The document discusses how social networks can be leveraged for campaign purposes. It begins by outlining several real-life applications of influence maximization and network analysis, including viral marketing, adoption of prescription drugs, regulation of yeast cell cycles, and voter turnout analysis. It then discusses classic influence maximization problems and algorithms like greedy maximization. Reverse influence sampling techniques are introduced as an improvement over baseline algorithms, using the concept of reverse reachable sets to estimate influence spread.
Vote Solar is a non-profit organization working to make solar a mainstream energy resource. Learn more about our organization and recent successes in our 2012 Annual Report.
WSDM 2018 Tutorial on Influence Maximization in Online Social NetworksCigdem Aslay
In this tutorial, we extensively survey the research on social influence propagation and maximization, with a focus on the recent algorithmic and theoretical advances. To this end, we provide detailed reviews of the latest research effort devoted to (i) improving the efficiency and scalability of the influence maximization algorithms; (ii) context-aware modeling of the influence maximization problem to better capture real-world marketing scenarios; (iii) modeling and learning of real-world social influence; (iv) bridging the gap between social advertising and viral marketing.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
Vote Solar is a non-profit organization working to make solar a mainstream energy resource. Learn more about our organization and recent successes in our 2012 Annual Report.
WSDM 2018 Tutorial on Influence Maximization in Online Social NetworksCigdem Aslay
In this tutorial, we extensively survey the research on social influence propagation and maximization, with a focus on the recent algorithmic and theoretical advances. To this end, we provide detailed reviews of the latest research effort devoted to (i) improving the efficiency and scalability of the influence maximization algorithms; (ii) context-aware modeling of the influence maximization problem to better capture real-world marketing scenarios; (iii) modeling and learning of real-world social influence; (iv) bridging the gap between social advertising and viral marketing.
Social Network Analysis: What It Is, Why We Should Care, and What We Can Lear...Xiaohan Zeng
The advent of the social networks has completely changed our daily life. The deluge of data collected on Social Network Services (SNS) and recent developments in complex network theory have enabled many marvelous predictive analysis, which tells us many amazing stories.
Why do we often feel that "the world is so small?" Is the six-degree separation purely imagination or based on mathematical insights? Why are there just a few rockstars who enjoy extreme popularity while most of us stay unknown to the world? When science meets coffee shop knowledge, things are bound to be intriguing.
I will first briefly describe what social networks are, in the mathematical sense. Then I will introduce some ways to extract characteristics of networks, and how these analyses can explain many anecdotes in our life. Finally, I'll show an example of what we can learn from social network analysis, based on data from Groupon.
An improvised model for identifying influential nodes in multi parameter soci...csandit
Influence Maximization is one of the major tasks in the field of viral marketing and community
detection. Based on the observation that social networks in general are multi-parameter graphs
and viral marketing or Influence Maximization is based on few parameters, we propose to
convert the general social networks into “interest graphs”. We have proposed an improvised
model for identifying influential nodes in multi-parameter social networks using these “interest
graphs”. The experiments conducted on these interest graphs have shown better results than the
method proposed in [8].
Breakout 3. AI for Sustainable Development and Human Rights: Inclusion, Diver...Saurabh Mishra
This group reviewed data and measurements indicating the positive potential of AI to serve Sustainable Development Goals (SDG’s). Alongside these optimistic inquiries, this group also investigated the risks of AI in areas such as privacy, vulnerable populations, human rights, workplace and organizational policy. The socio-political consequences of AI raise many complex questions which require continued rigorous examination.
Social Network Mining has been an area of interesting research due to billions of people using social media. Community detection is identified as one of the major issues of a social network. Here, a new approach has been presented for community detection which is greedy as well as incremental in nature. The approach is tested on standard datasets and the results are presented as well as analyzed
THE SHADOW OF HIERARCHY - HOW TO SAMPLE A HIDDEN POPULATION OF FORMER EMPLOYEES?Danny Pająk
Relocations in terms of outsourcing to a non-affiliated company and offshoring, the cross-border relo- cation within the company, are widely used in recent years and in many cases cause collective em- ployee layoffs. Even if one of the main intentions is the reduction of costs, relocations may not produce the highly anticipated financial benefits that most companies pursue. One reason is that organizations often have overlooked and underestimated social or 'hidden' consequences of reloca- tions. The goal of the project was to investigate the research question whether there is a kind of hier- archical 'shadow'. Do former hierarchical structures still exist among victims and survivors of the re- location? How is this structure affected by hierarchy even years after the event and how is the shadow affecting the hierarchy of the firm itself? To answer these questions and to test whether former em- ployees are still connected among one other, a pilot study was carried out among a German manufac- turer of electrical equipment which relocated its entire workforce in 2006. The pilot study also tested the feasibility of Respondent-Driven-Sampling (RDS) as an effective and efficient form to sample rare and hidden populations.
Minimizing Influence of Rumors by Blockers on Social Networks: Algorithms and...JAYAPRAKASH JPINFOTECH
Minimizing Influence of Rumors by Blockers on Social Networks: Algorithms and Analysis
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
A high-level overview of social network analysis, providing background on how it came into the knowledge management field. Includes an example and core concepts pertinent to the audience, online community managers.
On A Quest for Combating Filter Bubbles and Misinformation.
Invited Talk, Chinese University of Hong Kong at Shenzhen, Dec 13, 2022.
Social media have greatly facilitated access to information and news and have enhanced users' ability to share with peers their views on issues. However, they have unfortunately led to increased societal polarization. At the center of this phenomenon are filter bubbles and misinformation. Filter bubbles are the result of excessive personalization which enhances relevance of content at the price of limiting exposure to a specific viewpoint. These bubbles are amplified by the so-called echo chambers that exist in social media, whereby members of a community mutually reinforce a fixed opinion or viewpoint on an issue. Misinformation as well as disinformation, on the other hand, tends to propagate through the network, often faster and more virally than truth.
Both problems manifest themselves in the form of groups of actors working in concert and providing mutual reinforcement. How can we recognize these groups? Having detected them, how can we counteract these problems? The first question can benefit from an examination of techniques developed to search for dense subgraphs in an underlying network. As for the second question, a natural approach for countering filter bubbles is to launch some kind of counter-campaign to balance users' exposure to viewpoints. Countermeasures for misinformation propagating through a network depend on the party planning the countermeasure. The network host can intervene and take steps to limit the propagation of misinformation, but these actions come with a cost. Besides the political sensitivity and cost of limiting freedom of expression, what if the intervention was by mistake done on genuine information? On the other hand, a third party interested in countering the propagation of misinformation may launch a counter-campaign. Some of the ideas behind designing such campaigns have strong connections to a classic problem called Influence Maximization, studied in a very different context, driven by different applications like viral marketing, infection containment, and revenue or welfare maximization. In this talk, we will examine research on detecting dense subgraphs as well as competitive influence maximization and discuss how that can inspire techniques for addressing the two problems above.
An improvised model for identifying influential nodes in multi parameter soci...csandit
Influence Maximization is one of the major tasks in the field of viral marketing and community
detection. Based on the observation that social networks in general are multi-parameter graphs
and viral marketing or Influence Maximization is based on few parameters, we propose to
convert the general social networks into “interest graphs”. We have proposed an improvised
model for identifying influential nodes in multi-parameter social networks using these “interest
graphs”. The experiments conducted on these interest graphs have shown better results than the
method proposed in [8].
Breakout 3. AI for Sustainable Development and Human Rights: Inclusion, Diver...Saurabh Mishra
This group reviewed data and measurements indicating the positive potential of AI to serve Sustainable Development Goals (SDG’s). Alongside these optimistic inquiries, this group also investigated the risks of AI in areas such as privacy, vulnerable populations, human rights, workplace and organizational policy. The socio-political consequences of AI raise many complex questions which require continued rigorous examination.
