Conference proceeding
The Friendship Paradox: Implications In Statistical Inference Of Social Networks
2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), Vol.2019-, pp.1-6
10/2019
DOI: 10.1109/MLSP.2019.8918848
Abstract
The friendship paradox is a type of observation bias in undirected social networks: "on average, the number of friends of a random friend is always greater than or equal to the number of friends of a random individual". This paper discusses friendship paradox, its recent generalizations to directed networks as well as its applications. Specifically, we discuss how the friendship paradox can be exploited in two important statistical inference problems in social networks: (i) polling a social network where the aim is to estimate the fraction of nodes in the network with a specific label (e.g. gender, political affiliation, etc.) by querying (sampling) only some of the nodes and, (ii) estimating the power-law exponent in social networks with a power-law degree distribution.
Details
- Title: Subtitle
- The Friendship Paradox: Implications In Statistical Inference Of Social Networks
- Creators
- Buddhika Nettasinghe - Cornell UniversityVikram Krishnamurthy - Cornell University
- Resource Type
- Conference proceeding
- Publication Details
- 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), Vol.2019-, pp.1-6
- Publisher
- IEEE
- DOI
- 10.1109/MLSP.2019.8918848
- ISSN
- 2161-0363
- eISSN
- 2161-0371
- Language
- English
- Date published
- 10/2019
- Academic Unit
- Business Analytics
- Record Identifier
- 9984422735002771
Metrics
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