chengqi zhang graph processing and mining in the era of big data
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Graph Processing and Mining in the Era of Big
DataChengqi Zhang
Centre for Quantum Computation & Intelligent Systems (QCIS)
University of Technology, Sydney (UTS)
Outline Background Challenges and Opportunities Our Work: Graph Semantics Our Work: Graph Mining Our Work: Query Processing Our Work: Indexing Our Work: Computing Models Graph Processing System Design Future Developments
Graph Everywhere!
Social NetworkFacebook, Twitter
Web GraphGoogle, Yahoo
Road Network
The Internet of Things
Big Data Characteristics
Big Data
Volume• Petabytes• Records• Transactions
Velocity• Batch• Real time• Streaming
Variety• Structured• Unstructure
d• Semi-
structured
Graph in Big Data: Volume
• 1.23 billon active users in 2013• 190 friends/user on average• 500 TB data/day in 2012
• 2.1 billion webpages in 2000• 15 billion edges in 2000• 20 PB data/day in 2008
• 180-200 PB data in 2011
• 6.5 PB data + 50 TB/day in 2009
Graph in Big Data: Velocity
• Fast flowing data• Evolving data structures and relationships
Graph in Big Data: Variety
• Directed vs Undirected• Labeled vs Unlabeled • Weighted vs Unweighted• Heterogeneous vs homogeneous
Outline Background Challenges and Opportunities Our Work: Graph Semantics Our Work: Graph Mining Our Work: Query Processing Our Work: Indexing Our Work: Computing Models Graph Processing System Design Future Developments
Challenges and Opportunities
New Graph Semantics (Variety)
New Query Processing Algorithms (Volume & Velocity)New Indexing Techniques (Volume &
Velocity) New Computing Models (Volume)
New Graph Mining Tasks (Variety)
New Graph Semantics
Traditional (Google)• Input: keywords• Output: webpages
containing keywords• Ranked by PageRank
New (Google)• Input: keywords• Output: knowledge
graph/subgraph• Ranking should
consider both structural and content information
New Graph Mining Tasks
Chemical Compound Database
Chemical Features
Team of Experts
Several Years
Graph Mining
Several Hours
New Query Processing Algorithms
LocationRelationship
Text
Spatial query processing, nearest neighbor search …
Link analysis, shortest path search, community detection …
Text processing, string matching, semantic analysis …
All of these should be processed inMilliseconds
New Indexing Techniques
Traditional: webpages, files ?
Hash table, B-tree, Inverted Index …
New: subgraphs, trees, paths ?What’s more
Graph is Frequently Changing…
New Computing Models
Single Machine vs Multiple Machines
Internal Algorithms vs External Algorithms
Single Core vs Multiple Cores
Outline Background Challenges and Opportunities Our Work: Graph Semantics Our Work: Graph Mining Our Work: Query Processing Our Work: Indexing Our Work: Computing Models Graph Processing System Design Future Developments
Structural Keyword Search
Jim, data mining Jim
data mining
data mining
Jim
Jim, data mining
data miningJim
data miningJim
Traditional: Content Keyword Search
New: Structural Keyword Search
VS
Our Work:• ICDE’07: Finding Top-K Min-Cost Connected Trees in Databases• SIGMOD’09: Keyword Search in Databases: The Power of RDBMS• Morgan & Claypool 2009 (Book): Keyword Search in Databases• VLDBJ’11: Scalable Keyword Search on Large Data Streams• ICDE’11 & TKDE’12: Computing Structural Statistics by Keywords in
Databases
Graph Matching
MatchGraph 1 Graph 2
2
41
7
53
6
2
41
7
1
53
6
Graph PatternMatch
NP-Hard Problems
Our Work:• EDBT’12: Finding Top-K Similar Graphs in Graph Databases• CIKM’11 & VLDBJ’13: High Efficiency and Quality: Large Graphs
Matching• VLDB’14: Leveraging Graph Dimensions in Online Graph Search
Community Detection
?What is a community in a graph?