Social Network Mining has been an area of interesting research due to billions of people using social media. Community detection is identified as one of the major issues of a social network. Here, a new approach has been presented for community detection which is greedy as well as incremental in nature. The approach is tested on standard datasets and the results are presented as well as analyzed
THE SHADOW OF HIERARCHY - HOW TO SAMPLE A HIDDEN POPULATION OF FORMER EMPLOYEES?Danny Pająk
Relocations in terms of outsourcing to a non-affiliated company and offshoring, the cross-border relo- cation within the company, are widely used in recent years and in many cases cause collective em- ployee layoffs. Even if one of the main intentions is the reduction of costs, relocations may not produce the highly anticipated financial benefits that most companies pursue. One reason is that organizations often have overlooked and underestimated social or 'hidden' consequences of reloca- tions. The goal of the project was to investigate the research question whether there is a kind of hier- archical 'shadow'. Do former hierarchical structures still exist among victims and survivors of the re- location? How is this structure affected by hierarchy even years after the event and how is the shadow affecting the hierarchy of the firm itself? To answer these questions and to test whether former em- ployees are still connected among one other, a pilot study was carried out among a German manufac- turer of electrical equipment which relocated its entire workforce in 2006. The pilot study also tested the feasibility of Respondent-Driven-Sampling (RDS) as an effective and efficient form to sample rare and hidden populations.
Minimizing Influence of Rumors by Blockers on Social Networks: Algorithms and...JAYAPRAKASH JPINFOTECH
Minimizing Influence of Rumors by Blockers on Social Networks: Algorithms and Analysis
To buy this project in ONLINE, Contact:
Email: jpinfotechprojects@gmail.com,
Website: https://www.jpinfotech.org
A high-level overview of social network analysis, providing background on how it came into the knowledge management field. Includes an example and core concepts pertinent to the audience, online community managers.
On A Quest for Combating Filter Bubbles and Misinformation.
Invited Talk, Chinese University of Hong Kong at Shenzhen, Dec 13, 2022.
Social media have greatly facilitated access to information and news and have enhanced users' ability to share with peers their views on issues. However, they have unfortunately led to increased societal polarization. At the center of this phenomenon are filter bubbles and misinformation. Filter bubbles are the result of excessive personalization which enhances relevance of content at the price of limiting exposure to a specific viewpoint. These bubbles are amplified by the so-called echo chambers that exist in social media, whereby members of a community mutually reinforce a fixed opinion or viewpoint on an issue. Misinformation as well as disinformation, on the other hand, tends to propagate through the network, often faster and more virally than truth.
Both problems manifest themselves in the form of groups of actors working in concert and providing mutual reinforcement. How can we recognize these groups? Having detected them, how can we counteract these problems? The first question can benefit from an examination of techniques developed to search for dense subgraphs in an underlying network. As for the second question, a natural approach for countering filter bubbles is to launch some kind of counter-campaign to balance users' exposure to viewpoints. Countermeasures for misinformation propagating through a network depend on the party planning the countermeasure. The network host can intervene and take steps to limit the propagation of misinformation, but these actions come with a cost. Besides the political sensitivity and cost of limiting freedom of expression, what if the intervention was by mistake done on genuine information? On the other hand, a third party interested in countering the propagation of misinformation may launch a counter-campaign. Some of the ideas behind designing such campaigns have strong connections to a classic problem called Influence Maximization, studied in a very different context, driven by different applications like viral marketing, infection containment, and revenue or welfare maximization. In this talk, we will examine research on detecting dense subgraphs as well as competitive influence maximization and discuss how that can inspire techniques for addressing the two problems above.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
ANAMOLOUS SECONDARY GROWTH IN DICOT ROOTS.pptxRASHMI M G
Abnormal or anomalous secondary growth in plants. It defines secondary growth as an increase in plant girth due to vascular cambium or cork cambium. Anomalous secondary growth does not follow the normal pattern of a single vascular cambium producing xylem internally and phloem externally.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
3. Information Propagation
People are connected and perform actions
nice
read
indeed!
09:3009:00
comment, link,
rate, like,
retweet, post a
message, photo,
or video, etc.
friends,
fans,
followers,
etc. SDM, May 2019, Calgary, Canada.
4. Outline
• Some real-life applications
• Classic Influence Maximization
• Awareness vs Adoption
• Competition “revisited”
• [Incentivized] Social Advertising
• Social Welfare
• Summary & Open Challenges
SDM, May 2019, Calgary, Canada.
5. Real-life Applications of Influence
Analysis
• Viral Marketing
• adoption of prescription drugs
• regulatory mechanism for yeast cell cycle
• voter turnout influence in 2010 US congressional elections
• influence maximization for social good (HEALER)
• Gang violence control by Chicago PD using profit
maximization!
• …
SDM, May 2019, Calgary, Canada.
7. Propagation of Drug Prescriptions
• nodes = physicians; links = ties.
• Question: does contagion work through the network?
• answer: affirmative.
• volume of usage (prescription of drug) by peer controls
contagion more than whether peer prescribed drug.
• genuine social contagion found to be at play, even after
controlling for mass media marketing efforts, and global
network wide changes.
• targeting sociometric opinion leaders definitely beneficial.
[R. Iyengar, C. Van den Bulte, and T.W. Valente. Opinion Leadership and Social Contagion in New
Product Diffusion. Marketing Science, 30(2):195–212, 2011.]
SDM, May 2019, Calgary, Canada.
8. Analysis workflow for Saccharomyces cerevisiae.
IM and Yeast Cell Cycle Regulation
[Gibbs DL, Schmulevich I (2017). Solving the influence maximization problem reveals regulatory
organization of the yeast cell cycle. PLOS Compt.Biol 13(6). e1005591. https://doi.org/10.1371/journal.pcbi.
1005591].
SDM, May 2019, Calgary, Canada.
9. Topology of influential nodes.
[Gibbs DL, Shmulevich I (2017) Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle.
PLOS Computational Biology 13(6): e1005591. https://doi.org/10.1371/journal.pcbi.1005591]
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005591
IM and Yeast Cell Cycle Regulation
SDM, May 2019, Calgary, Canada.
10. Yeast Cell Cycle Study Conclusions
• IM contributes to understanding of yeast cell
cycles.
• Can we find minimum sets of biological
entities that have the greatest influence in the
network context?
• they in turn have greatest control on network
è understand link between network
dynamics and disease.
SDM, May 2019, Calgary, Canada.
11. IM for Social Good – The Healer
homeless
youth
Facebook
application
homeless
youth
.
.
.
DIME
solver
shelter
official
action
recommendation
feedback
[A. Yadav, H. Chan, A. Jiang, H. Xu, E. Rice, and M. Tambe. Using Social Networks to Aid Homeless Shelters:
Dynamic Influence Maximization Under Uncertainty. Proc. Int. Conf. on Autonomous Agents and Multiagent
Systems (AAMAS), 2016.]
[A. Yadav, B. Wilder, E. Rice, R. Petering J. Craddock, A.Y. Maxwell, M. Hemler, L.O. Vera, M. Tambe, and D.
Woo. Bridging the Gap Between Theory and Practice in Influence Maximization: Raising Awareness about HIV
among Homeless Youth. IJCAI 2018.]
HEALER PROJECT: http://teamcore.usc.edu/people/amulya/healer/index.html
SDM, May 2019, Calgary, Canada.
13. Propagation/Diffusion Models
• How does influence/information travel?
• Deterministic versus stochastic models (e.g.,
independent cascade, linear threshold, …)
• Discrete time versus continuous time models.
• Phenomena captured: infection, product
adoption, information, opinion, rumor, etc.
[W. Chen, L., and C. Castillo. Information and Influence Propagation in Social Networks. Morgan-Claypool
2013].
SDM, May 2019, Calgary, Canada.
14. Some Basics
• initial focus on single (product/infection/
rumor) campaign.
inactive active
SDM, May 2019, Calgary, Canada.
