2. • ( )
• @kimuras
• G(2007 )
•
•
•

3. Agenda
• Introduction
• The past work
• Introduction to GraphDB
• Introduction to Neo4j
• Introduction to analysis sample

4. Introduction

5. Motivation for social graph analysis

6. mixi
30000000
ID
22500000
# of member id
15000000
7500000
0
2007 2008 2009 2010 2011
year

7. What is Social Graph?

15. Feed Back

16. Feed Back

17. Feed Back

18. Feed Back

19. Feed Back

20. Approach for SG
analysis
Feed Back

21. The past work

22. •
•

23. Relational Databases
Dump &
Denormalization
from_id to_id id name age Key value
1 2 1 Kimura 18 From:1 2,3
1 3 2 kato 45 From:2 3
2 3 3 ito 21 Prof:1 Kimura,18
Prof:2 Kato,45

24. Relational Databases
Dump &
reimplementation Denormalization
from_id to_id id name age Key value
1
1
2
3
maintenance cost
1
2
Kimura
kato
18
45
From:1
From:2
2,3
3
2 3 3 ito 21 Prof:1 Kimuras,18
Prof:2 Kato,45
scalability

25. Introduction to GraphDB

26. What is graph

27. What is graph
Vertex (node : )

28. What is graph
Vertex (node : )
Edge ( )

29. What is graph
Vertex (node : )
Undirected graph ( )
Edge ( )

30. What is graph
Vertex (node : )
Edge ( )

31. What is graph
Vertex (node : )
Edge ( )

32. What is graph
Vertex (node : )
Edge ( )

33. What is graph
Vertex (node : )
Directed graph ( )
Edge ( )

34. What is GraphDB
Vertex (node : )
Edge ( )

35. What is GraphDB
ID: 1
Vertex (node : )
NAME: kimura
PROP: Male
AGE: 18
Edge ( )

36. What is GraphDB
ID: 1
Vertex (node : )
NAME: kimura
PROP: Male
AGE: 18
Edge ( )
ID: 2
NAME: ITO
PROP: Female
AGE: 21

37. What is GraphDB
ID: 1
Vertex (node : )
NAME: kimura
PROP: Male
AGE: 18
Edge ( )
ID: 2
NAME: ITO
PROP: Female
AGE: 21

38. What is GraphDB
ID: 1
Vertex (node : )
NAME: kimura
PROP: Male
AGE: 18
Edge ( )
ID: 2
NAME: ITO
PROP: Female
AGE: 21

39. What is GraphDB
ID: 1
Vertex (node : )
NAME: kimura
PROP: Male
AGE: 18
Edge ( )
ID: 2
ID: 3 NAME: ITO
LABEL: Like PROP: Female
Since: 2011/08/06 AGE: 21
OutGoing: 2

40. What is GraphDB
ID: 1
Vertex (node : )
NAME: kimura
PROP: Male
AGE: 18
Edge ( )
ID: 2
ID: 3 NAME: ITO
LABEL: Like PROP: Female
Since: 2011/08/06 AGE: 21
OutGoing: 2

41. What is GraphDB
ID: 1
Vertex (node : )
NAME: kimura
PROP: Male
AGE: 18
Edge ( )
ID: 2
ID: 3 NAME: ITO
LABEL: Like PROP: Female
Since: 2011/08/06 AGE: 21
OutGoing: 2

42. The implementations
for GraphDB
http://en.wikipedia.org/wiki/GraphDB

43. Introduction to Neo4j

44. GraphDB Neo4j
• True ACID transactions
• High availability
• Scales to billions of nods and relationships
• High speed querying through traversals
Single instance(GPLv3) Multiple instance(AGPLv3)
Embedded EmbeddedGraphDatabase HighlyAvailableGraphDatabase
Standalone Neo4j Server Neo4j Server high availability mode
http://neo4j.org/

45. Other my favorite features
for Neo4j
• RESTful APIs
• Query Language(Cypher)
• Full indexing
– lucene
• Implemented graph algorithm
– A*, Dijkstra
– High speed traverse
• Gremlin supported
– Like a query language
http://www.tinkerpop.com/post/4633229547/tinkerpop-graph-stack

46. Introduction simple Neo4j usecase
Single node Multi node
Embedded
Analyses system Analyses system
Analyses system Analyses system
Server

47. Introduction simple Neo4j usecase
Single node Multi node
Embedded
Analyses system Analyses system
Analyses system Analyses system
Server

48. Introduction simple Neo4j usecase
Single node Multi node
Analyses system
Embedded
Analyses system
Analyses system Analyses system
Server

