Transform Your Telco
Operations with Graph
Technologies
Neo4j: The #1 Platform for Connected Data
Jesús Barrasa
Director Telecom Solutions,

Neo4j
jesus.barrasa@neo4j.com
@barrasaDV
Transform Your Telco Operations with Graph Technologies
Amy E. Hodler
Analytics Program Manager,
Neo4j
amy.hodler@neo4j.com
@amyhodler
Why Graphs in Telco?
Requirements
Capture Complexity
Allow Flexibility
High Performance
Bridge Business - IT gap
Rich, Dynamic, human
friendly Graph Model on
a Native Graph Platform
“Give me solutions that provide me and
my customers with accurate and
timely visibility into the state of the
network and the services riding on that
network”
Tier1 Service Provider (*)
(*) https://www.tmforum.org/resources/quick_insights/maximizing-network-agility-effective-resource-management/
Simplified view of a graph representation of a service
based on the SID model (http://www.tmforum.org)
Structural vs. Discrete analysis
Structural representation of
composition, dependencies,
recursion…
A
B C D E
F G
.25 .25 .25 .25
.5 .5
MATCH (:Device)-[*]->(x:Port)
WHERE NOT (x)--()
Device
Shelf Shelf
Card Card
Slot
Card
Port Port
Port
Port
Structural vs. Discrete analysis
Data integration
123
456 789
A_123
F_456 F_789
“Graph data structures provide enormous advantages over
relational databases overall. Ericsson has chosen to take
this approach because of the tremendous advantage it
has to service providers”(*)
Graeme Jones. Strategic Product Manager
(*) https://www.tmforum.org/resources/quick_insights/maximizing-network-agility-effective-resource-management/
The Property Graph Model
Optical MUX Filter Card
Use Cases
IoT OSS/BSS
Governance &
Metadata Mgmnt
IAM &
Fraud Analysis
Common Graph Use Cases in Telco
Digital
Transformation
• Smart Homes • Assurance
• Fulfilment
• CRM/Support
• Planning &
Optimisation
• Regulatory
compliance
• Data Lineage
• Consent Mgmnt
• Identity &
Access Mgmnt
• CEM
• Customer 360
• Graph Rules
Engine
In Network Operations
Network Service Assurance White Paper
neo4j.com/whitepapers/white-paper-optimize-network-services-neo4j/
Fault Management
Fault Management
(Deep) Impact/Root Cause Analysis
🏦 :DEPENDS_ON
:DEPENDS_ON
:DEPENDS_ON
IF/AX2431
💥
Customer
Event Correlation
Event Prioritisation
MATCH (fe:Link { linkId: $id})<-[:DEPENDS_ON*]-(s:Service)
RETURN max(s.priority) AS severity
{ alarmType: “LOS”,
notifyingEntity: “IF/AX/0/3”, …}
Fault Management
(Deep) Impact/Root Cause Analysis
GET http://localhost:11001/engine/ia/s1_361_sdh
{"severitySummaryCode": 4,
"severitySummaryDesc": "MAJOR",
"detail": [{"elem":"2217/ol",

"impact" : 1,
...
Graph Size: ~50M nodes (avg depth: 6)Graph Size: ~1K nodes (avg depth: 5)
Simulation: 128 clients, synchronous requests with1ms wait between requests
50000x increase in size of dataset -> 1.14x impact in query performance
Graph Native Matters!
Fault Management at Scale Matters
(Deep) Impact/Root Cause Analysis
Algorithms – Community Detection
• Label Propagation
○ Spreads labels based on neighbors to infer clusters
• Union Find / Connected Components
○ Finds groups of nodes that all have a path to each other

• Strongly Connected Components
○ Finds groups of nodes that are all connected 

to each other following the 

direction of relationships
• Louvain Modularity
○ Measures the presumed accuracy of community grouping
• Triangle-Count & Clustering Coefficient
○ Measures the degree that nodes tend to cluster together
Source: “Fast unfolding of communities in large
networks” – Blondel, Guillaume, Lambiotte,
Lefebvre
Change Management
& What-If Analysis
Source: Network Science - Barabasi
Understanding
Influence
Source: “Robustness of the European power grids under intentional attack.” - R.V. Sole, M. Rosas-Casals, B. Corominas-Murtra, and S. Valverde.
Source: “Network Science” - Barabasi
Preventing 

