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Presented by
Yerasani Sinjana
Research Scholar, I&SE, IIT KGP
Supervised by
Prof. M K Tiwari & Prof. Monalisa Sarma
Literature Review on Social
Networking in Supply chain
Introduction
 Recent research shows that the role of social
media on user’s purchase decisions is startling.
 Products are getting popularized using Social
Influence.
 The effect of this advertising on present
demand has to be estimated.
 Objective is to reduce uncertainty, minimize
delay and accordingly maximize profit and
utility.
Literature Review
Year Author Title Description
2016 Shuai et al. A comprehensive study on
willingness maximization for
social activity planning with
quality guarantee
To maximizing
willingness CBAS
and CBAS-ND
algorithms are used
2015 Shuai et al. Scale-Adaptive group
optimization for Social Activity
planning
Optimally allocate
computational
budget using BARGS
method
2015 Morone et al. Influence maximization in
complex networks through
optimal percolation
Finding the optimal
set of influencers
2015 Molavi et al. Learning to coordinate in
social networks
Private observations
are optimally
aggregated in the
limit for generic
specifications of the
Cond…
Year Author Title Description
2015 Ferreira et
al.
Analytics for an Online
Retailer: Demand Forecasting
and Price Optimization
Algorithm to solve the
subsequent multiproduct price
optimization that incorporates
reference price effects
2014 Bimpikis et
al.
Competing over networks User in a social network
influence by the
recommendations that they
get from their peers
201
4
Aral and
Walker
Tie strength, embeddedness
and social influence: A large-
scale networked experiment
Relationship and social
structural characteristics play
on individual-based
propagation
201
2
Candogan
et al.
Optimal pricing in networks
with externalities
The value of network
information by comparing the
profits of a user who does not
consider network effect and
who uses information
optimally
Cond…
Year Author Title Description
2008 Wu et al. Mining face to face
interaction networks using
sociometric badges
Understanding the
performance correlated
with proximity networks
2007 Bharathi et al. Competitive Influence
Maximization in Social
networks
Maximizing the spread of
influence through a social
network in a competitive
environment
2004 Wakolbinger et
al.
Dynamic super networks
for the integration of social
networks and supply chain
with electronic commerce:
Modeling and analysis of
buyer-seller relationships
with computations
Maximizing the profit and
relationship value and
minimizing risk
References
 K. Bimpikis, A. Ozdaglar, and E. Yildiz, “Competing over networks,” submitted for
publication, 2013. Available online at
http://web.mit.edu/asuman/www/publications.htm
 Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence
through a social network. Proc. 9th ACM SIGKDD International Conf. Knowledge
Discovery and Data Mining (ACM New York). 137-146
 Mandala, Supreet. "A Game-theoretic Approach to Graph Clustering." INFORMS
Journal On Computing : JOC 26.3 (2014)
 Michael Campbell (2005) A Gibbsian Approach to Potential Game Theory (draft).
http://arxiv.org/pdf/cond-mat/0502112v2
 Naik, Prasad A, Ashutosh Prasad, Suresh P Sethi. 2008. Building brand
awareness in dynamic oligopoly markets. Management Science 54(1) 129–138
 Ozan Candogan, Kostas Bimpikis, Asuman Ozdaglar (2012) Optimal Pricing in
Networks with externalities. Operational Research 60(4) 883-995.
 Shishir Bharathi, David Kempe, MahyarSalek (2007) Competitive influence
maximization in social networks. WINE’07 Proceedings of the 3rd international
conference on Internet and network economics. 306-311
 Sinan Aral, Dylan Walker (2013) Tie Strength, Embeddedness, and Social
Influence: A Large- Scale Networked Experiment. Management Science 60(6)
THANK YOU

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Literature Review on Social Networking in Supply chain

  • 1. Presented by Yerasani Sinjana Research Scholar, I&SE, IIT KGP Supervised by Prof. M K Tiwari & Prof. Monalisa Sarma Literature Review on Social Networking in Supply chain
  • 2. Introduction  Recent research shows that the role of social media on user’s purchase decisions is startling.  Products are getting popularized using Social Influence.  The effect of this advertising on present demand has to be estimated.  Objective is to reduce uncertainty, minimize delay and accordingly maximize profit and utility.
  • 3. Literature Review Year Author Title Description 2016 Shuai et al. A comprehensive study on willingness maximization for social activity planning with quality guarantee To maximizing willingness CBAS and CBAS-ND algorithms are used 2015 Shuai et al. Scale-Adaptive group optimization for Social Activity planning Optimally allocate computational budget using BARGS method 2015 Morone et al. Influence maximization in complex networks through optimal percolation Finding the optimal set of influencers 2015 Molavi et al. Learning to coordinate in social networks Private observations are optimally aggregated in the limit for generic specifications of the
  • 4. Cond… Year Author Title Description 2015 Ferreira et al. Analytics for an Online Retailer: Demand Forecasting and Price Optimization Algorithm to solve the subsequent multiproduct price optimization that incorporates reference price effects 2014 Bimpikis et al. Competing over networks User in a social network influence by the recommendations that they get from their peers 201 4 Aral and Walker Tie strength, embeddedness and social influence: A large- scale networked experiment Relationship and social structural characteristics play on individual-based propagation 201 2 Candogan et al. Optimal pricing in networks with externalities The value of network information by comparing the profits of a user who does not consider network effect and who uses information optimally
  • 5. Cond… Year Author Title Description 2008 Wu et al. Mining face to face interaction networks using sociometric badges Understanding the performance correlated with proximity networks 2007 Bharathi et al. Competitive Influence Maximization in Social networks Maximizing the spread of influence through a social network in a competitive environment 2004 Wakolbinger et al. Dynamic super networks for the integration of social networks and supply chain with electronic commerce: Modeling and analysis of buyer-seller relationships with computations Maximizing the profit and relationship value and minimizing risk
  • 6. References  K. Bimpikis, A. Ozdaglar, and E. Yildiz, “Competing over networks,” submitted for publication, 2013. Available online at http://web.mit.edu/asuman/www/publications.htm  Kempe D, Kleinberg J, Tardos E (2003) Maximizing the spread of influence through a social network. Proc. 9th ACM SIGKDD International Conf. Knowledge Discovery and Data Mining (ACM New York). 137-146  Mandala, Supreet. "A Game-theoretic Approach to Graph Clustering." INFORMS Journal On Computing : JOC 26.3 (2014)  Michael Campbell (2005) A Gibbsian Approach to Potential Game Theory (draft). http://arxiv.org/pdf/cond-mat/0502112v2  Naik, Prasad A, Ashutosh Prasad, Suresh P Sethi. 2008. Building brand awareness in dynamic oligopoly markets. Management Science 54(1) 129–138  Ozan Candogan, Kostas Bimpikis, Asuman Ozdaglar (2012) Optimal Pricing in Networks with externalities. Operational Research 60(4) 883-995.  Shishir Bharathi, David Kempe, MahyarSalek (2007) Competitive influence maximization in social networks. WINE’07 Proceedings of the 3rd international conference on Internet and network economics. 306-311  Sinan Aral, Dylan Walker (2013) Tie Strength, Embeddedness, and Social Influence: A Large- Scale Networked Experiment. Management Science 60(6)