20181123 dn2018 graph_analytics_k_patenge

Accessing Data Entities
• Data retrieval via REST API
https://www.meetup.com/meetup_api
• Different API methods & versions
• API Key required
• Sample request
• Data returned as JSON
@kpatenge @datanativesconf #DN18

Potential Questions of Interest
• Which Meetup groups are most active in terms of:
– # members
– # events
– # event attendees
• Who and where are influencers in the Meetup community?
• Where are connections between the Meetup groups in different locations?
• Which topics are “hot” and where?
• How close/similar are groups?
• …

Approach: Modeling Data as Graphs
The more connected the data is, the better a Graph fits
Source: http://www.ateam-oracle.com/intro-to-graphs-at-oracle/

• A set of nodes (aka vertices)
– each vertex has a unique identifier
– each vertex has a set of in/out edges
– each vertex has a collection of key-value
properties
• A set of edges
– each edge has a unique identifier
– each edge has a head/tail vertex
– each edge has a label denoting type of
relationship between two vertices
– each edge has a collection of key-value properties
• Implementations
– Oracle (Spatial and Graph/Big Data Spatial and
Graph), Neo4j, DataStax (Titan), InfiniteGraph, …
What is a Property Graph?
https://github.com/tinkerpop/blueprints/wiki/Property-Graph-Model

• PageRank, Weighted PageRank
– Find influencers, critical vertices
• Personalized PageRank
– Find important people/products/...
with respect to a given starting point
• Sparsification
– Trim down the graph to make it more
fragmented
• Clustering
– Find communities which can be the
basis of segmentation, and/or
recommendation/anomaly detection,
churn analysis
• Centrality
– Find critical people/devices/...
• Shortest path
– Discover links, find suspect‘s close
collaborators, transportation routing
• Breadth-First-Search (BFS)
– Impact analysis, link analysis
• Matric factorization
– Recommendation
• Reachability
– Connectivity test
• ...
Graph Algorithms and their Applications

Pathfinding
– fattestPath
– shortestPathBellmanFord
– shortestPathBellmanFordReverse
– shortestPathDijkstra
– shortestPathDijkstraBidirectional
– shortestPathFilteredDijkstra
– shortestPathFilteredDijkstraBidirectional
– shortestPathHopDist
– shortestPathHopDistReverse
Ranking
– closenessCentralityUnitLength
– degreeCentrality
– eigenvectorCentrality
– Hyperlink-Induced Topic Search (HITS)
– inDegreeCentrality
– nodeBetweennessCentrality
– outDegreeCentrality
– PageRank, weighted PageRank
– approximatePagerank
– personalizedPagerank
– randomWalkWithRestart
Social Network Analysis Algorithms (1)
https://tinyurl.com/pgxdocs

Structure Evaluation
– Conductance
– countTriangles
– inDegreeDistribution
– outDegreeDistribution
– partitionConductance
– partitionModularity
– sparsify
– K-Core computes
Community Detection
– communitiesLabelPropagation
Recommendation
– salsa
– personalizedSalsa
– whomToFollow
Classic - Connected Components
– sccKosaraju
– sccTarjan
– wcc
Social Network Analysis Algorithms (2)
https://tinyurl.com/pgxdocs

Architecture of Oracle Property Graph Analytics
Property
Graph formats
GraphML
GML
GraphSON
Flat Files

PGX
• Toolkit for In-Memory, Parallel Graph
Analytics containing
– PGX shell
– Analyst API with a large collection of built-in
algorithms (45+)
– Enhance with user defined algorithms written
in Green-Marl
– Tutorials, JavaDoc, Use Cases, and more
• Developed by Oracle Labs
• https://docs.oracle.com/cd/E56133_01/latest/i
ndex.html
PGQL – Property Graph Query Language
• http://pgql-lang.org/
• Graph Pattern Matching combined with
SQL
• Developed by Oracle Labs
• Proposed for standardization
• Changes in Version 1.1:
http://pgql-lang.org/spec/1.1/#breaking-syntax-
changes-since-pgql-10
Property Graph Analytics Engine

Data Processing and Analysis Workflow: Overview
Retrieve&Prepare
Prepare
source data
• Using R for data
retrieval via
REST API and
conversion
JSON  CSV 
OPV/OPE
Load&Build
Load
nodes and
edges data
into a graph
• Using Oracle
NoSQL DB as
Graph data
store
Analyze
Analyze
graph data
• Using Graph
Analytics Engine
(PGX) and
Property Graph
Query Language
(PGQL)
Visualize
Visualize
graph data
• Using
Cytoscape
Results
Summarize
results

Demo

‚Big Data‘ Groups
in relation with
Topics and Cities

in relation with
Organizers, Cities
and Events

in relation with
Organizers and Cities
Weakly Connected
Components (WCC)

Ranking via PageRank (Top 10+1)

✓Which cities are tech hot spots?
✓Who are important people in the
Meetup landscape?
✓Which Meetup groups cover with
topics?
✓Which Meetup groups are relevant in
terms of
#Members, #Participants of events,
#Events
✓Which Meetup groups are related
and how?
✓Which topics are related and how?
• The way you model the graph has
influence on the results of executing
Graph algorithms
• The choice of edge directions does
matter depending on the algorithms
• Attaching weights to edges is useful
for certain algorithms
Some Results

Key Takeaways
• Graph data model perfect to focus on connectivity
• Graph databases are powerful tools, complementing relational and other
databases
– Especially strong for analysis of graph topology and connectedness
• Visual analysis helps a great deal to understand how data are connected
– New insights, especially with relationships, dependencies and behavioral patterns
• Big variety of analytic tools and frameworks to answer all kind of questions
• Oracle Graph Technologies combined with Open Source or 3rd party tools

Follow us @kpatenge @SpatialHannes @JeanIhm
karin.patenge@oracle.com
GitHub:
https://github.com/karinpatenge/DN2018
Blogs:
https://blogs.oracle.com/bigdataspatialgraph/
https://blogs.oracle.com/oraclespatial/
AskTom Office Hours for Property Graph:
https://asktom.oracle.com/pls/apex/f?p=100:551

20181123 dn2018 graph_analytics_k_patenge

20181123 dn2018 graph_analytics_k_patenge

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