessions. Each company should manage operations and response during emergencies, a path to recovery, and prepare for future similar events with appropriate data management tools. Sharing economy is part of social commerce, that brings together individuals who have underused assets and who want to rent those assets short-term. COVID-19 has emphasized the need for digital transformation. Since the pandemic began, the sharing economy has been facing challenges, while market demand dropped significantly. Shelter-in-Place and Stay-at-Home orders changed the way of offering such sharing services. Stricter safety procedures and the need for a strong balance sheet are the key take points to surviving during this difficult health crisis. Predictive analytics and peer-reviewed articles are used to assess the pandemic\u27s effects. The approaches chosen to assess the research objectives and the research questions are the predictive financial performance of Uber & Airbnb, bibliographic coupling, and keyword occurrence analyses of peer-reviewed works about the influence of data analytics on the sharing economy. The VOSViewer Bibliometric software program is utilized for computing bibliometric analysis, RapidMiner Predictive Data Analytics for computing data analytics, and LucidChart for visualizing data
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