A cohesive subgraph?A dense subgraph?
Everyone is highly connected to others?Everyone is with small distance with others?
An Example: k-core
1-core 2-core
3-core
Community Detection
Graph 3-core
4-clique 3-edge-cc 4-truss
? Other Semantics?
Our Work:• SIGMOD’13: Efficiently Computing k-Edge Connected Components via
Graph Decomposition• SIGMOD’14: Querying k-truss Community in Large and Dynamic Graphs• VLDB’15: Influential Community Search in Large Networks• KDD’15: Locally Densest Subgraph Discovery
Influential Community (VLDB’15)
Which are the most influential research groups?
A Collaboration Network
Locally Densest Subgraph (KDD’15)
Which are the most representative densesubgraphs?
Outline Background Challenges and Opportunities Our Work: Graph Semantics Our Work: Graph Mining Our Work: Query Processing Our Work: Indexing Our Work: Computing Models Graph Processing System Design Future Developments
Graph Classification+ -+
++
-
--
Graph Database
…Frequent Subgraphs
…Optimal Subgraphs Classifier
1
2
3
4
1
2
3
+ -++
+-
--
Graph Database
…Optimal Subgraphs Classifier
+ -++
+-
--
Graph Database
…Optimal Subgraphs Classifier
1 2 3
Traditional: 3 Phases
Our work (CIKM’12): 2 Phases
Our work (PR’15): 1 Phase
Direct Selection
Direct Selection
Our Work:• CIKM’12: Graph Classification: A Diversified Discriminative Feature Selection
Approach• ICDE’13: Graph Stream Classification using Labeled and Unlabeled Graphs• IJCAI’13: Graph Classification with Imbalanced Class Distributions and Noise• TKDE’14: Bag Constrained Structure Pattern Mining for Multi-Graph
Classification• SDM’14: Multi-Graph Learning with Positive and Unlabeled Bags• ICDM’14: Multi-Graph-View Learning for Graph Classification• IJCAI’15: Multi-Graph-View Learning for Complicated Object Classification• TKDE’15: CogBoost: Boosting for Fast Cost-sensitive Graph Classification• PR’15: Finding the Best not the Most: Regularized Loss Minimization
Subgraph Selection for Graph Classification
Outline Background Challenges and Opportunities Our Work: Graph Semantics Our Work: Graph Mining Our Work: Query Processing Our Work: Indexing Our Work: Computing Models Graph Processing System Design Future Developments
Polynomial DelayEnumeration Problems in Graph?• Structural keyword search• Community detection• Graph pattern matching• Similar graph search
Polynomial Time w.r.t. Input?Output can be exponential
Impossible!So…
Polynomial Total: Polynomial to Input+Output
Possible, but…
Polynomial Delay
time… … …
Many answers!
Can’t you be faster?
time
How about this?
Polynomial Total
Polynomial Delay
New SolutionPolynomial Delay: Delay Time Polynomial to InputTotal time is still large,
but…
Our Work:• ICDE’09: Querying Communities in Relational Databases• Algorithmica’13: Fast Maximal Cliques Enumeration in Sparse Graphs• EDBT’15: Efficiently Computing Top-K Shortest Path Join• VLDB’15: Optimal Enumeration - Efficient Top-k Tree Matching
Diversified Graph Search
Enumeration Problems in Graph• Structural keyword search• Community detection• Graph pattern matching• Frequent graph pattern
mining• …
Top-6 Answers
Top-6 Diversified Answers
Top-K Densest Communities? Consider Diversity?
GraphOur Work:
• VLDB’12: Diversifying Top-K Results• CIKM’12: Graph Classification: A Diversified Discriminative Feature
Selection Approach• VLDB’13 & VLDBJ’15: Top-K Structural Diversity Search in Large
Networks• ICDE’15: Diversified Top-K Clique Search
Diversified Top-K Cliques (ICDE’15)
AB
E
J
G H
KI
F
C
D
Maximum CliqueTop-2 Maximum Cliques
Too much overlap!
