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• ( ) • @kimuras• G(2007 )• ••

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

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Introduction

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Motivation for social graph analysis

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

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What is Social Graph?

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Feed Back

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Feed Back

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Feed Back

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Feed Back

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Feed Back

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Approach for SG analysis Feed Back

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The past work

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••

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Relational Databases Dump & Denormalizationfrom_id to_id id name age Key value1 2 1 Kimura 18 From:1 2,31 3 2 kato 45 From:2 32 3 3 ito 21 Prof:1 Kimura,18 Prof:2 Kato,45

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Relational Databases Dump & reimplementation Denormalizationfrom_id to_id id name age Key value11 2 3 maintenance cost 1 2 Kimura kato 18 45 From:1 From:2 2,3 32 3 3 ito 21 Prof:1 Kimuras,18 Prof:2 Kato,45 scalability

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Introduction to GraphDB

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What is graph

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What is graph Vertex (node : )

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What is graph Vertex (node : )Edge ( )

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

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What is graph Vertex (node : )Edge ( )

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What is graph Vertex (node : )Edge ( )

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What is graph Vertex (node : )Edge ( )

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

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What is GraphDB Vertex (node : )Edge ( )

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What is GraphDBID: 1 Vertex (node : )NAME: kimuraPROP: MaleAGE: 18Edge ( )

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What is GraphDBID: 1 Vertex (node : )NAME: kimuraPROP: MaleAGE: 18Edge ( ) ID: 2 NAME: ITO PROP: Female AGE: 21

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What is GraphDBID: 1 Vertex (node : )NAME: kimuraPROP: MaleAGE: 18Edge ( ) ID: 2 NAME: ITO PROP: Female AGE: 21

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What is GraphDBID: 1 Vertex (node : )NAME: kimuraPROP: MaleAGE: 18Edge ( ) ID: 2 NAME: ITO PROP: Female AGE: 21

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What is GraphDBID: 1 Vertex (node : )NAME: kimuraPROP: MaleAGE: 18 Edge ( ) ID: 2ID: 3 NAME: ITOLABEL: Like PROP: FemaleSince: 2011/08/06 AGE: 21OutGoing: 2

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What is GraphDBID: 1 Vertex (node : )NAME: kimuraPROP: MaleAGE: 18 Edge ( ) ID: 2ID: 3 NAME: ITOLABEL: Like PROP: FemaleSince: 2011/08/06 AGE: 21OutGoing: 2

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What is GraphDBID: 1 Vertex (node : )NAME: kimuraPROP: MaleAGE: 18 Edge ( ) ID: 2ID: 3 NAME: ITOLABEL: Like PROP: FemaleSince: 2011/08/06 AGE: 21OutGoing: 2

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

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Introduction to Neo4j

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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 HighlyAvailableGraphDatabaseStandalone Neo4j Server Neo4j Server high availability mode http://neo4j.org/

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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

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Introduction simple Neo4j usecase Single node Multi nodeEmbedded Analyses system Analyses system Analyses system Analyses systemServer

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Introduction simple Neo4j usecase Single node Multi nodeEmbedded Analyses system Analyses system Analyses system Analyses systemServer

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Introduction simple Neo4j usecase Single node Multi node Analyses systemEmbedded Analyses system Analyses system Analyses systemServer

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Introduction simple Neo4j usecase Single node Multi node Analyses systemEmbedded Analyses system Analyses system Analyses systemServer

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

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Insert vertices, make relationshippublic 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(); }}

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Insert vertices, make relationshippublic 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(); }}

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Insert vertices, make relationshippublic 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(); }}

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Insert vertices, make relationshippublic 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(); }}

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Insert vertices, make relationshippublic 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(); }}

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Insert vertices, make relationshippublic 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(); }}

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Insert vertices, make relationshippublic 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(); }}

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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(); }}

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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(); } }

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Traversal sample Order.BREADTH_FIRST•

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Traversal sample Order.BREADTH_FIRST•

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Traversal sample Order.BREADTH_FIRST•

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Traversal sample Order.BREADTH_FIRST•

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Traversal sample Order.BREADTH_FIRST•

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Traversal sample Order.BREADTH_FIRST•

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Traversal sample Order.DEPTH_FIRST•

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Traversal sample Order.DEPTH_FIRST•

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Traversal sample Order.DEPTH_FIRST•

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Traversal sample Order.DEPTH_FIRST•

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Traversal sample Order.DEPTH_FIRST•

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Traversal sample Order.DEPTH_FIRST•

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

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

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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

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Introduction to Analysis Sample

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Architecture Service Database Analysis Visualization(Social Graph)

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

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Centrality ( )• = Pagerank

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•• = Vertex ( )

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•• = Vertex ( ) 1 1 1

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•• = Vertex ( ) 2 1 1 2 1 2

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•• = Vertex ( ) 2 1 1 2 1 2

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•• = Vertex ( ) 2 1 1 4 2 1 2

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•• = Vertex ( ) 2 1 1 4 2 1 2

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mixi • 1000 • summaryMin 1st Que. Median Mean 3rd Que. Max1.00 3.00 10.00 25.69 30.00 903.00

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• • ≒

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• • ≒ =0/3=0

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• • ≒ =0/3=0 =1/3

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• • ≒ =0/3=0 =1/3 =2/3

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• • ≒ =0/3=0 =1/3 =2/3 =3/3=1

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• 1000• summaryMin 1st Que. Median Mean 3rd Que. Max0.00 0.00 0.1157 0.2071 0.2667 1.000

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• 25 0.08

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• 14 0.17

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• 10 0.68

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• 4 1

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Visualization Sample

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

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• Social Graph •• GraphDB• Neo4j• R• Visualization

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Thanks!