Abstract
On one hand, compared with traditional relational and XML models, graphs have more expressive power and are widely used today. On the other hand, various applications of social computing trigger the pressing need of a new search paradigm. In this article, we argue that big graph search is the one filling this gap. We first introduce the application of graph search in various scenarios. We then formalize the graph search problem, and give an analysis of graph search from an evolutionary point of view, followed by the evidences from both the industry and academia. After that, we analyze the difficulties and challenges of big graph search. Finally, we present three classes of techniques towards big graph search: query techniques, data techniques and distributed computing techniques.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Cukier K. Data, data everywhere: a special report on managing information. Economist Newspaper, 2010
Ma S, Li J, Liu X, Huai J. Graph search: a new searching approach to the social computing era. Communications of CCF, 2012, 8(11): 26–31
Ma S, Cao Y, Wo T, Huai J. Social networks and graph matching. Communications of CCF, 2012, 8(4): 20–24
Ma S, Li J, Liu X, Huai J. Graph search in the big data era. Information and Communications Technologies, 2013, 6: 44–51
Tian Y, Patel J M. Tale: A tool for approximate large graph matching. In: Proceedings of IEEE the 24th International Conference on Data Engineering. 2008, 963–972
Fan W, Li J, Ma S, Tang N, Wu Y, Wu Y. Graph pattern matching: from intractable to polynomial time. Proceedings of the VLDB Endowment, 2010, 3(1): 264–275
Barcelo P, Hurtado C A, Libkin L, Wood P T. Expressive languages for path queries over graph-structured data. In: Proceedings of the 29th ACM Symposium on Principles of Database Systems. 2010, 3–14
Feng K, Cong G, Bhowmick S S, Ma S. In search of influential event organizers in online social networks. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. 2014, 63–74
Maserrat H, Pei J. Neighbor query friendly compression of social networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2010, 533–542
Schenker A, Last M, Bunke H, Kandel A. Classification of web documents using graph matching. International Journal of Pattern Recognition and Artificial Intelligence, 2004, 18(3): 475–496
Fan W, Li J, Ma S, Wang H, Wu Y. Graph homomorphism revisited for graph matching. Proceedings of the VLDB Endowment, 2010, 3(1): 1161–1172
Terveen L G, McDonald D W. Social matching: a framework and research agenda. ACM Transactions on Computer-Human Interaction, 2005, 12(3): 401–434
Ma S, Cao Y, Fan W, Huai J, Wo T. Capturing topology in graph pattern matching. Proceedings of the VLDB Endowment, 2011, 5(4): 310–321
Ma S, Cao Y, Fan W, Huai J, Wo T. Strong simulation: capturing topology in graph pattern matching. ACM Transactions on Database Systems, 2014, 39(1)
Eckerson W. Data quality and the bottom line: achieving business success through a commitment to high quality data. TDWI Report. 2002
Otto B, Weber K. From health checks to the seven sisters: the data quality journey at bt. Report: BT TR-BE HSG/CC CDQ/8. 2009
Fan W, Li J, Ma S, Tang N, Yu W. Interaction between record matching and data repairing. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data. 2011, 469–480
Ullmann J R. An algorithm for subgraph isomorphism. Journal of the ACM, 1976, 23(1): 31–42
Liu C, Chen C, Han J, Yu P S. Gplag: detection of software plagiarism by program dependence graph analysis. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006, 872–881
Ferrante J, Ottenstein K J, Warren J D. The program dependence graph and its use in optimization. ACM Transactions on Programming Languages and Systems, 1987, 9(3): 319–349
Rice M N, Tsotras V J. Graph indexing of road networks for shortest path queries with label restrictions. Proceedings of the VLDB Endowment, 2010, 4(2): 69–80
Cormen T H, Leiserson C E, Rivest R L, Stein C. Introduction to Algorithms. Cambridge: The MIT Press, 2001
Chen Z, Shen H T, Zhou X, Yu J X. Monitoring path nearest neighbor in road networks. In: Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data. 2009, 591–602
Chowdhury N M M K, Rahman M R, Boutaba R. Virtual network embedding with coordinated node and link mapping. In: Proceedings of IEEE 28th Conference on Computer Communications. 2009, 783–791
Conte D, Foggia P, Sansone C, Vento M. Thirty years of graph matching in pattern recognition. International Journal of Pattern Recognition and Artificial, 2004, 18(3): 265–298