Diversified Top-2 Maximum Cliques
Cover All Nodes!
Problem Statement:Compute k Cliques to Cover Maximum Number of Nodes
Outline Background Challenges and Opportunities Our Work: Graph Semantics Our Work: Graph Mining Our Work: Query Processing Our Work: Indexing Our Work: Computing Models Graph Processing System Design Future Developments
Dijkstra’s Algorithm?
Shortest Path Computation
A* Algorithm?
Traverse the whole graph in worst case
Precompute all-pair shortest paths?Impractical!
Our approach (VLDBJ’12):Compute a subset of pairs
VLDBJ’12
Our Work:• VLDBJ’12: The Exact Distance to Destination in Undirected World• VLDB’13: Top-K Nearest Keyword Search on Large Graphs• VLDBJ’13: Computing Weight Constraint Reachability in Large Networks• SIGMOD’15: Index-based Optimal Algorithms for Computing Steiner
Components with Maximum Connectivity
Outline Background Challenges and Opportunities Our Work: Graph Semantics Our Work: Graph Mining Our Work: Query Processing Our Work: Indexing Our Work: Computing Models Graph Processing System Design Future Developments
Our Focus
I/O Efficient Computation
Control
Data-path
Secondary
Storage(Disk)
Processor
Registers
MainMemory(DRAM)
Second
LevelCache(SRAM
)
On-ChipCache
1 ns 10 msSpeed: 5 ns 100 ns100B TBSize: KB GB
Tertiary
Storage
(Tape)
10 secPB
10 nsMB
Graph ProblemsMain Memory vs Disk
Sequential I/O vs Random I/OExternal vs Semi-external
Partition based vs Nested loop based
Our Work:• EDBT’12: I/O Cost Minimization: Reachability Queries Processing over
Massive Graphs• SIGMOD’13 & VLDBJ’14: I/O Efficient: Computing SCCs in Massive
Graphs• ICDE’14: Contract & Expand: I/O Efficient SCCs Computing• SIGMOD’15: Divide and Conquer - I/O Efficient Depth-First Search
Parallel Computation
Memory
Core Core
L1 L1
L2Switch
Core Core
L1 L1
L2Switch
CPU
DiskMemory
CPU
DiskMemory
CPU
DiskMemory
Network
• Computation SensitiveMulticore
• Shared Memory• Separated L1 Cache• Reduce Cache Miss
• Data SensitiveDistributed Computing
• Shared Nothing• Separated CPU, memory, Disk• Reduce Communication
• Divide Tasks • Divide Data
Multicore Distributed ComputingMapReduce, BSP…
Comparison…
Our Work:• VLDB’10: Ten Thousand SQLs: Parallel Keyword Queries Computing• SIGMOD’14: Scalable Big Graph Processing in MapReduce• VLDB’15: Scalable Subgraph Enumeration in MapReduce
Outline Background Challenges and Opportunities Our Work: Graph Semantics Our Work: Graph Mining Our Work: Query Processing Our Work: Indexing Our Work: Computing Models Graph Processing System Design Future Developments
Graph Processing System Design
Objective 1:Extracting Primitive Operators from DB
and DMChallenge: Completeness & Minimality
Objective 2:Scalable Processing Techniques
Challenge: Guarantee of “Optimality”
Objective 3:Characterizing Real-time Tractability
Challenge: Hard & Risky
Graph System Structure
Data EnvironmentsStatic, Streaming, Dynamic Graph, Probabilistic, Spatial, Evolving Graph, Random Graph