15. Independent cascade model
0.1
0.02
0.
3
0.
1
0.
3
0.
3
0.7
0.1
[Kempe et al. KDD 2003].
• Each edge has
influence probability .
• Seeds selected activate
at time
• At each , each active
node gets one shot at
activating its inactive
neighbor ; succeeds w.p.
and fails w.p.
• Once active, stay active.
(u,v)
puv
t = 0.
t > 0
u
v
puv (1− puv ).
e.g., infection propagation. SDM, May 2019, Calgary, Canada.
16. For all discrete time models
• Let be a set of nodes activated at time 0.
– initial adaptors, “patients zero”, …
• = expected number of nodes activated
under model M when diffusion saturates. (spread)
• Key IM problem: choose S to maximize
• Model parameters: edge weights/probabilities.
• Problem parameter: budget k.
S
M (S)
M (S)
SDM, May 2019, Calgary, Canada.
17. Influence Maximization
• Core optimization problem in IM: Given a
diffusion model M, a network G = (V,E),
model parameters, and problem parameters
(budget). Find a seed set under budget
that maximizes
(expected) spread.
S ⇢ V
M (S)
SDM, May 2019, Calgary, Canada.
18. Complexity of IM
• Theorem: The IM problem is NP-hard for
several major diffusion models under both
discrete time and continuous time.
.
SDM, May 2019, Calgary, Canada.
19. Complexity of Spread Computation
• Theorem: It is #P-hard to compute the
expected spread of a node set under both IC
and LT models.
SDM, May 2019, Calgary, Canada.
20. Properties of Spread Function
(resp., ) is
monotone: and
submodular:
S ✓ S0
=) (S) (S0
).
(S) (S, T)
S, S0
⇢ V =)
(S [ S0
) + (S S0
) (S) + (S0
).
S ⇢ S0
⇢ V, x 2 V S0
=)
(x|S0
) (x|s), where
(x|S) := (S [ {x}) (S).
⌘
marginal gain.
SDM, May 2019, Calgary, Canada.
21. Approximation of Submodular
Function Maximization
• Theorem: Let be a monotone
submodular function, with Let
and resp. be the greedy and optimal solutions.
Then
f : 2V
! R 0
f(;) = 0. SGrd
S⇤
f(SGrd
) (1
1
e
)f(S⇤
).
[Nemhauser et al. An analysis of the approximations for maximizing submodular
set functions. Math. Prog., 14:265–294, 1978.]
SDM, May 2019, Calgary, Canada.
22. Approximation of Submodular
Function Maximization
Theorem: Let be a monotone
submodular function, with Let and
resp. be the greedy and optimal solutions. Then
• Theorem: The spread function is monotone
and submodular under various major diffusion
models.
(.)
[D. Kempe, J. Kleinberg, and E. Tardos. On maximizing the spread of influence through a
network. KDD 2003.]
SDM, May 2019, Calgary, Canada.
23. Baseline Approximation Algorithm
Monte Carlo simulations for estimating
expected spread.
Lazy Forward optimization to save useless
updates.
Greedy still extremely slow on large networks.
[J. Leskovec, A. Krause, C. Guestarin, C. Faloutsos, J. VanBriesen, and
N. Glance. Cost-effective outbreak detection in networks. KDD, pp. 420–429, 2007].
[D. Kempe, J. Kleinberg, and E. Tardos. Maximizing the spread
of influence through a social network. KDD 2003].
SDM, May 2019, Calgary, Canada.
24. Reverse Influence Sampling
• A series of algorithms that guarantee a
-approximation to the optimal
expected spread.
• Key : use random reverse reachable sets
(rr-sets) to gauge quality of (candidate) seeds.
(1
1
e
✏)
<latexit sha1_base64="AW/ZWNJ71ORm2nTuWljbif+hLkI=">AAACAXicbVBNS8NAEN34WetX1IvgZbEI9dCSVEGPBS8eK9gPaErZbCft0s0m7G6EEuLFv+LFgyJe/Rfe/Ddu2xy09cHA470ZZub5MWdKO863tbK6tr6xWdgqbu/s7u3bB4ctFSWSQpNGPJIdnyjgTEBTM82hE0sgoc+h7Y9vpn77AaRikbjXkxh6IRkKFjBKtJH69nHZrXiBJDR1sxSyigexYjwS53275FSdGfAycXNSQjkaffvLG0Q0CUFoyolSXdeJdS8lUjPKISt6iYKY0DEZQtdQQUJQvXT2QYbPjDLAQSRNCY1n6u+JlIRKTULfdIZEj9SiNxX/87qJDq57KRNxokHQ+aIg4VhHeBoHHjAJVPOJIYRKZm7FdERMHtqEVjQhuIsvL5NWrepeVGt3l6V6PY+jgE7QKSojF12hOrpFDdREFD2iZ/SK3qwn68V6tz7mrStWPnOE/sD6/AGGeJZN</latexit>
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
25. Reverse Reachable Sets (RR-Sets)
A
B
C
E
D
0.4
0.3
0.6
0.5
0.2
0.3
0.4
• rr-set = sample subgraph of G.
• example of rr-set generation under IC model.
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
26. Reverse Reachable Sets (RR-Sets)
start from a
random node
A
B
C
E
D
0.4
0.3
0.6
0.5
0.2
0.3
0.4
RR-‐set
=
{A}
• rr-set = sample subgraph of G.
• example of rr-set generation under IC model.
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
27. Reverse Reachable Sets (RR-Sets)
start from a
random node
A
B
C
E
D
0.4
0.3
0.6
0.5
0.2
0.3
0.4
sample its
incoming edges
RR-‐set
=
{A}
• rr-set = sample subgraph of G.
• example of rr-set generation under IC model.
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
28. Reverse Reachable Sets (RR-Sets)
start from a
random node
A
B
C
E
D
0.4
0.3
0.6
0.5
0.2
0.3
0.4
sample its
incoming edges
RR-‐set
=
{A}
add the sampled
neighbors
• rr-set = sample subgraph of G.
• example of rr-set generation under IC model.
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
29. Reverse Reachable Sets (RR-Sets)
start from a
random node
A
B
C
E
D
0.4
0.3
0.6
0.5
0.2
0.3
0.4
sample its
incoming edges
RR-‐set
=
{A,
C}
add the sampled
neighbors
• rr-set = sample subgraph of G.
• example of rr-set generation under IC model.
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
30. Reverse Reachable Sets (RR-Sets)
start from a
random node
A
B
C
E
D
0.4
0.3
0.6
0.5
0.2
0.3
0.4
sample its/their
incoming edges
RR-‐set
=
{A,
C}
add the sampled
neighbors
• rr-set = sample subgraph of G.
• example of rr-set generation under IC model.
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
31. Reverse Reachable Sets (RR-Sets)
start from a
random node
A
B
C
E
D
0.4
0.3
0.6
0.5
0.2
0.3
0.4
sample its/their
incoming edges
RR-‐set
=
{A,
C}
add the sampled
neighbors
• rr-set = sample subgraph of G.
• example of rr-set generation under IC model.
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
32. Reverse Reachable Sets (RR-Sets)
start from a
random node
A
B
C
E
D
0.4
0.3
0.6
0.5
0.2
0.3
0.4
sample its/their
incoming edges
RR-‐set
=
{A,
C,
B,
E}
add the sampled
neighbors
• rr-set = sample subgraph of G.
• example of rr-set generation under IC model.
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
33. Reverse Reachable Sets (RR-Sets)
start from a
random node
A
B
C
E
D
0.4
0.3
0.6
0.5
0.2
0.3
0.4
sample its/their
incoming edges
RR-‐set
=
{A,
C,
B,
E}
add the sampled
neighbors
• rr-set = sample subgraph of G.
• example of rr-set generation under IC model.
