Graph Exploration: Taking the User into the Loop

Transcription

1 Grph Explortion: Tking the User into the Loop Dvide Mottin, Anj Jentzsch, Emmnuel Müller Hsso Plttner Institute, Potsdm, Germny 2016/10/24 CIKM2016, Indinpolis, US

2 Who we re Dvide Mottin grph mining, novel query prdigms, interctive methods Anj Jentzsch Linked Open Dt, grph explortion, dt profiling Emmnuel Müller grph mining, strem mining, clustering nd outlier mining on grphs, strems, nd trditionl dtbses D. MOTTIN, A. JENTZSCH, E. MÜLLER 2

3 Big dt nd novice users D. MOTTIN, A. JENTZSCH, E. MÜLLER 3

4 Dt explortion Efficiently extrcting knowledge from dt even if we do not know exctly wht we re looking for Idreos et l., Overview of Dt Explortion Methods, SIGMOD 2015 D. MOTTIN, A. JENTZSCH, E. MÜLLER 4

5 The importnce of grphs Socil Networks Complex Ubiquitous Lrge Vluble Rod Networks Recommendtion Grphs Knowledge Grphs D. MOTTIN, A. JENTZSCH, E. MÜLLER 5

6 Lost in the grph? D. MOTTIN, A. JENTZSCH, E. MÜLLER 6

7 Current: Visuliztion tools Severl visuliztion tools: Generl: Gephi, GrphViz, Biologicl: Cytoscpe, Network Workbench Socil: EgoNet, NodeXL,... Reltionl: Tulip but No Sclbility to lrge networks! No for novice users Limited expressivity D. MOTTIN, A. JENTZSCH, E. MÜLLER 7

8 Current: Query lnguges SELECT?nme?emil WHERE {?person fof:person.?person fof:nme?nme.?person fof:mbox?emil. } Query lnguges ARE: Expressive Powerful Sclble Compct SPARQL g.v().hslbel('movie').s('','b'). where(ine('rted').count().is(gt(10))). select('','b'). by('nme'). by(ine('rted').vlues('strs').men()). order(). by(select('b'),decr). limit(10 GREMLIN MATCH (node1:lbel1)-->(node2:lbel2) WHERE node1.propertya = {vlue} RETURN node2.propertya, node2.propertyb but Not user friendly No guided serch Not interctive Not sclble CYPHER D. MOTTIN, A. JENTZSCH, E. MÜLLER 8

9 This tutoril is bout Algorithms for helping the user finding the wnted informtion Approximte serch on grphs to ssist the user in finding the informtion Interctive methods on grphs bsed on user feedbck Automticlly discovery of portions of grphs using exmples NOT bout Visuliztion methods for grphs Query lnguges nd semntics Efficient indexing methods Pure mchine lerning on grphs D. MOTTIN, A. JENTZSCH, E. MÜLLER 9

10 Our grph explortion txonomy Explortory Grph Anlysis Focused Grph Mining Refinement of Query Results D. MOTTIN, A. JENTZSCH, E. MÜLLER 10

11 Grph explortion txonomy Explortory Grph Anlysis Other politicins like Angel Merkel? Schröder chncellor Schröder chncellor Merkel chncellor Germny Two explortory options: 1. An imprecise query Imprecise President ofmtch Query is n exmple Guch President of Merkel? 2. A by-exmple query Merkel Chncellor Germny D. MOTTIN, A. JENTZSCH, E. MÜLLER 11

12 Grph explortion txonomy Focused Grph Mining How cn I see only the prt of the grph I m interested in? Trgeted nlysis on lrge grphs 1. Focused grph clustering 2. Spce restriction methods 3. Grph Reweighting They ll like the Colts Ego-net nlysis D. MOTTIN, A. JENTZSCH, E. MÜLLER 12

13 Grph explortion txonomy Refinement of Query Results Where is this molecule contined? OH O S O 5 Too mny results! results Deling with generic queries: 1. Reformultion nd refinement 2. Top-k results 3. Skyline queries Query reformultions ODominnce reltion O OH S O OH S O H CH 3 SH 270 results 220 results D. MOTTIN, A. JENTZSCH, E. MÜLLER 13

14 Tutoril outline Bckground (5 min) Grph models, subgrph isomorphism, subgrph mining, grph clustering Explortory Grph Anlysis (35 min) Focused Grph Mining (35 min) Refinement of Query Results (35 min) Rel World-Use Cse (15min) Linked Dt grphs Chllenges nd discussion D. MOTTIN, A. JENTZSCH, E. MÜLLER 14

15 Where we re Bckground (5 min) Grph models, subgrph isomorphism, subgrph mining, grph clustering Explortory Grph Anlysis (35 min) Focused Grph Mining (35 min) Refinement of Query Results (35 min) Rel World-Use Cse (15min) Linked Dt grphs Chllenges nd discussion D. MOTTIN, A. JENTZSCH, E. MÜLLER 15

16 Grphs b c G = (V, E,p) E) l) Vertices Edges Lbeling Probbility function!: # % Σ 0.3 b 0.8 b c0.6 Undirected Grphs Co-uthorship, Rods, Biologicl Directed grphs Follows, Lbeled Grphs Knowledge grphs, Probbilistic grphs Cusl grphs D. MOTTIN, A. JENTZSCH, E. MÜLLER 16

17 Grph dtbses (set of grphs) b c c b d b c b b G 1 G 2 G 3 ( = * +, * -,, * /, * 0 = # 0, % 0,! 0,! 0 : % 0 # 0 Σ Set of smll lbeled grphs Chemicl compounds, Business models, 3D objects D. MOTTIN, A. JENTZSCH, E. MÜLLER 17

18 Grph Isomorphism G 1 G 2 f Given two grphs,* + : # +, % +,! +, * - : # -, % -,! - * + is isomorphic * - iff exists bijective function 4: # + # - s.t.: 1. For ech 5 + # +,! 5 + =!(4 5 + ) , ; + % + iff 4 5 +, 4 ; + % - GRAPH MINING WS

19 Subgrph Isomorphism Q G G A grph,<: # =, % =,! = is subgrph isomorphic to grph *: #, %,! if exists subgrph * > *, isomorphic to Q D. MOTTIN, A. JENTZSCH, E. MÜLLER 19

20 Frequent Subgrph Mining c c Problem Find ll subgrphs of G tht pper t times b c = 2, the frequent subgrphs re (only edge lbels), b, c -, -c, b-c, c-c -c- b Exponentil number of ptterns!!! G D. MOTTIN, A. JENTZSCH, E. MÜLLER 20

21 Grph Clustering nd Community Detection Given: grph with nodes, edges, lbels G = (V, E, l) b c Vertices Edges Lbeling function!: # % Σ Discover: prtitioning of communities c C = {C 1, C 2, C 3,, C k } b Optimize given qulity criterion Q(C), e.g. Modulrity or other mesures Is n NP-hrd problem to find the optiml prtitioning D. MOTTIN, A. JENTZSCH, E. MÜLLER 21