9 research outputs found
A k-hop Collaborate Game Model: Extended to Community Budgets and Adaptive Non-Submodularity
Revenue maximization (RM) is one of the most important problems on online
social networks (OSNs), which attempts to find a small subset of users in OSNs
that makes the expected revenue maximized. It has been researched intensively
before. However, most of exsiting literatures were based on non-adaptive
seeding strategy and on simple information diffusion model, such as
IC/LT-model. It considered the single influenced user as a measurement unit to
quantify the revenue. Until Collaborate Game model appeared, it considered
activity as a basic object to compute the revenue. An activity initiated by a
user can only influence those users whose distance are within k-hop from the
initiator. Based on that, we adopt adaptive seed strategy and formulate the
Revenue Maximization under the Size Budget (RMSB) problem. If taking into
account the product's promotion, we extend RMSB to the Revenue Maximization
under the Community Budget (RMCB) problem, where the influence can be
distributed over the whole network. The objective function of RMSB and RMCB is
adatpive monotone and not adaptive submodular, but in some special cases, it is
adaptive submodular. We study the RMSB and RMCB problem under both the speical
submodular cases and general non-submodular cases, and propose RMSBSolver and
RMCBSolver to solve them with strong theoretical guarantees, respectively.
Especially, we give a data-dependent approximation ratio for RMSB problem under
the general non-submodular cases. Finally, we evaluate our proposed algorithms
by conducting experiments on real datasets, and show the effectiveness and
accuracy of our solutions
Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations
Continuous influence maximization (CIM) generalizes the original influence
maximization by incorporating general marketing strategies: a marketing
strategy mix is a vector such that for each
node in a social network, could be activated as a seed of diffusion
with probability , where is a strategy activation
function satisfying DR-submodularity. CIM is the task of selecting a strategy
mix with constraint where is a budget
constraint, such that the total number of activated nodes after the diffusion
process, called influence spread and denoted as , is
maximized. In this paper, we extend CIM to consider budget saving, that is,
each strategy mix has a cost where is a
convex cost function, we want to maximize the balanced sum where is a balance parameter, subject
to the constraint of . We denote this problem as
CIM-BS. The objective function of CIM-BS is neither monotone, nor DR-submodular
or concave, and thus neither the greedy algorithm nor the standard result on
gradient method could be directly applied. Our key innovation is the
combination of the gradient method with reverse influence sampling to design
algorithms that solve CIM-BS: For the general case, we give an algorithm that
achieves -approximation, and for the case
of independent strategy activations, we present an algorithm that achieves
approximation.Comment: To appear in AAAI-20, 43 page
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Destination-based Routing and Circuit Allocation for Future Traffic Growth
Internet traffic continues to grow relentlessly, driven largely by increasingly high- \\ resolution video streaming, the increasing adoption of cloud computing, the emergence of 5G networks, and the ever-growing reach of social media and social networks. Existing networks use packet switching to route packets on a hop-by-hop basis from the source to the destination. However, they suffer from two shortcomings. First, in existing networks, packets are routed along a fixed shortest path using the Open Shortest Path First (OSPF) protocol or obliviously load-balanced across equal-cost paths using the Equal-Cost Multi-Path (ECMP) protocol. These routing protocols do not fully utilize the network capacity because they do not adapt to network congestions in their routing decisions. Second, although studies have shown that the majority of packets processed by Internet routers are pass-through traffic, packets nonetheless have to be queued and routed at every hop in existing networks, which unnecessarily adds substantial delays and processing costs.In this thesis, we present two new approaches to overcome these shortcomings. First, we propose new backpressure-based routing algorithms which use only shortest-path routes when they are sufficient to accommodate the given traffic load, but will incrementally expand routing choices as needed to accommodate increasing traffic loads. This avoids the poor delay performance inherent in backpressure-based routing algorithms where packets may take long detours under light or moderate loads, and still retains the notable advantage, the network-wide optimal throughput, because packets are adaptively routed along less congested paths.Second, we propose a unified packet and circuit switched network in which the underlying optical transport is used to circuit-switch pass-through traffic by means of pre-established circuits. This avoids unnecessary packet queuing delays and processing costs at each hop. We propose a novel convex optimization framework based on a new destination-based multicommodity flow formulation for the allocation of circuits in such unified networks
Social Media Influencers- A Review of Operations Management Literature
This literature review provides a comprehensive survey of research on Social Media
Influencers (SMIs) across the fields of SMIs in marketing, seeding strategies, influence
maximization and applications of SMIs in society. Specifically, we focus on examining the
methods employed by researchers to reach their conclusions. Through our analysis, we
identify opportunities for future research that align with emerging areas and unexplored
territories related to theory, context, and methodology. This approach offers a fresh
perspective on existing research, paving the way for more effective and impactful studies in
the future. Additionally, gaining a deeper understanding of the underlying principles and
methodologies of these concepts enables more informed decision-making when
implementing these strategie
Profit Maximization over Social Networks
Influence maximization is the problem of finding a set of influential users in a social network such that the expected spread of influence under a certain propagation model is maximized. Much of the previous work has neglected the important distinction between social influence and actual product adoption. However, as recognized in the management science literature, an individual who gets influenced by social acquaintances may not necessarily adopt a product (or technology), due, e.g., to monetary concerns. In this work, we distinguish between influence and adoption by explicitly modeling the states of being influenced and of adopting a product. We extend the classical Linear Threshold (LT) model to incorporate prices and valuations, and factor them into users β decision-making process of adopting a product. We show that the expected profit function under our proposed model maintains submodularity under certain conditions, but no longer exhibits monotonicity, unlike the expected influence spread function. To maximize the expected profit under our extended LT model, we employ an unbudgeted greedy framework to propose three profit maximization algorithms. The results of our detailed experimental study on three real-world datasets demonstrate that of the three algorithms, PAGE, which assigns prices dynamically based on the profit potential of each candidate seed, has the best performance both in the expected profit achieved and in running time