49. Introduction simple Neo4j usecase
Single node Multi node
Analyses system
Embedded
Analyses system
Analyses system Analyses system
Server

50. Introduction to simple
embedded Neo4j
• Insert Vertices & make Relationships
• Single node & Embedded
• Traversal sample

51. Insert vertices,
make relationship
public final class InputVertex {
public static void main(final String[] args) {
GraphDatabaseService graphDb = new
EmbeddedGraphDatabase("/tmp/neo4j");
Transaction tx = graphDb.beginTx();
try {
Node firstNode = graphDb.createNode();
firstNode.setProperty("Name", "Kimura");
Node secondNode = graphDb.createNode();
secondNode.setProperty("Name", "Kato");
firstNode.createRelationshipTo(secondNode,
DynamicRelationshipType.withName("LIKE"));
tx.success();
} finally {
tx.finish();
}
graphDb.shutdown();
}
}

52. Insert vertices,
make relationship
public final class InputVertex {
public static void main(final String[] args) {
GraphDatabaseService graphDb = new
EmbeddedGraphDatabase("/tmp/neo4j");
Transaction tx = graphDb.beginTx();
try {
Node firstNode = graphDb.createNode();
firstNode.setProperty("Name", "Kimura");
Node secondNode = graphDb.createNode();
secondNode.setProperty("Name", "Kato");
firstNode.createRelationshipTo(secondNode,
DynamicRelationshipType.withName("LIKE"));
tx.success();
} finally {
tx.finish();
}
graphDb.shutdown();
}
}

53. Insert vertices,
make relationship
public final class InputVertex {
public static void main(final String[] args) { ID: 1
GraphDatabaseService graphDb = new NAME: kimura
EmbeddedGraphDatabase("/tmp/neo4j");
Transaction tx = graphDb.beginTx();
try {
Node firstNode = graphDb.createNode();
firstNode.setProperty("Name", "Kimura");
Node secondNode = graphDb.createNode();
secondNode.setProperty("Name", "Kato");
firstNode.createRelationshipTo(secondNode,
DynamicRelationshipType.withName("LIKE"));
tx.success();
} finally {
tx.finish();
}
graphDb.shutdown();
}
}

54. Insert vertices,
make relationship
public final class InputVertex {
public static void main(final String[] args) { ID: 1
GraphDatabaseService graphDb = new NAME: kimura
EmbeddedGraphDatabase("/tmp/neo4j");
Transaction tx = graphDb.beginTx();
try {
Node firstNode = graphDb.createNode();
firstNode.setProperty("Name", "Kimura");
Node secondNode = graphDb.createNode();
secondNode.setProperty("Name", "Kato");
firstNode.createRelationshipTo(secondNode,
DynamicRelationshipType.withName("LIKE"));
tx.success();
} finally {
tx.finish();
}
graphDb.shutdown();
}
}

55. Insert vertices,
make relationship
public final class InputVertex {
public static void main(final String[] args) { ID: 1
GraphDatabaseService graphDb = new NAME: kimura
EmbeddedGraphDatabase("/tmp/neo4j");
Transaction tx = graphDb.beginTx();
try {
Node firstNode = graphDb.createNode();
firstNode.setProperty("Name", "Kimura");
Node secondNode = graphDb.createNode();
secondNode.setProperty("Name", "Kato");
firstNode.createRelationshipTo(secondNode,
DynamicRelationshipType.withName("LIKE"));
tx.success();
} finally { ID: 2
tx.finish(); NAME: Kato
}
graphDb.shutdown();
}
}

56. Insert vertices,
make relationship
public final class InputVertex {
public static void main(final String[] args) { ID: 1
GraphDatabaseService graphDb = new NAME: kimura
EmbeddedGraphDatabase("/tmp/neo4j");
Transaction tx = graphDb.beginTx();
try {
Node firstNode = graphDb.createNode();
firstNode.setProperty("Name", "Kimura");
Node secondNode = graphDb.createNode();
secondNode.setProperty("Name", "Kato");
firstNode.createRelationshipTo(secondNode,
DynamicRelationshipType.withName("LIKE"));
tx.success();
} finally { ID: 2
tx.finish(); NAME: Kato
}
graphDb.shutdown();
}
}

57. Insert vertices,
make relationship
public final class InputVertex {
public static void main(final String[] args) { ID: 1
GraphDatabaseService graphDb = new NAME: kimura
EmbeddedGraphDatabase("/tmp/neo4j");
Transaction tx = graphDb.beginTx();
try {
Node firstNode = graphDb.createNode();
ID: 3
firstNode.setProperty("Name", "Kimura"); Relation: Like
Node secondNode = graphDb.createNode();
secondNode.setProperty("Name", "Kato");
firstNode.createRelationshipTo(secondNode,
DynamicRelationshipType.withName("LIKE"));
tx.success();
} finally { ID: 2
tx.finish(); NAME: Kato
}
graphDb.shutdown();
}
}