Computing ModelsMain-memory, Distributed/Cloud/MapReduce/BSP/Spark/Pregel,
SSD, Parallel/Multi-core, External/Semi-External
Advanced ApplicationsSocial Network (Twitter, Facebook), Geo Social (Checkin), Chemical, Biological,
Web Graph (Wiki), Collaboration (DBLP), Public Opinion Mining
Query Primitives• Given a Graph Pattern:
Similarity, Pattern, Sub/Super Graph• Given a Set of Nodes:
Topology: SimRank, Connectivity, Path
K-hop, Flow, Community, Reachability• Given a Set of Keywords:
Knowledge Graph, Attributed Graph, RDF
Mining Primitives• Subgraph Based:
Cohesive Subgraph Mining
Community DetectionGraph Clustering,
PartitionFrequent Subgraph
Mining• Aggregate Based:
PageRank, Outlier, Anonymity
Influence Maximization
Primitive Computing ParadigmsJoins, BFS, DFS, Topological Sort, Spanning Tree, Diameter
Our Current Development
Computing ModelsSIGMOD’15b, VLDB’15a, VLDBJ’14, SIGMOD’14a, SIGMOD’13a,
EDBT’12b, VLDB’10
Advanced ApplicationsVLDB’15c, VLDBJ’13b, VLDB’13a, TKDE’12, ICDE’11, CIKM’11b
Query Primitives
VLDBJ’15, SIGMOD’15a, VLDB’15b, KDD’15, ICDE’15b,
VLDB’13b, VLDBJ’12,EDBT’12a, ICDE’09b, ICDE’07
Mining PrimitivesAlgorithmica’13, CIKM’12,
CIKM’11a, IJCAI’15,TKDE’14, SDM’14,
ICDE’13a, TKDE’15, ICDE’13b,
ICDM’13IJCAI’13, ICDM’14, PR’15
Primitive Computing ParadigmsICDE’15a, EDBT’15, ICDE’14, VLDB’14, SIGMOD’13b, VLDBJ’13a, VLDB’12,
Data EnvironmentsSIGMOD’14b, VLDBJ’11, SIGMOD’09, ICDE’09a, EDBT’08, SSDBM’08
Outline Background Challenges and Opportunities Our Work: Graph Semantics Our Work: Graph Mining Our Work: Query Processing Our Work: Indexing Our Work: Computing Models Graph Processing System Design Future Developments
Future Developments
Social Network Recommendation
Location Based Social Network
Big Graph Processing in CloudMassive Graph Matching
Graph Summary
Graph Stream
Personalized CommunitySearchHigh Influence Community
SearchGraph Clustering in Cloud
Massive Uncertain Graph
…
Conclusion
Mining and Query Processing
The Era of Big Data
Indexing
Semantics
Computing Model
Big Graph: Larger, More ComplexMore Challenges!
More Opportunities to Explore the Unknown World!
Aknowledgements
1. Dr Lu Qin2. Prof. Xingquan Zhu3. Mr Jia Wu4. Mr Shirui Pan
References1. Jeffrey Xu Yu, Lu Qin, and Lijun Chang: Keyword Search in Databases, published by
Morgan & Claypool, 2009.2. Xin Huang, Hong Cheng, Rong-Hua Li, Lu Qin, and Jeffrey Xu Yu: Top-K Structural
Diversity Search in Large Networks, in the International Journal on Very Large Data Bases (VLDBJ), Vol. 24, No. 3, Pages 319-343, 2015.
3. Zhiwei Zhang, Jeffrey Xu Yu, Lu Qin, Lijun Chang, and Xuemin Lin: I/O Efficient: Computing SCCs in Massive Graphs, in the International Journal on Very Large Data Bases (VLDBJ), Vol. 24, No. 2, Pages 245-270, 2014.
4. Yuanyuan Zhu, Lu Qin, Jeffrey Xu Yu, Yiping Ke, and Xuemin Lin: High Efficiency and Quality: Large Graphs Matching, in the International Journal on Very Large Data Bases (VLDBJ), Vol. 22, No. 3, Pages 345-368, 2013.