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
34. Reverse Reachable Sets (RR-Sets)
• An RR-set is a subgraph sample of 𝐺
• Generation of RR-sets under the IC model:
start from a
random node
A
B
C
E
D
0.4
0.3
0.6
0.5
0.2
0.3
0.4
sample its/their
incoming edges
RR-‐set
=
{A,
C,
B,
E}
add the sampled
neighbors
• Intuition:
– An rr-set is a sample set of nodes that can
influence node A
[C. Borgs, M. Brautbar, J. Chayes, and Maximizing Social Influence in Nearly Optimal Time. SODA 2014]
SDM, May 2019, Calgary, Canada.
35. Influence Estimation with RR-Sets
• Theorem: Pr[S overlaps a random rr-set] =
1/n * expected spread of S.
• Family of approx. algorithms: TIM, IMM, Stop-
and-Stare.
[Tang et al., “Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency”, SIGMOD 2014]
[Tang et al., “Influence Maximization in Near-Linear Time: A Martingale Approach”, SIGMOD 2015]
[Chen et al. An issue in the Martingale Analysis of the Influence Maximization Algorithm IMM. arXiv 2018].
[Nguyen et al., “Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks”,
SIGMOD 2016] à arXiv
[K. Huang, S. Wang, G. Bevilacqua, X. Xiao, and L. Revisiting the Stop-and-Stare Algorithms for Influence
Maximization, PVLDB 2017]
SDM, May 2019, Calgary, Canada.
36. Is there anything more to
campaigns?
SDM, May 2019, Calgary, Canada.
38. Awareness vs. Adoption
• Influenced è Adopt?
• Classical models:
– Assume influenced è adopt.
– Profit captured by proxy: expected
spread!
• Need models and algorithms for VM
taking these distinctions into account.
[S. Bhagat, A. Goyal, and L. Maximizing product adoption in social networks. WSDM 2012]
SDM, May 2019, Calgary, Canada.
39. Influence ⇏ Adoption
• Observation: Only a subset of
influenced users actually adopt the
marketed product
Influenced Adopt
q Awareness/information spreads in an
epidemic-like manner while adoption
depends on factors such as product quality
and price
[S. Kalish. A new product adoption model with price, advertising, and uncertainty.
Management Science, 31(12), 1985].
SDM, May 2019, Calgary, Canada.
40. LT-C – LT Model with Colors
• Model Parameters
– A is the set of active friends
– fv(A) is the activation function
– ru,i is the (predicted) rating for product i given by user u
– αv is the probability of user v adopting the product
– βv is the probability of user v promoting the product
Inactive
Tattle
Adopt
Active
Inhibit
Promote
fv(A)
1 fv(A)
↵v
1 ↵v
1 v
v
User
v
Active
Friends
fv(A) =
P
u2A wu,v(ru,i rmin)
rmax rmin
[S. Bhagat, A. Goyal, and L. Maximizing product adoption in social networks. WSDM 2012]
SDM, May 2019, Calgary, Canada.
41. Maximizing Product Adoption
• Problem: Given a social network and product actions,
find k users, targeting whom the expected number of
adopters is maximized.
• Problem is NP-hard.
• Spread function is monotone and submodular è
-approximation algorithm.
• Separation of awareness from adoption improves
accuracy of prediction of adoption on real datasets.
(1 1/e ✏)<latexit sha1_base64="DT+VH0uZKf52NvqKLSW7Gr1jFko=">AAAB+XicbVA9SwNBEN2LXzF+nVraLAYhFol3UdDCImBjGcF8QHKEvc0kWdzbPXb3AuHIP7GxUMTWf2Lnv3GTXKGJDwYe780wMy+MOdPG876d3Nr6xuZWfruws7u3f+AeHjW1TBSFBpVcqnZINHAmoGGY4dCOFZAo5NAKn+5mfmsMSjMpHs0khiAiQ8EGjBJjpZ7rlvyyfwHlLsSacSnOe27Rq3hz4FXiZ6SIMtR77le3L2kSgTCUE607vhebICXKMMphWugmGmJCn8gQOpYKEoEO0vnlU3xmlT4eSGVLGDxXf0+kJNJ6EoW2MyJmpJe9mfif10nM4CZImYgTA4IuFg0Sjo3Esxhwnymghk8sIVQxeyumI6IINTasgg3BX355lTSrFf+yUn24KtZuszjy6ASdohLy0TWqoXtURw1E0Rg9o1f05qTOi/PufCxac042c4z+wPn8AXiqkkA=</latexit>
[S. Bhagat, A. Goyal, and L. Maximizing product adoption in social networks. WSDM 2012]
SDM, May 2019, Calgary, Canada.
43. Previously in Competitive IM
• Mainly follower’s perspective: given
state (say of seed selection) of previous
companies (agents/players):
– what’s the best strategy for the “follower” to maximize its
spread in the face of the competition?
– What’s the best strategy for the follower to maximize its
blocked influence against opponent?
• Most competitive IM algorithms not
scalable or assume unfettered access to
the n/w for all players.
SDM, May 2019, Calgary, Canada.
44. But …
• Campaign runners don’t necessarily have unfettered
access to the network!
• There is an owner of the network.
• Campaigns need owner’s permission.
• May need to pay the owner.
SDM, May 2019, Calgary, Canada.
45. A New Business Model – Introducing
…
Network owner
Provides VM service.
How should the host select/allocate seeds?
I need 100 seeds
I need 250 seeds
Competition starts after host
selects/allocates seeds.
[W. Lu, F. Bonchi, A. Goyal, and L. The bang for the buck: … host perspective. KDD 2013].
SDM, May 2019, Calgary, Canada.
47. Why fairness?
Possible scenario:
Fitbit Versa
30 seeds
Spread 240
Garmin Forerunner 935
50 seeds
Spread 1000
For comparable products, if the b4b is substantially different,
dissatisfied company(ies) may take their VM business elsewhere!
SDM, May 2019, Calgary, Canada.
48. What should we optimize?
• Obvious candidate:
• Proposition:
• è allocations differ only in degree of fairness, gauged
using bang for buck (b4b) :=
• Theorem: Fair Seed Allocation using min-max fairness
is NP-hard.
• Needy Greedy Algorithm for fair seed allocation.
all(S) :=
X
i
i
(S), where S = (S1, . . . , SK).
<latexit sha1_base64="jOCptERYpjnHMg9vqZu/6YECpmY=">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</latexit>
all(S) = LT (
[
i
Si).
<latexit sha1_base64="RXRDzikcM5dua+W26JWF4I1T/UM=">AAACHnicbVDLSsNAFJ3UV62vqEs3g0VoNyGpii4UCm5cuKj0JTQhTKaTdujkwcxEKCFf4sZfceNCEcGV/o3TNoK2HrhwOOde7r3HixkV0jS/tMLS8srqWnG9tLG5tb2j7+51RJRwTNo4YhG/85AgjIakLalk5C7mBAUeI11vdDXxu/eECxqFLTmOiROgQUh9ipFUkquf2oIOAuSmiLGsktqeD5tZFV7CH/2mlVVsjw5wErsUNl1aNSB09bJpmFPARWLlpAxyNFz9w+5HOAlIKDFDQvQsM5ZOirikmJGsZCeCxAiP0ID0FA1RQISTTt/L4JFS+tCPuKpQwqn6eyJFgRDjwFOdAZJDMe9NxP+8XiL9cyelYZxIEuLZIj9hUEZwkhXsU06wZGNFEOZU3QrxEHGEpUq0pEKw5l9eJJ2aYR0btduTcv0ij6MIDsAhqAALnIE6uAYN0AYYPIAn8AJetUftWXvT3metBS2f2Qd/oH1+A/k3oRE=</latexit>
i
(S)
bi
.