58. Batch Insert
• Non thread safe, non transaction
• But very fast!
public final class Batch {
public static void main(final String[] args) {
BatchInserter inserter = new BatchInserterImpl("/tmp/neo4j",
BatchInserterImpl.loadProperties("/tmp/neo4j.props"));
Map<String, Object> prop = new HashMap<String, Object>();
prop.put("Name", "Kimura");
prop.put("Age", 21);
long node1 = inserter.createNode(prop);
prop.put("Name", "Kato");
prop.put("Age", 21);
long node2 = inserter.createNode(prop);
inserter.createRelationship(node1, node2,
DynamicRelationshipType.withName("LIKE"), null);
inserter.shutdown();
}
}

59. Traversal sample
•
public static void main(final String[] args) {
GraphDatabaseService graphDB = new EmbeddedGraphDatabase(args[0]);
Node node = graphDB.getNodeById(1);
Traverser friends = node.traverse(
//
Order.DEPTH_FIRST, BREADTH_FIRST
//
StopEvaluator.END_OF_GRAPH, DEPTH_ONE
//
ReturnableEvaluator.ALL_BUT_START_NODE, ALL, isReturnableNode()
//
DynamicRelationshipType.withName("LIKE"),
//
Direction.OUTGOING); INCOMING, BOTH
for (Node nodeBuf : friends) {
TraversalPosition currentPosition = friends.currentPosition();
}
}

60. Traversal sample
Order.BREADTH_FIRST
•

61. Traversal sample
Order.BREADTH_FIRST
•

62. Traversal sample
Order.BREADTH_FIRST
•

63. Traversal sample
Order.BREADTH_FIRST
•

64. Traversal sample
Order.BREADTH_FIRST
•

65. Traversal sample
Order.BREADTH_FIRST
•

66. Traversal sample
Order.DEPTH_FIRST
•

67. Traversal sample
Order.DEPTH_FIRST
•

68. Traversal sample
Order.DEPTH_FIRST
•

69. Traversal sample
Order.DEPTH_FIRST
•

70. Traversal sample
Order.DEPTH_FIRST
•

71. Traversal sample
Order.DEPTH_FIRST
•

72. Neoclipse sample
http://wiki.neo4j.org/content/Neoclipse

73. experiment
• mixi Neo4j
•
• Machine: 24 core CPU, Memory 65GB
• Neo4j: BatchInsert, community, embedded
• Data
• 1500 6
513m17sec (about 8.6h)

74. Network Dataset
• Stanford Large Network Dataset Collection
• SNAP has a Wide variety of graph data!
Social Networks Communication networks
Citation networks Collaboration networks
Web graphs Product co-purchasing networks
Internet peer-to-peer networks Road networks
Autonomous systems graphs Signed networks
Wikipedia networks and metadata Memetracker and Twitter
http://snap.stanford.edu/data/index.html

75. Introduction to Analysis
Sample

76. Architecture
Service
Database Analysis Visualization
(Social Graph)

77. Introduction Analyses
Sample
• Centrality ( )
• Clustering coefficient ( )

78. Centrality ( )
• =
Pagerank

79. •
• = Vertex ( )

80. •
• = Vertex ( )
1 1
1

81. •
• = Vertex ( )
2
1 1
2
1
2

82. •
• = Vertex ( )
2
1 1
2
1
2

83. •
• = Vertex ( )
2
1 1
4
2
1
2

84. •
• = Vertex ( )
2
1 1
4
2
1
2

85. mixi
• 1000
• summary
Min 1st Que. Median Mean 3rd Que. Max
1.00 3.00 10.00 25.69 30.00 903.00

86. •
• ≒

87. •
• ≒
=0/3=0

88. •
• ≒
=0/3=0
=1/3

89. •
• ≒
=0/3=0
=1/3
=2/3

90. •
• ≒
=0/3=0
=1/3
=2/3
=3/3=1

91. • 1000
• summary
Min 1st Que. Median Mean 3rd Que. Max
0.00 0.00 0.1157 0.2071 0.2667 1.000

94. • 25 0.08

95. • 14 0.17

96. • 10 0.68

97. • 4 1

98. Visualization Sample

99. • 2hop Social Graph
• Edge
• ( )
• Vertex
• ( )
• Gephi
http://gephi.org/

101. • Social Graph
•
• GraphDB
• Neo4j
• R
• Visualization

102. Thanks!