5. Miao Qiao, Hong Cheng, Lu Qin, Jeffrey Xu Yu, Philip S. Yu, and Lijun Chang: Computing Weight Constraint Reachability in Large Networks, in the International Journal on Very Large Data Bases (VLDBJ), Vol. 22, No. 3, Pages 275-294, 2013.
6. Lijun Chang, Jeffrey Xu Yu, and Lu Qin: Fast Maximal Cliques Enumeration in Sparse Graphs, in Algorithmica, Vol. 66, No. 1, Pages 173-186, 2013.
7. Lu Qin, Jeffrey Xu Yu, and Lijun Chang: Computing Structural Statistics by Keywords in Databases. Invited paper by IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 24, No. 10, Pages 1731-1746, 2012.
8. Lijun Chang, Jeffrey Xu Yu, Lu Qin, Hong Cheng, and Miao Qiao: The Exact Distance to Destination in Undirected World, in the International Journal on Very Large Data Bases (VLDBJ), Vol. 21, No. 6, Pages 869-888, 2012.
9. Lu Qin, Jeffrey Xu Yu, and Lijun Chang: Scalable Keyword Search on Large Data Streams, in the International Journal on Very Large Data Bases (VLDBJ), Vol. 20, No. 1, Pages 35-57, 2011.
10. Lu Qin, Rong-Hua Li, Lijun Chang, and Chengqi Zhang: Locally Densest Subgraph Discovery, to appear in Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'15), 2015.
References11. Longbin Lai, Lu Qin, Xuemin Lin, and Lijun Chang: Scalable Subgraph Enumeration in
MapReduce, to appear in Proceedings of the Very Large Database Endowment (VLDB), 2015. 12. Lijun Chang, Xuemin Lin, Lu Qin, Jeffrey Xu Yu, Wenjie Zhang: Index-based Optimal
Algorithms for Computing Steiner Components with Maximum Connectivity, to appear in Proceedings of ACM Conference on Management of Data (SIGMOD'15), 2015.
13. Zhiwei Zhang, Jeffrey Xu Yu, Lu Qin, and Zechao Shang: Divide & Conquer: I/O Efficient Depth First Search, to appear in Proceedings of ACM Conference on Management of Data (SIGMOD'15), 2015.
14. Lijun Chang, Xuemin Lin, Lu Qin, Jeffrey Xu Yu, and Jian Pei: Efficiently Computing Top-K Shortest Path Join, in Proceedings of the 18th International Conference on Extending Database Technology (EDBT'15), 2015.
15. Rong-Hua Li, Jeffrey Xu Yu, Lu Qin, Rui Mao, and Tan Jin: On Random Walk Based Graph Sampling, in the 31st IEEE International Conference on Data Engineering (ICDE'15), 2015.
16. Long Yuan, Lu Qin, Xuemin Lin, Lijun Chang, and Wenjia Zhang: Diversified Top-K Clique Search, in the 31st IEEE International Conference on Data Engineering (ICDE'15), 2015.
17. Lijun Chang, Xuemin Lin, Wenjie Zhang, Jeffrey Xu Yu, Ying Zhang, and Lu Qin: Optimal Enumeration: Efficient Top-k Tree Matching, in Proceedings of the Very Large Database Endowment (VLDB), Vol. 8, No. 5, Pages 533-544, 2015.
18. Rong-Hua Li, Lu Qin, Jeffrey Xu Yu, and Rui Mao: Influential Community Search in Large Networks, in Proceedings of the Very Large Database Endowment (VLDB), Vol. 8, No. 5, Pages 509-520, 2015.
19. Yuanyuan Zhu, Jeffrey Xu Yu, and Lu Qin: Leveraging Graph Dimensions in Online Graph Search, in Proceedings of the Very Large Database Endowment (VLDB), Vol. 8, No. 1, Pages 85-96, 2015.
20. Xin Huang, Hong Cheng, Lu Qin, Wentao Tian, and Jeffrey Xu Yu: Querying K-Truss Community in Large and Dynamic Graphs, in Proceedings of ACM Conference on Management of Data (SIGMOD'14), Pages 1311-1322, 2014.
