Graph Data Science Training - Alicia Frame Presentation

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GDS can be used for a variety of purposes including community detection, fraud detection, disambiguation, recommendations, and more. Community detection algorithms break up graphs into subgraphs based on edges, which can speed up calculations when combined with node similarity, or focus analysis on important parts when combined with centrality. Example use cases include identifying customer segments in retail graphs and behavior clusters in marketing website traffic graphs.

Technology

Data Pre-processing:
1. Identifying and removing super nodes: degree centrality
2. Identifying subgraphs: weakly connected components
2
Common Algorithms & Combinations

Community Detection + ???? = Proﬁt
Community detection algorithms break up your graph into smaller
subgraphs based on edges. Use community detection to downscope
problems on large graphs and focus on the important parts.
- Speed up calculations: Community detection + node similarity
- Focus on the important stuﬀ: Community detection + centrality
- Aggregate your graph: Treat communities as nodes and run centrality,
similarity, etc
3
Common Algorithms & Combinations

4
What else can we use GDS for?
Fraud Detection
Weakly Connected
Components - First Party
Fraud
Louvain - Fraud Rings
Page Rank, Degree
Centrality - Anomalies
Disambiguation
Weakly Connected
Components - Common
identiﬁers
Label Propagation -
Overlapping relationships
Node Similarity
Recommendations
Louvain - Interacting
communities
Page Rank, Betweenness,
Closeness Centrality-
Important users
Node Similarity
And much more!

Transactional graph:
What kind of questions can we answer?
6
Examples from … Retail

Customer segmentation:
Identify customers who buy similar items (node
similarity), and use Louvain to identify clusters of
consumers with similar behaviour
Item recommendations:
Recommend items that are frequently bought by the
same customers or in the same transaction with node
similarity.
Find items that inﬂuence copurchases using Page
Rank, or fast moving items with closeness Centrality7
Examples from … Retail

8
Examples from … Marketing
Web Traﬃc Graph:
How can we
tell whos
who?

9
Examples from … Marketing
Disambiguation:
Identify subgraphs of users with co-occurring
identiﬁers using Weakly Connected Components
User Behavior:
Identify communities of unique users that interact
with the same websites using Label Propagation
Identify which websites drive traﬃc using Page
Rank

Knowledge graph representing genes, chemicals, diseases:
10
Examples from … Life Sciences
What questions can we answer?

PageRank & Betweenness to identify essential regulatory genes or
drug targets
Louvain to identify protein regulatory networks
Shortest path to link drug targets to possible outcomes or side
eﬀects
Node Similarity to ﬁnd structurally similar chemicals
Link Prediction to estimate likelihood of interactions
11
Examples from … Life Sciences

12
What are your use
cases? Let’s brainstorm!

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