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)

7. THANK YOU