References21. Lu Qin, Jeffrey Xu Yu, Lijun Chang, Hong Cheng, Chengqi Zhang, and Xuemin Lin: Scalable Big
Graph Processing in MapReduce, in Proceedings of ACM Conference on Management of Data (SIGMOD'14), Pages 827-838, 2014.
22. Zhiwei Zhang, Lu Qin, and Jeffrey Xu Yu: Contract & Expand: I/O Efficient SCCs Computing, in the 30th IEEE International Conference on Data Engineering (ICDE'14), Pages 208-219, 2014.
23. Xin Huang, Hong Cheng, Rong-Hua Li, Lu Qin, and Jeffrey Xu Yu: Top-K Structural Diversity Search in Large Networks, in Proceedings of the Very Large Database Endowment (VLDB), Vol. 6, No. 13, Pages 1618-1629, 2013.
24. Miao Qiao, Lu Qin, Hong Cheng, Jeffrey Xu Yu, and Wentao Tian: Top-K Nearest Keyword Search on Large Graphs, in Proceedings of the Very Large Database Endowment (VLDB), Vol. 6, No. 10, Pages 901-912, 2013.
25. Lijun Chang, Jeffrey Xu Yu, Lu Qin, Xuemin Lin, Chengfei Liu, and Weifa Liang: Efficiently Computing k-Edge Connected Components via Graph Decomposition, in Proceedings of ACM Conference on Management of Data (SIGMOD'13), Pages 205-216, 2013.
26. Zhiwei Zhang, Jeffrey Xu Yu, Lu Qin, Lijun Chang, and Xuemin Lin: I/O Efficient: Computing SCCs in Massive Graphs, in Proceedings of ACM Conference on Management of Data (SIGMOD'13), Pages 181-192, 2013.
27. Yuanyuan Zhu, Jeffrey Xu Yu, Hong Cheng, and Lu Qin: Graph Classification: A Diversified Discriminative Feature Selection Approach, in Proceedings of 2012 ACM International Conference on Information and Knowledge Management (CIKM'12), Pages 205-214, 2012.
28. Lu Qin, Jeffrey Xu Yu, and Lijun Chang: Diversifying Top-K Results, in Proceedings of the Very Large Database Endowment (VLDB), Vol. 5, No. 11, Pages 1124-1135, 2012.
29. Yuanyuan Zhu, Lu Qin, and Jeffrey Xu Yu: Finding Top-K Similar Graphs in Graph Databases, in Proceedings of the 15th International Conference on Extending Database Technology (EDBT'12), Pages 456-467, 2012.
30. Zhiwei Zhang, Jeffrey Xu Yu, Lu Qin, Qing Zhu, and Xiaofang Zhou: I/O Cost Minimization: Reachability Queries Processing over Massive Graphs, in Proceedings of the 15th International Conference on Extending Database Technology (EDBT'12), Pages 468-479, 2012.
References31. Yuanyuan Zhu, Lu Qin, Jeffrey Xu Yu, Yiping Ke, and Xuemin Lin: High Efficiency and Quality: Large
Graphs Matching, in Proceedings of 2011 ACM International Conference on Information and Knowledge Management (CIKM'11), Pages 1755-1764, 2011.
32. Lijun Chang, Jeffrey Xu Yu, Lu Qin, Yuanyuan Zhu, and Haixun Wang: Finding Information Nebula over Large Networks, in Proceedings of 2011 ACM International Conference on Information and Knowledge Management (CIKM'11), Pages 1465-1474, 2011.
33. Lu Qin, Jeffrey Xu Yu, and Lijun Chang: Computing Structural Statistics by Keywords in Databases, in Proceedings of the 27th IEEE International Conference on Data Engineering (ICDE'11), Pages 363-374, 2011.