Cascading Failures
with 

4 Nodes Removed
Traffic Engineering
Traffic Engineering
Diverse Routing
?
Traffic Engineering
Diverse Routing
Path Analysis
Least cost path from A to B
+
Dependency Analysis
No shared underlying
resources
• Single-Source Shortest Path
○ Calculates “shortest” path between a node and all other nodes
Algorithms - Pathfinding & Search
• All-Pairs Shortest Path
○ Finds all shortest paths between
all nodes
• Parallel Breadth-First Search & Depth-First Search
○ Traverses tree structure by exploring nearest neighbors (BFS) or down each branch (DFS)
• Single-Source Shortest Path
○ Calculates path between a node and all other nodes
Algorithms - Pathfinding & Search
• All-Pairs Shortest Path
○ Calculates shortest path group
with all shortest paths between
nodes
• Minimum Weight Spanning Tree
○ Calculates the path with the smallest value for visiting all nodes Least Cost Routing
Conclusions
The Neo4j Graph Platform
Neo4j Adoption Highlights
MVPD
2 of the Top 5 US multichannel
video service providers have
chosen Neo4j
SW & Eqpmnt Vendors
3 of the 5 largest Telco software
and equipment vendors in the
world embed Neo4j in key products
2 of the 3 world’s largest CSP
use Neo4j in mission critical
solutions
Largest Telcos
(March 2018)
Magic Quadrant for Operations
Support Systems. March 2018
Telecom is All About Connections
Accurate, real-time visibility for
your network and services with a
native-graph platform
Uncover hidden dependencies
for planning and predictions with
optimized graph algorithms
Let’s Get Started Together
Telecom page
neo4j.com/industries/telecom/
Network Service Assurance Paper
neo4j.com/whitepapers/white-paper-
optimize-network-services-neo4j/
Desktop Download
neo4j.com/download/
Questions?