<latexit sha1_base64="QNo2yhMtfzyvCaFoSK0xiIoytb8=">AAACCHicbVDLSsNAFJ3UV62vqEsXDhahbkJSBV24KLhxWdE+oIlhMp20Q2eSMDMRSsjSjb/ixoUibv0Ed/6N0zYLbT1w4XDOvdx7T5AwKpVtfxulpeWV1bXyemVjc2t7x9zda8s4FZi0cMxi0Q2QJIxGpKWoYqSbCIJ4wEgnGF1N/M4DEZLG0Z0aJ8TjaBDRkGKktOSbh24oEM5cSQcc3dNa5gYhvM1P8izwaW5B36zalj0FXCROQaqgQNM3v9x+jFNOIoUZkrLn2InyMiQUxYzkFTeVJEF4hAakp2mEOJFeNn0kh8da6cMwFroiBafq74kMcSnHPNCdHKmhnPcm4n9eL1XhhZfRKEkVifBsUZgyqGI4SQX2qSBYsbEmCAuqb4V4iHQySmdX0SE48y8vknbdck6t+s1ZtXFZxFEGB+AI1IADzkEDXIMmaAEMHsEzeAVvxpPxYrwbH7PWklHM7IM/MD5/ABB5mVM=</latexit>
[W. Lu, F. Bonchi, A. Goyal, and L. The bang for the buck: … host perspective. KDD 2013].
SDM, May 2019, Calgary, Canada.
51. Social Advertising
• Similar to organic posts from friends
in a social network
• Contain an advertising message:
text, image or video
• Can propagate to friends via social
actions: “likes”, “shares”
• Each click to a promoted post
produces social proof to friends,
increasing their chances to click
Promoted Posts
SDM, May 2019, Calgary, Canada.
52. Business Model
promoted posts via
social feeds
$Bi<latexit sha1_base64="xpsNJ2f1ME0brEj3LTdJjPUgNIw=">AAAB7HicbVA9SwNBEJ3zM8avqKXNYhSswl0UtLAI2lhG8JJAcoS9zV6yZG/v2J0TQshvsLFQxNYfZOe/cZNcoYkPBh7vzTAzL0ylMOi6387K6tr6xmZhq7i9s7u3Xzo4bJgk04z7LJGJboXUcCkU91Gg5K1UcxqHkjfD4d3Ubz5xbUSiHnGU8iCmfSUiwShaye+c3nZFt1R2K+4MZJl4OSlDjnq39NXpJSyLuUImqTFtz00xGFONgkk+KXYyw1PKhrTP25YqGnMTjGfHTsiZVXokSrQthWSm/p4Y09iYURzazpjiwCx6U/E/r51hdB2MhUoz5IrNF0WZJJiQ6eekJzRnKEeWUKaFvZWwAdWUoc2naEPwFl9eJo1qxbuoVB8uy7WbPI4CHMMJnIMHV1CDe6iDDwwEPMMrvDnKeXHenY9564qTzxzBHzifPxuJjjQ=</latexit>
[C. Aslay, W. Lu, F. Bonchi, A. Goyal, and L. Viral marketing meets social advertising: Ad allocation with minimum
regret”, PVLDB 2015]
SDM, May 2019, Calgary, Canada.
53. Challenges
• Allocate promoted posts subject to user
attention constraints, balancing ad-user
match (click-through probabilities) against
user influence, and network effect against
advertiser budget.
• max possible revenue =
• service rendered could be more or less à
regret.
• new objective: minimize regret!
X
i
Bi
<latexit sha1_base64="iI8SFuRy08Z3TR/Z/LbaPT3ACio=">AAAB8XicbVA9SwNBEJ2LXzF+RS1tFoNgFe5iQAuLoI1lBPOByXHsbfaSJbt7x+6eEI78CxsLRWz9N3b+GzfJFZr4YODx3gwz88KEM21c99sprK1vbG4Vt0s7u3v7B+XDo7aOU0Voi8Q8Vt0Qa8qZpC3DDKfdRFEsQk474fh25neeqNIslg9mklBf4KFkESPYWOmxr1MRMHQTsKBccavuHGiVeDmpQI5mUP7qD2KSCioN4Vjrnucmxs+wMoxwOi31U00TTMZ4SHuWSiyo9rP5xVN0ZpUBimJlSxo0V39PZFhoPRGh7RTYjPSyNxP/83qpia78jMkkNVSSxaIo5cjEaPY+GjBFieETSzBRzN6KyAgrTIwNqWRD8JZfXiXtWtW7qNbu65XGdR5HEU7gFM7Bg0towB00oQUEJDzDK7w52nlx3p2PRWvByWeO4Q+czx8Q8ZB/</latexit>
[C. Aslay, W. Lu, F. Bonchi, A. Goyal, and L. Viral marketing meets social advertising: Ad allocation with minimum
regret”, PVLDB 2015]
SDM, May 2019, Calgary, Canada.
54. Budget and Regret
• Host:
• Owns directed social graph G = (V,E) and Topic-specific CTP model
instance
• Sets user attention bound κu for each user u ∊ V
• Advertiser i:
• agrees to pay CPE(i) for each click up to his budget Bi
• total monetary value of the clicks πi(Si) = σi(Si) × cpe(i)
• Exp. revenue of the host from assigning seed set Si to ad i: min(πi(Si), Bi)
Host’s regret
• πi(Si) < Bi : Lost revenue opportunity
• πi(Si) > Bi : Free service to the advertiser
SDM, May 2019, Calgary, Canada.
55. Regret Minimization
• Theorem: Regret minimization is NP-hard and NP-hard to
approximate.
• Regret function is neither submodular nor monotone.
• Mon. decreasing and submodular for πi(Si) < Bi and
πi(Si U {u}) < Bi
• Mon. increasing and submodular for πi(Si) > Bi and
πi(Si U {u}) > Bi
• Neither monotone nor submodular for πi(Si) < Bi and
πi(Si U {u}) > Bi
Bi
πi(Si) πi(Si U {u})
SDM, May 2019, Calgary, Canada.
56. Regret Minimization
• Simple Greedy Algorithm
• Select the (ad i, user u) pair that gives the max. reduction in
regret at each step, while respecting the attention constraints
• Stop the allocation to i when Ri(Si) starts to increase
[C. Aslay, W. Lu, F. Bonchi, A. Goyal, and L. Viral marketing meets social advertising: Ad allocation with minimum
regret”, PVLDB 2015]
SDM, May 2019, Calgary, Canada.
57. Regret Minimization
• Theorem: Suppose the attention bound of
every user is the #advertisers. Then Greedy
achieves a regret where
• is typically very small in practice.
• TIRM scalable Greedy algorithm using rr-sets.
