View
2
Download
0
Category
Preview:
Citation preview
SALESDECK2016v2
Social Data Science
DR PAUL SIEGEL/ DATA SCIENTIST
SALESDECK2016v2
You have a good sentiment model. Now what?
● Identify influencers
● Construct audiences
● Activate advocates Key model: social networks
SALESDECK2016v2
NOW YOU KNOW | #NYKCONF
BRANDWATCH.COM
SALESDECK2016v2
Social Network Analysis
1. Build network model from data
2. Community detection
3. Influence modelling / optimization
SALESDECK2016v2
Network Models
SALESDECK2016v2
Social graph: nodes are people, edges are: ● Interactions
● Followership
● Latent influence
SALESDECK2016v2
Coupled Hidden Markov Models
SALESDECK2016v2
Community Detection
SALESDECK2016v2
Isoperimetric Problem
SALESDECK2016v2
Algorithms: ● Fiedler vector
● Louvain
● Short random walks
SALESDECK2016v2
Short Random Walks
SALESDECK2016v2
Influence
SALESDECK2016v2
● Linear threshold model
● Independent cascade model
● Centrality heuristics (degree, eigenvector, PageRank, etc.)
SALESDECK2016v2
Tech
SALESDECK2016v2
● Networkx (Python)
● Graphx / GraphFrames (Spark)
● Graph databases?
SALESDECK2016v2
References
● Everyone’s an Influencer: Quantifying Influence on Twitter by Watts et al ● Modeling Temporal Activity Patterns in Dynamic Social Networks by
Vasanthan Raghavan et al ● Four proofs for the Cheeger inequality and graph partition algorithms by
Chung ● Maximizing the Spread of Influence through a Social Network by Kempe et
al ● networkx.github.io ● spark.apache.org/graphx
Recommended