Karypis G, Aggarwal R, Kumar V, Shekhar S. Multilevel hypergraph partitioning: applications in vlsi domain. IEEE Transactions on Very Large Scale Integration Systems, 1999, 7(1): 69–79
Fan W, Li J, Ma S, Tang N, Wu Y. Adding regular expressions to graph reachability and pattern queries. In: Proceedings of IEEE the 27th Conference on Data Engineering. 2011, 39–50
Hansen P B, ed. Classic Operating Systems. New York: Springer, 2001
Ramakrishnan R, Gehrke J. Database Management Systems. New York: McGraw-Hill Higher Education, 2000
Abiteboul S, Hull R, Vianu V. Foundations of Databases. Addison-Wesley, 1995
Sakr S, Pardede E, eds. Graph Data Management: Techniques and Applications. IGI Global, 2011
Malewicz G, Austern M H, Bik A J C, Dehnert J C, Horn I, Leiser N, Czajkowski G. Pregel: a system for large-scale graph processing. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. 2010, 135–146
Yang S, Wu Y, Sun H, Yan X. Schemaless and structureless graph querying. Proceedings of the VLDB Endowment, 2014, 7(7): 565–576
Beitzel S M, Jensen E C, Frieder O, Lewis D D, Chowdhury A, Kolcz A. Improving automatic query classification via semi-supervised learning. In: Proceedings of the 5th IEEE International Conference on Data Mining. 2005, 42–49
Shen D, Sun J T, Yang Q, Chen Z. Building bridges for web query classification. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2006, 131–138
Xing Q, Liu Y, Nie J Y, Zhang M, Ma S, Zhang K. Incorporating user preferences into click models. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management. 2013, 1301–1310
Hu B, Zhang Y, Chen W, Wang G, Yang Q. Characterizing search intent diversity into click models. In: Proceedings of the 20th International Conference on World Wide Web. 2011, 17–26
Maria G, Symeon P, Athena V. Massive graph management for the Web and Web 2.0. New Directions in Web Data Management 1. Springer, 2011, 19–58
Newman M, Barabási A L, Watts D J. The Structure and Dynamics of Networks. Princeton: Princeton University Press, 2006
Rahm E, Do H H. Data cleaning: problems and current approaches. IEEE Data Engineering Bulletin, 2000, 23(4): 3–13
Fan W, Li J, Ma S, Tang N, Yu W. Towards certain fixes with editing rules and master data. The International Journal on Very Large Data Bases, 2012, 21(2): 213–238
Henzinger M R, Henzinger T A, Kopke P W. Computing simulations on finite and infinite graphs. In: Proceedings of the 36th Annual Symposium on Foundations of Computer Science. 1995, 453–462
Ramalingam G, Reps T W. A categorized bibliography on incremental computation. In: Proceedings of the 20th Symposium on Principles of Programming Languages. 1993, 502–510
Ramalingam G, Reps T W. On the computational complexity of dynamic graph problems. Theoretical Computer Science, 1996, 158(1): 233–277
Dean J, Ghemawat S. Mapreduce: simplified data processing on large clusters. In: Proceedings of the 6th USENIX Conference on Operating System Design and Implementation. 2004, 137–149
Peng D, Dabek F. Large-scale incremental processing using distributed transactions and notifications. In: Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation. 2010, 1–15
Papadimitriou C H. Computational Complexity. Addison-Wesley, 1994
Yu W, Aggarwal C C, Ma S, Wang H. On anomalous hotspot discovery in graph streams. In: Proceedings of the 13th IEEE International Conference on Data Mining. 2013, 1271–1276
Aggarwal C C, Wang H. Managing and Mining Graph Data. New York: Springer, 2010
Jordan M I. Divide-and-conquer and statistical inference for big data. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 4–4
Kleiner A, Talwalkar A, Sarkar P, Jordan M I. The big data bootstrap. In: Proceedings of the 29th International Conference on Machine Learning. 2012, 1759–1766
Kernighan B W, Lin S. An efficient heuristic procedure for partitioning graphs. Bell System Technical Journal, 1970, 49(2): 291–307
Karypis G, Kumar V. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 1998, 20(1): 359–392
Yang S, Yan X, Zong B, Khan A. Towards effective partition management for large graphs. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. 2012, 517–528
Salomon D. Data compression: The Complete Reference. 4th ed. New York: Springer, 2007
Buehrer G, Chellapilla K. A scalable pattern mining approach to Web graph compression with communities. In: Proceedings of the 2008 International Conference on Web Search and Data Mining. 2008, 95–106
Adler M, Mitzenmacher M. Towards compressing Web graphs. In: Proceedings of Data Compression Conference. 2001, 203–212
Boldi P, Vigna S. The Web Graph framework I: compression techniques. In: Proceedings of the 13th International Conference on World Wide Web. 2004, 595–602
