70,782 research outputs found
Optimal Resource Allocation Over Time and Degree Classes for Maximizing Information Dissemination in Social Networks
We study the optimal control problem of allocating campaigning resources over
the campaign duration and degree classes in a social network. Information
diffusion is modeled as a Susceptible-Infected epidemic and direct recruitment
of susceptible nodes to the infected (informed) class is used as a strategy to
accelerate the spread of information. We formulate an optimal control problem
for optimizing a net reward function, a linear combination of the reward due to
information spread and cost due to application of controls. The time varying
resource allocation and seeds for the epidemic are jointly optimized. A problem
variation includes a fixed budget constraint. We prove the existence of a
solution for the optimal control problem, provide conditions for uniqueness of
the solution, and prove some structural results for the controls (e.g. controls
are non-increasing functions of time). The solution technique uses Pontryagin's
Maximum Principle and the forward-backward sweep algorithm (and its
modifications) for numerical computations. Our formulations lead to large
optimality systems with up to about 200 differential equations and allow us to
study the effect of network topology (Erdos-Renyi/scale-free) on the controls.
Results reveal that the allocation of campaigning resources to various degree
classes depends not only on the network topology but also on system parameters
such as cost/abundance of resources. The optimal strategies lead to significant
gains over heuristic strategies for various model parameters. Our modeling
approach assumes uncorrelated network, however, we find the approach useful for
real networks as well. This work is useful in product advertising, political
and crowdfunding campaigns in social networks.Comment: 14 + 4 pages, 11 figures. Author's version of the article accepted
for publication in IEEE/ACM Transactions on Networking. This version includes
4 pages of supplementary material containing proofs of theorems present in
the article. Published version can be accessed at
http://dx.doi.org/10.1109/TNET.2015.251254
Evaluation of Enroll America: An Implementation Assessment and Recommendations for Future Outreach Efforts
Families USA spearheaded formation of Enroll America in 2010 to identify newly eligible adults for enrollment in expanded health insurance coverage made possible by the Affordable Care Act. Mathematica is conducting a rigorous evaluation that includes qualitative and quantitative assessments. For its first outreach campaign, Enroll America built infrastructure in 11 states (Arizona, Florida, Georgia, Illinois, Michigan, New Jersey, North Carolina, Ohio, Pennsylvania, Tennessee, and Texas), training staff and engaging volunteers and local partners in outreach to consumers. Areas of recommendation for the second enrollment period include:Expand the number of consumer assistance counselors.Reconsider how resources are allocated in states that have geographically dispersed uninsured.Continue to place a high priority on seeking partnerships, especially with groups connected to key uninsured constituencies
Effects of Time Horizons on Influence Maximization in the Voter Dynamics
In this paper we analyze influence maximization in the voter model with an
active strategic and a passive influencing party in non-stationary settings. We
thus explore the dependence of optimal influence allocation on the time
horizons of the strategic influencer. We find that on undirected heterogeneous
networks, for short time horizons, influence is maximized when targeting
low-degree nodes, while for long time horizons influence maximization is
achieved when controlling hub nodes. Furthermore, we show that for short and
intermediate time scales influence maximization can exploit knowledge of
(transient) opinion configurations. More in detail, we find two rules. First,
nodes with states differing from the strategic influencer's goal should be
targeted. Second, if only few nodes are initially aligned with the strategic
influencer, nodes subject to opposing influence should be avoided, but when
many nodes are aligned, an optimal influencer should shadow opposing influence.Comment: 22 page
Ahead of the Curve: Insights for the International NGO of the Future
International NGOs have a unique and important role to play in addressing today's complex global challenges. But few of them are living up to their full potential. With support from the Hewlett Foundation, FSG researched how the most innovative INGOs are adapting to the disruptions in the global development sector and embracing four approaches to create greater impact
Private Sector Investment and Sustainable Development: The Current and Potential Role of Institutional Investors, Companies, Banks and Foundations in Sustainable Development
This paper seeks to provide the Financing for Development process with a perspective on the role institutional investors, companies, and foundations can play in the design and implementation of a financing strategy for global sustainability. This will help bridge the terminology and investment approaches of institutional investors, companies, foundations, and governments. The paper highlights ongoing efforts among private investors to increase the impact of their investments. It concludes with a set of key actions facing investors, companies and foundations in their transition towards investment practices that contribute to sustainable development
Seeding with Costly Network Information
We study the task of selecting nodes in a social network of size , to
seed a diffusion with maximum expected spread size, under the independent
cascade model with cascade probability . Most of the previous work on this
problem (known as influence maximization) focuses on efficient algorithms to
approximate the optimal seed set with provable guarantees, given the knowledge
of the entire network. However, in practice, obtaining full knowledge of the
network is very costly. To address this gap, we first study the achievable
guarantees using influence samples. We provide an approximation
algorithm with a tight (1-1/e){\mbox{OPT}}-\epsilon n guarantee, using
influence samples and show that this dependence on
is asymptotically optimal. We then propose a probing algorithm that queries
edges from the graph and use them to find a seed set with the
same almost tight approximation guarantee. We also provide a matching (up to
logarithmic factors) lower-bound on the required number of edges. To address
the dependence of our probing algorithm on the independent cascade probability
, we show that it is impossible to maintain the same approximation
guarantees by controlling the discrepancy between the probing and seeding
cascade probabilities. Instead, we propose to down-sample the probed edges to
match the seeding cascade probability, provided that it does not exceed that of
probing. Finally, we test our algorithms on real world data to quantify the
trade-off between the cost of obtaining more refined network information and
the benefit of the added information for guiding improved seeding strategies
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