h,<latexit sha1_base64="GAXHjoxyGoNAFNeVidiT4vuFKRI=">AAAB7nicbVA9SwNBEJ2LXzF+RS1tFoNgIeEuClpYBGwsI5gPSI6wt5lLluztHbt7QjjyI2wsFLH199j5b9wkV2jig4HHezPMzAsSwbVx3W+nsLa+sblV3C7t7O7tH5QPj1o6ThXDJotFrDoB1Si4xKbhRmAnUUijQGA7GN/N/PYTKs1j+WgmCfoRHUoeckaNldq9IZLRBemXK27VnYOsEi8nFcjR6Je/eoOYpRFKwwTVuuu5ifEzqgxnAqelXqoxoWxMh9i1VNIItZ/Nz52SM6sMSBgrW9KQufp7IqOR1pMosJ0RNSO97M3E/7xuasIbP+MySQ1KtlgUpoKYmMx+JwOukBkxsYQyxe2thI2ooszYhEo2BG/55VXSqlW9y2rt4apSv83jKMIJnMI5eHANdbiHBjSBwRie4RXenMR5cd6dj0VrwclnjuEPnM8fFpaOug==</latexit>
hX
i=1
piBi
2
,
<latexit sha1_base64="oGG1Ri5coL7cp1BD+facRJy4vwM=">AAACDXicbVDLSsNAFJ3UV62vqEs3g1VwISWpgi4Uim5cVrAPaGKYTCft0JlJmJkIJeQH3Pgrblwo4ta9O//G6WOhrQcuHM65l3vvCRNGlXacb6uwsLi0vFJcLa2tb2xu2ds7TRWnEpMGjlks2yFShFFBGppqRtqJJIiHjLTCwfXIbz0QqWgs7vQwIT5HPUEjipE2UmAfeIxAT6U8yOilm9/3oRdJhLMkoPAqoHlWzY9hYJedijMGnCfulJTBFPXA/vK6MU45ERozpFTHdRLtZ0hqihnJS16qSILwAPVIx1CBOFF+Nv4mh4dG6cIolqaEhmP190SGuFJDHppOjnRfzXoj8T+vk+ro3M+oSFJNBJ4silIGdQxH0cAulQRrNjQEYUnNrRD3kUlDmwBLJgR39uV50qxW3JNK9fa0XLuYxlEEe2AfHAEXnIEauAF10AAYPIJn8ArerCfrxXq3PiatBWs6swv+wPr8ARzpmus=</latexit>
pi = maxx2V ⇧i({x})/Bi.<latexit sha1_base64="6YRSpU5bSNVKFJy+q8lja2PN/1Y=">AAACDnicbVC7TsMwFHV4lvIKMLJYVJXKEpKCBANIFSyMRaIPqYkix3Vbq44T2Q5qFeULWPgVFgYQYmVm429w2wzQcqQrHZ9zr3zvCWJGpbLtb2NpeWV1bb2wUdzc2t7ZNff2mzJKBCYNHLFItAMkCaOcNBRVjLRjQVAYMNIKhjcTv/VAhKQRv1fjmHgh6nPaoxgpLflmOfYpvIIhGvnpyKUcNjPo1qlPK65+Z8cn1z61oG+WbMueAi4SJyclkKPum19uN8JJSLjCDEnZcexYeSkSimJGsqKbSBIjPER90tGUo5BIL52ek8GyVrqwFwldXMGp+nsiRaGU4zDQnSFSAznvTcT/vE6iehdeSnmcKMLx7KNewqCK4CQb2KWCYMXGmiAsqN4V4gESCCudYFGH4MyfvEiaVcs5tap3Z6XaZR5HARyCI1ABDjgHNXAL6qABMHgEz+AVvBlPxovxbnzMWpeMfOYA/IHx+QOiL5qL</latexit>
pi<latexit sha1_base64="enio4NQE/hjSyMPuiWw4zoSYBNE=">AAAB63icbVBNSwMxEJ34WetX1aOXYBE8ld0q6MFDwYvHCvYD2qVk02wbmmSXJCuUpX/BiwdFvPqHvPlvzLZ70NYHA4/3ZpiZFyaCG+t532htfWNza7u0U97d2z84rBwdt02caspaNBax7obEMMEVa1luBesmmhEZCtYJJ3e533li2vBYPdppwgJJRopHnBKbS8mA40Gl6tW8OfAq8QtShQLNQeWrP4xpKpmyVBBjer6X2CAj2nIq2KzcTw1LCJ2QEes5qohkJsjmt87wuVOGOIq1K2XxXP09kRFpzFSGrlMSOzbLXi7+5/VSG90EGVdJapmii0VRKrCNcf44HnLNqBVTRwjV3N2K6ZhoQq2Lp+xC8JdfXiXtes2/rNUfrqqN2yKOEpzCGVyAD9fQgHtoQgsojOEZXuENSfSC3tHHonUNFTMn8Afo8weqgI34</latexit>
[C. Aslay, W. Lu, F. Bonchi, A. Goyal, and L. Viral marketing meets social advertising: Ad allocation with minimum
regret”, PVLDB 2015]
SDM, May 2019, Calgary, Canada.
59. Incentivized Social Advertising
CPE Model with Seed User Incentives
• Host
• Sells ad-engagements to advertisers
• Inserts promoted posts to feed of users in exchange for monetary
incentives
• Seed users take a cut on the social advertising revenue
• Advertiser
• Pays a fixed CPE to host for each
engagement
• Pays monetary incentive to each seed
user engaging with her ad
• Total payment subject to her budget
[C. Aslay, F. Bonchi, L., and W. Lu. Revenue Maximization in Incentivized Social Advertising”, PVLDB 2017]
SDM, May 2019, Calgary, Canada.
60. • Given
• a social graph G = (V,E)
• TIC propagation model
• h advertisers with budget Bi and CPE(i) for each ad i
• seed user incentives ci(u) for each user u∈V and for each ad i
• Find an allocation S = (S1, …, Sh) maximizing the overall revenue of the host:
Incentivized Social Advertising
[C. Aslay, F. Bonchi, L., and W. Lu. Revenue Maximization in Incentivized Social Advertising”, PVLDB 2017]
SDM, May 2019, Calgary, Canada.
61. • Revenue-Maximization problem is NP-hard
• Restricted special case with h = 1:
• NP-Hard Submodular-Cost Submodular-Knapsack (SCSK) problem
Partition matroid
Submodular knapsack constraints
• Family 𝘊 of feasible solutions form an Independence System
• Two greedy approximation algorithms w.r.t. sensitivity to seed user
costs during the node selection
Incentivized Social Advertising
[C. Aslay, F. Bonchi, L., and W. Lu. Revenue Maximization in Incentivized Social Advertising”, PVLDB 2017]
[R.K. Iyer and J. Bilmes. Submodular optimization with submodular cover and submodular knapsack constraints.
NIPS 2013]
SDM, May 2019, Calgary, Canada.
62. • Cost-agnostic greedy algorithm
• Selects (node,ad) pair giving the max. marginal gain in revenue
• Approximation guarantee follows from 𝘊 forming an independence
system
where
• R and r are, respectively, upper and lower rank of 𝘊
• κπ is the curvature of total revenue function π(.)
Incentivized Social Advertising
[C. Aslay, F. Bonchi, L., and W. Lu. Revenue Maximization in Incentivized Social Advertising”, PVLDB 2017]
SDM, May 2019, Calgary, Canada.
63. • Cost-sensitive greedy algorithm
• Selects the (node,ad) pair giving the max. rate of marginal gain in
revenue per marginal gain in payment
• Approximation guarantee obtained
where
• ρmax and ρmin are, respectively, max. and min. singleton payments
• κρi is the curvature of ad i’s payment function ρi(.)
Incentivized Social Advertising
[C. Aslay, F. Bonchi, L., and W. Lu. Revenue Maximization in Incentivized Social Advertising”, PVLDB 2017]
SDM, May 2019, Calgary,
Canada.
67. Welfare maximization
● Welfare := Sum of utilities
○ Utility := value – price + noise.
● Find a seedset for each item that maximizes
the overall (expected) social welfare
[P. Banerjee, W. Chen, and L. Maximizing Welfare in Social Networks under A Utility Driven Influence
Diffusion model. SIGMOD 2019].
SDM, May 2019, Calgary, Canada.
68. Welfare maximization: In other
contexts
○ Under network externalities
■ Does not consider the recursive propagation in a
network
■ Primary objective – maximize social welfare; no
budget constraints
[N. Economides. Network externalities, complementarities, and invitations to enter. Euro. Jl. Political
Economy. Vol. 12 (1996)].
[S. Bhattacharya, W. Dvorak, M. Henzinger, and M. Starnberger. Welfare maximization with friends-of-friends
network externalities. Theory of Comp. Sys. 2017].
○ Combinatorial Auctions
■ Allocate items to agents to maximize SW
■ No recursive propagation
e.g., [M. Kapralov, I. Post, and J. Vondrak. Online submodular welfare maximization: Greedy is
optimal. SODA 2013].
SDM, May 2019, Calgary, Canada.