Transform Your Telecom Operations with Graph Technologies

  • 1.
    Transform Your Telco Operationswith Graph Technologies Neo4j: The #1 Platform for Connected Data
  • 2.
    Jesús Barrasa Director TelecomSolutions,
 Neo4j jesus.barrasa@neo4j.com @barrasaDV Transform Your Telco Operations with Graph Technologies Amy E. Hodler Analytics Program Manager, Neo4j amy.hodler@neo4j.com @amyhodler
  • 3.
  • 4.
    Requirements Capture Complexity Allow Flexibility HighPerformance Bridge Business - IT gap Rich, Dynamic, human friendly Graph Model on a Native Graph Platform
  • 5.
    “Give me solutionsthat provide me and my customers with accurate and timely visibility into the state of the network and the services riding on that network” Tier1 Service Provider (*) (*) https://www.tmforum.org/resources/quick_insights/maximizing-network-agility-effective-resource-management/ Simplified view of a graph representation of a service based on the SID model (http://www.tmforum.org)
  • 6.
    Structural vs. Discreteanalysis Structural representation of composition, dependencies, recursion… A B C D E F G .25 .25 .25 .25 .5 .5 MATCH (:Device)-[*]->(x:Port) WHERE NOT (x)--() Device Shelf Shelf Card Card Slot Card Port Port Port Port
  • 7.
    Structural vs. Discreteanalysis Data integration 123 456 789 A_123 F_456 F_789
  • 8.
    “Graph data structuresprovide enormous advantages over relational databases overall. Ericsson has chosen to take this approach because of the tremendous advantage it has to service providers”(*) Graeme Jones. Strategic Product Manager (*) https://www.tmforum.org/resources/quick_insights/maximizing-network-agility-effective-resource-management/
  • 9.
    The Property GraphModel Optical MUX Filter Card
  • 10.
  • 11.
    IoT OSS/BSS Governance & MetadataMgmnt IAM & Fraud Analysis Common Graph Use Cases in Telco Digital Transformation • Smart Homes • Assurance • Fulfilment • CRM/Support • Planning & Optimisation • Regulatory compliance • Data Lineage • Consent Mgmnt • Identity & Access Mgmnt • CEM • Customer 360 • Graph Rules Engine
  • 12.
    In Network Operations NetworkService Assurance White Paper neo4j.com/whitepapers/white-paper-optimize-network-services-neo4j/
  • 13.
  • 14.
    Fault Management (Deep) Impact/RootCause Analysis 🏦 :DEPENDS_ON :DEPENDS_ON :DEPENDS_ON IF/AX2431 💥 Customer Event Correlation Event Prioritisation
  • 15.
    MATCH (fe:Link {linkId: $id})<-[:DEPENDS_ON*]-(s:Service) RETURN max(s.priority) AS severity { alarmType: “LOS”, notifyingEntity: “IF/AX/0/3”, …} Fault Management (Deep) Impact/Root Cause Analysis GET http://localhost:11001/engine/ia/s1_361_sdh {"severitySummaryCode": 4, "severitySummaryDesc": "MAJOR", "detail": [{"elem":"2217/ol",
 "impact" : 1, ...
  • 16.
    Graph Size: ~50Mnodes (avg depth: 6)Graph Size: ~1K nodes (avg depth: 5) Simulation: 128 clients, synchronous requests with1ms wait between requests 50000x increase in size of dataset -> 1.14x impact in query performance Graph Native Matters! Fault Management at Scale Matters (Deep) Impact/Root Cause Analysis
  • 17.
    Algorithms – CommunityDetection • Label Propagation ○ Spreads labels based on neighbors to infer clusters • Union Find / Connected Components ○ Finds groups of nodes that all have a path to each other
 • Strongly Connected Components ○ Finds groups of nodes that are all connected 
 to each other following the 
 direction of relationships • Louvain Modularity ○ Measures the presumed accuracy of community grouping • Triangle-Count & Clustering Coefficient ○ Measures the degree that nodes tend to cluster together Source: “Fast unfolding of communities in large networks” – Blondel, Guillaume, Lambiotte, Lefebvre
  • 18.
  • 19.
  • 20.
    Understanding Influence Source: “Robustness ofthe European power grids under intentional attack.” - R.V. Sole, M. Rosas-Casals, B. Corominas-Murtra, and S. Valverde. Source: “Network Science” - Barabasi Preventing 
 Cascading Failures with 
 4 Nodes Removed
  • 21.
  • 22.
  • 23.
    Traffic Engineering Diverse Routing PathAnalysis Least cost path from A to B + Dependency Analysis No shared underlying resources
  • 24.
    • Single-Source ShortestPath ○ Calculates “shortest” path between a node and all other nodes Algorithms - Pathfinding & Search • All-Pairs Shortest Path ○ Finds all shortest paths between all nodes
  • 25.
    • Parallel Breadth-FirstSearch & Depth-First Search ○ Traverses tree structure by exploring nearest neighbors (BFS) or down each branch (DFS) • Single-Source Shortest Path ○ Calculates path between a node and all other nodes Algorithms - Pathfinding & Search • All-Pairs Shortest Path ○ Calculates shortest path group with all shortest paths between nodes • Minimum Weight Spanning Tree ○ Calculates the path with the smallest value for visiting all nodes Least Cost Routing
  • 26.
  • 27.
  • 28.
    Neo4j Adoption Highlights MVPD 2of the Top 5 US multichannel video service providers have chosen Neo4j SW & Eqpmnt Vendors 3 of the 5 largest Telco software and equipment vendors in the world embed Neo4j in key products 2 of the 3 world’s largest CSP use Neo4j in mission critical solutions Largest Telcos (March 2018)
  • 29.
    Magic Quadrant forOperations Support Systems. March 2018
  • 30.
    Telecom is AllAbout Connections Accurate, real-time visibility for your network and services with a native-graph platform Uncover hidden dependencies for planning and predictions with optimized graph algorithms Let’s Get Started Together Telecom page neo4j.com/industries/telecom/ Network Service Assurance Paper neo4j.com/whitepapers/white-paper- optimize-network-services-neo4j/ Desktop Download neo4j.com/download/
  • 31.