34. Lu Qin, Jeffrey Xu Yu, and Lijun Chang: Ten Thousand SQLs: Parallel Keyword Queries Computing, in Proceedings of the Very Large Database Endowment (VLDB), Vol. 3, No. 1, Pages 58-69, 2010.
35. Lu Qin, Jeffrey Xu Yu, and Lijun Chang: Keyword Search in Databases: The Power of RDBMS, in Proceedings of ACM Conference on Management of Data (SIGMOD'09), Pages 681-694, 2009.
36. Lu Qin, Jeffrey Xu Yu, Lijun Chang, and Yufei Tao: Querying Communities in Relational Databases, in Proceedings of the 25th IEEE International Conference on Data Engineering (ICDE'09), Pages 724-735, 2009.
37. Lu Qin, Jeffrey Xu Yu, Lijun Chang, and Yufei Tao: Scalable Keyword Search on Large Data Streams, in Proceedings of the 25th IEEE International Conference on Data Engineering (ICDE'09), Short Paper, Pages 1199-1202, 2009.
38. Lu Qin, Jeffrey Xu Yu, Bolin Ding, and Yoshiharu Ishikawa: Monitoring Aggregate k-NN Objects in Road Networks, in Proceedings of the 20th International Conference on Scientific and Statistical Database Management (SSDBM’08), Pages 168-186, 2008.
39. Bolin Ding, Jeffrey Xu Yu, and Lu Qin: Finding Time-Dependent Shortest Paths over Large Graphs, in Proceedings of the 11th International Conference on Extending Database Technology (EDBT'08), Pages 205-216, 2008.
40. Bolin Ding, Jeffrey Xu Yu, Shan Wang, Lu Qin, Xiao Zhang, and Xuemin Lin: Finding Top-k Min-Cost Connected Trees in Databases, in Proceedings of the 23rd IEEE International Conference on Data Engineering (ICDE'07), Pages 836-845, 2007. (Best Student Paper)
References41. Jia Wu, Xingquan Zhu, Chengqi Zhang, Philip S. Yu. Bag Constrained Structure Pattern
Mining for Multi-Graph Classification. IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol 26, No 10, pp.2382-2396, 2014.
42. Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Chengqi Zhang, Zhihua Cai. Multi-Graph Learning with Positive and Unlabeled Bags. SDM 2014: 217-225.
43. Jia Wu, Xingquan Zhu, Chengqi Zhang, Zhihua Cai: Multi-instance Multi-graph Dual Embedding Learning. ICDM’13, 2013: 827-836.
44. Jia Wu, Shirui Pan, Xingquan Zhu, Chengqi Zhang. Multi-Graph-View Learning for Complicated Object Classification. International Joint Conference on Artificial Intelligence (IJCAI’15), 2015
45. Shirui Pan, Jia Wu, and Xingquan Zhu, "CogBoost: Boosting for Fast Cost-sensitive Graph Classification", IEEE Transactions on Knowledge and Data Engineering (TKDE), Accepted, 2015.
46. Shirui Pan, Xingquan Zhu, Chengqi Zhang, and Philip S. Yu. "Graph Stream Classification using Labeled and Unlabeled Graphs", International Conference on Data Engineering (ICDE’13), 2013
47. Shirui Pan and Xingquan Zhu. "CGStream: Continuous Correlated Graph Query for Data Streams". 21st ACM International Conference on Information and Knowledge Management (CIKM), 2012.
48. Shirui Pan and Xingquan Zhu. "Graph Classification with Imbalanced Class Distributions and Noise", 23rd International Joint Conference on Artificial Intelligence (IJCAI), 2013
49. Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Chengqi Zhang, Zhihua Cai. "Multi-graph-view Learning for Graph Classification", Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM), 2014
50. Shirui Pan, Jia Wu, Xingquan Zhu, Guodong Long, Chentqi Zhang, “Finding the Best not the Most: Regularized Loss Minimization Subgraph Selection for Graph Classification”, to appear in Pattern Recognition (PR), 2015
Thank you!
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