75. Utility Based IC Model
• UIC model supports any value and price
functions.
• utility = value – price + noise.
• capture uncertainty in our knowledge of value
via noise à global or local.
• Complementary focus: supermodular value,
additive (or submodular) price, global noise.
SDM, May 2019, Calgary, Canada.
76. Properties of social welfare
● monotone in sets of (seed, item) pairs?
○ Wrinkle: adding a high priced item can
decrease utility.
○ Reachability property to the rescue!
SDM, May 2019, Calgary, Canada.
77. Properties of social welfare
● monotone in sets of (seed, item) pairs
○ Wrinkle: adding a high priced item can
decrease utility.
○ Reachability property to the rescue!
SDM, May 2019, Calgary, Canada.
78. Properties of social welfare
● neither submodular, nor supermodular
○ Not submodular because utility is
supermodular
SDM, May 2019, Calgary, Canada.
79. Why welfare is not submodular?
● Utility is supermodular
Not adopted
SDM, May 2019, Calgary, Canada.
80. Why welfare is not submodular?
● Utility is supermodular
Adopted
SDM, May 2019, Calgary, Canada.
81. Properties of social welfare
● neither submodular, nor supermodular
○ Not supermodular because reachability
is submodular
SDM, May 2019, Calgary, Canada.
82. Why welfare is not supermodular
Marginal gain = 2
SDM, May 2019, Calgary, Canada.
83. Why welfare is not supermodular
Marginal gain = 0
SDM, May 2019, Calgary, Canada.
85. A simple greedy still does the job!
bundleGRD()
provides (1- 1/e - )
approximation
Does not require
knowledge of value/
price functions as
input!
~b = (b1, ..., bm)<latexit sha1_base64="8HR2BmsGBTUvoFHH7uD+mLttJ9g=">AAACAnicbVDLSgMxFM3UV62vUVfiJlgKFcowUwXdCAU3LivYB7TDkEnTNjTJDEmmUIbixl9x40IRt36FO//GtJ2Fth64cDjnXu69J4wZVdp1v63c2vrG5lZ+u7Czu7d/YB8eNVWUSEwaOGKRbIdIEUYFaWiqGWnHkiAeMtIKR7czvzUmUtFIPOhJTHyOBoL2KUbaSIF90h0TnIZTeAPLYeBVoOM4FRgG/Dywi67jzgFXiZeRIshQD+yvbi/CCSdCY4aU6nhurP0USU0xI9NCN1EkRniEBqRjqECcKD+dvzCFJaP0YD+SpoSGc/X3RIq4UhNuDi1xpIdq2ZuJ/3mdRPev/ZSKONFE4MWifsKgjuAsD9ijkmDNJoYgLKm5FeIhkghrk1rBhOAtv7xKmlXHu3Cq95fFWi2LIw9OwRkoAw9cgRq4A3XQABg8gmfwCt6sJ+vFerc+Fq05K5s5Bn9gff4AIfiUqw==</latexit>
✏<latexit
sha1_base64="FFpV7GpkkkeRpfN/x3Cvl4MBQjo=">AAAB73icbVDLSgNBEOz1GeMr6tHLYBA8hd0o6DHgxWME84BkCbOT3mTI7Mw6MyuEkJ/w4kERr/6ON//GSbIHTSxoKKq66e6KUsGN9f1vb219Y3Nru7BT3N3bPzgsHR03jco0wwZTQul2RA0KLrFhuRXYTjXSJBLYika3M7/1hNpwJR/sOMUwoQPJY86odVK7i6nhQsleqexX/DnIKglyUoYc9V7pq9tXLEtQWiaoMZ3AT204odpyJnBa7GYGU8pGdIAdRyVN0IST+b1Tcu6UPomVdiUtmau/JyY0MWacRK4zoXZolr2Z+J/XyWx8E064TDOLki0WxZkgVpHZ86TPNTIrxo5Qprm7lbAh1ZRZF1HRhRAsv7xKmtVKcFmp3l+Va7U8jgKcwhlcQADXUIM7qEMDGAh4hld48x69F+/d+1i0rnn5zAn8gff5A02FkCI=</latexit>
SDM, May 2019, Calgary, Canada.
86. Prefix-preserving seed selection
b1<latexit sha1_base64="ztLqcu68PYWTcRrTaW/Cj8ctAKc=">AAAB6nicbVBNS8NAEJ3Ur1q/qh69LBbBU0lqQY8FLx4r2g9oQ9lsJ+3SzSbsboQS+hO8eFDEq7/Im//GbZuDtj4YeLw3w8y8IBFcG9f9dgobm1vbO8Xd0t7+weFR+fikreNUMWyxWMSqG1CNgktsGW4EdhOFNAoEdoLJ7dzvPKHSPJaPZpqgH9GR5CFn1FjpIRh4g3LFrboLkHXi5aQCOZqD8ld/GLM0QmmYoFr3PDcxfkaV4UzgrNRPNSaUTegIe5ZKGqH2s8WpM3JhlSEJY2VLGrJQf09kNNJ6GgW2M6JmrFe9ufif10tNeONnXCapQcmWi8JUEBOT+d9kyBUyI6aWUKa4vZWwMVWUGZtOyYbgrb68Ttq1qndVrd3XK41GHkcRzuAcLsGDa2jAHTShBQxG8Ayv8OYI58V5dz6WrQUnnzmFP3A+fwDq0Y2M</latexit>
b2<latexit sha1_base64="V5AJK9qEdnJUfdPG6kJm1wyLVAE=">AAAB6nicbVBNS8NAEJ3Ur1q/qh69LBbBU0lqQY8FLx4r2g9oQ9lsN+3SzSbsToQS+hO8eFDEq7/Im//GbZuDtj4YeLw3w8y8IJHCoOt+O4WNza3tneJuaW//4PCofHzSNnGqGW+xWMa6G1DDpVC8hQIl7yaa0yiQvBNMbud+54lrI2L1iNOE+xEdKREKRtFKD8GgNihX3Kq7AFknXk4qkKM5KH/1hzFLI66QSWpMz3MT9DOqUTDJZ6V+anhC2YSOeM9SRSNu/Gxx6oxcWGVIwljbUkgW6u+JjEbGTKPAdkYUx2bVm4v/eb0Uwxs/EypJkSu2XBSmkmBM5n+TodCcoZxaQpkW9lbCxlRThjadkg3BW315nbRrVe+qWruvVxqNPI4inME5XIIH19CAO2hCCxiM4Ble4c2Rzovz7nwsWwtOPnMKf+B8/gDsVY2N</latexit>
(1 1/e)OPTb2<latexit sha1_base64="KlMnJ2y9Tndnf9YkK9wq/qu0C0s=">AAAB+XicbVBNS8NAEN3Ur1q/oh69LBahHqxJFfRY8OLNCv2CNoTNdtIu3WzC7qZQQv+JFw+KePWfePPfuG1z0OqDgcd7M8zMCxLOlHacL6uwtr6xuVXcLu3s7u0f2IdHbRWnkkKLxjyW3YAo4ExASzPNoZtIIFHAoROM7+Z+ZwJSsVg09TQBLyJDwUJGiTaSb9sV98K9hPOHRtPPAr828+2yU3UWwH+Jm5MyytHw7c/+IKZpBEJTTpTquU6ivYxIzSiHWamfKkgIHZMh9AwVJALlZYvLZ/jMKAMcxtKU0Hih/pzISKTUNApMZ0T0SK16c/E/r5fq8NbLmEhSDYIuF4UpxzrG8xjwgEmgmk8NIVQycyumIyIJ1SaskgnBXX35L2nXqu5VtfZ4Xa7X8ziK6ASdogpy0Q2qo3vUQC1E0QQ9oRf0amXWs/VmvS9bC1Y+c4x+wfr4Bg1pkf4=</latexit>