Feder T, Motwani R. Clique partitions, graph compression and speeding-up algorithms. Journal of Computer and System Sciences, 1995, 51(2): 261–272
Karande C, Chellapilla K, Andersen R. Speeding up algorithms on compressed Web graphs. In: Proceedings of the 2009 International Conference on Web Search and Data Mining. 2009, 272–281
Fan W, Li J, Wang X, Wu Y. Query preserving graph compression. In: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. 2012, 157–168
Baeza-Yates R A, Ribeiro-Neto B A. Modern Information Retrieval: the concepts and technology behind search. 2nd ed. Harlow: Pearson Education Ltd., 2011
Klein K, Kriege N, Mutzel P. CT-Index: Fingerprint-based graph indexing combining cycles and trees. In: Proceedings of IEEE the 27th International Conference on Data Engineering. 2011, 1115–1126
Lynch N A. Distributed Algorithms. San Francisco: Morgan Kaufmann, 1996
Peleg D. Distributed Computing: A Locality-Sensitive Approach. SIAM, 2000
Ma S, Cao Y, Huai J, Wo T. Distributed graph pattern matching. In: Proceedings of the 21st International Conference on World Wide Web. 2012, 949–958
Zaharia M, Chowdhury M, Das T, Dave A, Ma J, McCauly M, Franklin M J, Shenker S, Stoica I. Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation. 2012, 15–28
Gao J, Zhou J, Zhou C, Yu J X. Glog: A high level graph analysis system using mapreduce. In: Proceedings of IEEE the 30th International Conference on Data Engineering. 2014, 544–555
Qin L, Yu J X, Chang L, Cheng H, Zhang C, Lin X. Scalable big graph processing in mapreduce. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. 2014, 827–838
Xin R S, Gonzalez J E, Franklin M J, Stoica I. Graphx: a resilient distributed graph system on spark. In: Proceeding of the 1st International Workshop on Graph Data Management Experiences and Systems. 2013
Low Y, Gonzalez J, Kyrola A, Bickson D, Guestrin C, Hellerstein J M. Distributed graphlab: a framework for machine learning in the cloud. Proceedings of the VLDB Endowment, 2012, 5(8): 716–727
Gonzalez J E, Low Y, Gu H, Bickson D, Guestrin C. Powergraph: distributed graph-parallel computation on natural graphs. In: Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation. 2012, 17–30
Fan W, Huai J. Querying big data: bridging theory and practice. Journal of Computer Science and Technology, 2014, 29(5): 849–869
Author information
Authors and Affiliations
Corresponding author
Additional information
Shuai Ma is a professor in the School of Computer Science and Engineering, Beihang University, China. He obtained his two PhDs from University of Edinburgh, UK in 2010, and from Peking University, China in 2004. He was a postdoctoral research fellow in the database group, University of Edinburgh, and a summer intern at Bell labs, Murray Hill, USA in the summer of 2008. His research interests include database theory and systems, social data analysis, and data intensive computing. He is a recipient of the best paper award for VLDB 2010, the Visiting Young Faculty Program of MRSA in 2012, and the best challenge paper award for WISE 2013.
Jia Li is a PhD student in the School of Computer Science and Engineering, Beihang University, China. She obtained her Bachelor degree in computer science from Beihang University in 2012. Her research interests include databases, in particular, social data analysis.
Chunming Hu is an associate professor at the School of Computer Science and Engineering, Beihang University, China. He received his PhD degree from Beihang University in 2006. His current research interests include distributed systems, system virtualization, large scale data management and processing systems.
Xuelian Lin is currently a lecturer in the School of Computer Science and Engineering, Beihang University, China. He received his PhD degree from Beihang University in 2013. His current research interests include middleware and data process systems.
Jinpeng Huai is a professor in the School of Computer Science and Engineering at Beihang University, China. He received his PhD in computer science from Beihang University, in 1993. He is an academician of Chinese Academy of Sciences and the vice honorary chairman of China Computer Federation (CCF). His research interests include big data computing, distributed system, virtual computing, service-oriented computing, trustworthiness and security.
Rights and permissions
About this article
Cite this article
Ma, S., Li, J., Hu, C. et al. Big graph search: challenges and techniques. Front. Comput. Sci. 10, 387–398 (2016). https://doi.org/10.1007/s11704-015-4515-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11704-015-4515-1