(1 1/e)OPTb1<latexit sha1_base64="Xl+ZzCDGwozG4JG4O664dHCnX04=">AAAB+XicbVDLSgNBEJyNrxhfqx69DAYhHow7UdBjwIs3I+QFybLMTnqTIbMPZmYDYcmfePGgiFf/xJt/4yTZg0YLGoqqbrq7/ERwpR3nyyqsrW9sbhW3Szu7e/sH9uFRW8WpZNBisYhl16cKBI+gpbkW0E0k0NAX0PHHd3O/MwGpeBw19TQBN6TDiAecUW0kz7Yr5IJcwvlDo+llvkdmnl12qs4C+C8hOSmjHA3P/uwPYpaGEGkmqFI94iTazajUnAmYlfqpgoSyMR1Cz9CIhqDcbHH5DJ8ZZYCDWJqKNF6oPycyGio1DX3TGVI9UqveXPzP66U6uHUzHiWphogtFwWpwDrG8xjwgEtgWkwNoUxycytmIyop0yaskgmBrL78l7RrVXJVrT1el+v1PI4iOkGnqIIIukF1dI8aqIUYmqAn9IJercx6tt6s92VrwcpnjtEvWB/fC+SR/Q==</latexit>
(1 1/e)OPTbmax<latexit sha1_base64="P5Sj78nE5km5NYsQ3FzEdzoTc9k=">AAAB/XicbVDLSgMxFM3UV62v8bFzEyxCXVhnqqDLght3VugL2mHIpGkbmmSGJCPWYfBX3LhQxK3/4c6/MW1noa0HLhzOuZd77wkiRpV2nG8rt7S8srqWXy9sbG5t79i7e00VxhKTBg5ZKNsBUoRRQRqaakbakSSIB4y0gtH1xG/dE6loKOp6HBGPo4GgfYqRNpJvH5TcU/eMnNzW6n4S+AlHD2nq20Wn7EwBF4mbkSLIUPPtr24vxDEnQmOGlOq4TqS9BElNMSNpoRsrEiE8QgPSMVQgTpSXTK9P4bFRerAfSlNCw6n6eyJBXKkxD0wnR3qo5r2J+J/XiXX/ykuoiGJNBJ4t6scM6hBOooA9KgnWbGwIwpKaWyEeIomwNoEVTAju/MuLpFkpu+flyt1FsVrN4siDQ3AESsAFl6AKbkANNAAGj+AZvII368l6sd6tj1lrzspm9sEfWJ8/0teUMg==</latexit>
bmax := maxi bi.<latexit sha1_base64="/E96VXH+oDWhe1Un+mswtzoDmkk=">AAACAHicbVDLSgMxFM3UV62vURcu3ASL4GqYqYKiCAU3LivYB7TDkEkzbWiSGZKMWIbZ+CtuXCji1s9w59+YtrPQ1gOXezjnXpJ7woRRpV332yotLa+srpXXKxubW9s79u5eS8WpxKSJYxbLTogUYVSQpqaakU4iCeIhI+1wdDPx2w9EKhqLez1OiM/RQNCIYqSNFNgHYZBx9JjDy2toekB7V2FAHRjYVddxp4CLxCtIFRRoBPZXrx/jlBOhMUNKdT030X6GpKaYkbzSSxVJEB6hAekaKhAnys+mB+Tw2Ch9GMXSlNBwqv7eyBBXasxDM8mRHqp5byL+53VTHV34GRVJqonAs4eilEEdw0kasE8lwZqNDUFYUvNXiIdIIqxNZhUTgjd/8iJp1Rzv1KndnVXr9SKOMjgER+AEeOAc1MEtaIAmwCAHz+AVvFlP1ov1bn3MRktWsbMP/sD6/AEPgZVm</latexit>
choose seeds greedily and in a prefix-preserving
way.
bmax<latexit sha1_base64="oePrIZLfxdjdQmCNEWJUiufwv48=">AAAB7nicbVDLSgNBEOz1GeMr6tHLYBA8hd0o6DHgxWME84BkCbOTTjJkZnaZmRXDko/w4kERr36PN//GSbIHTSxoKKq66e6KEsGN9f1vb219Y3Nru7BT3N3bPzgsHR03TZxqhg0Wi1i3I2pQcIUNy63AdqKRykhgKxrfzvzWI2rDY/VgJwmGkg4VH3BGrZNaUS+T9GnaK5X9ij8HWSVBTsqQo94rfXX7MUslKssENaYT+IkNM6otZwKnxW5qMKFsTIfYcVRRiSbM5udOyblT+mQQa1fKkrn6eyKj0piJjFynpHZklr2Z+J/XSe3gJsy4SlKLii0WDVJBbExmv5M+18ismDhCmebuVsJGVFNmXUJFF0Kw/PIqaVYrwWWlen9VrtXyOApwCmdwAQFcQw3uoA4NYDCGZ3iFNy/xXrx372PRuublMyfwB97nD6Hej8E=</latexit>
SDM, May 2019, Calgary, Canada.
87. Prefix-preserving seed selection
• Key : generate enough rr-sets to let us
handle every budget in the budget vector.
• Challenge: #rr-sets needed (sample
complexity) is not monotone in the budget!
[P. Banerjee, W. Chen, and L. Maximizing Welfare in Social Networks under A Utility Driven Influence
Diffusion model. SIGMOD 2019].
Algorithm PRIMA.
SDM, May 2019, Calgary, Canada.
88. Why does greedy work?
● Items I à sequence of “atomic” blocks.
● marginalGain(block | previous blocks) >= 0.
● Nodes reachable from an adopting node adopt the block.
● Prefix-preserving greedy allocation ensures full blocks’ seeds
are approximately optimal
● For arbitrary allocations,
utility(partial block) utility(corresponding full block)
(by supermodularity).
<latexit sha1_base64="4/s3xIM2h235MIQTNROwgf7Gps0=">AAAB63icbVBNS8NAEJ34WetX1aOXxSJ4KkkV9Fjw4rGC/YA2lM120i7dbOLuRiihf8GLB0W8+oe8+W/ctDlo64OBx3szzMwLEsG1cd1vZ219Y3Nru7RT3t3bPzisHB23dZwqhi0Wi1h1A6pRcIktw43AbqKQRoHATjC5zf3OEyrNY/lgpgn6ER1JHnJGTS71BT4OKlW35s5BVolXkCoUaA4qX/1hzNIIpWGCat3z3MT4GVWGM4Gzcj/VmFA2oSPsWSpphNrP5rfOyLlVhiSMlS1pyFz9PZHRSOtpFNjOiJqxXvZy8T+vl5rwxs+4TFKDki0WhakgJib542TIFTIjppZQpri9lbAxVZQZG0/ZhuAtv7xK2vWad1mr319VG40ijhKcwhlcgAfX0IA7aEILGIzhGV7hzYmcF+fd+Vi0rjnFzAn8gfP5AxW1jkI=</latexit>
SDM, May 2019, Calgary, Canada.
92. Summary
• Networks are essential for campaigns but
they merely provide the plumbing.
• Interesting campaigns emerge from
distinguishing between influence and
adoption, multi-item interactions (competition,
complementarity), economic models of
“consumption”.
• Asking questions about alternative
objectives.
SDM, May 2019, Calgary, Canada.
93. Open Questions
• Dealing with problems studied over
– adaptive setting
– evolving networks
– continuous time
• do “economic” notions have a useful role to
play in problems related to infection
propagation/containment, misinformation
mitigation, biology, social good, … ?
• Newer applications? SDM, May 2019, Calgary, Canada.
94. Thanks!
Amit Goyal Smriti Bhagat Cigdem Aslay Glenn Bevilacqua
Suresh V. Wei Chen Francesco Bonchi Wei Lu Sharan Vaswani
Xiaokui Xiao Prithu Banerjee . . .
SDM, May 2019, Calgary, Canada.