Abstract
Graph data structures constitute a prominent way to model real-world networks. Most of the graphs originating from these networks are dynamic and constantly evolving. The state (snapshot) of a graph at various time instances forms an evolving graph sequence. By incorporating temporal information in the traditional graph queries, valuable characteristics regarding the nature of a graph can be extracted such as the evolution of the shortest path distance between two vertices through time. Most modern graph processing systems are not suitable for this task since they operate on single very large graphs. In this work we review centralized and distributed methods and solutions proposed towards handling evolving graph sequences.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Certain snapshots require applying only a subset of the operations in a complete delta.
- 2.
Throughout the remainder of this work we will use the terms “evolving graph sequences” and “temporal graphs” interchangeably.
References
Akiba, T., Iwata, Y., Yoshida, Y.: Dynamic and historical shortest-path distance queries on large evolving networks by pruned landmark labeling. In: 23rd International World Wide Web Conference, WWW 2014, Seoul, Republic of Korea, 7–11 April 2014, pp. 237–248 (2014)
Brisaboa, N.R., Caro, D., Fariña, A., Rodríguez, M.A.: A compressed suffix-array strategy for temporal-graph indexing. In: Moura, E., Crochemore, M. (eds.) SPIRE 2014. LNCS, vol. 8799, pp. 77–88. Springer, Heidelberg (2014)
Brisaboa, N.R., Fariña, A., Navarro, G., Paramá, J.R.: Lightweight natural language text compression. Inf. Retr. 10(1), 1–33 (2007)
Caro, D., Rodríguez, M.A., Brisaboa, N.R.: Data structures for temporal graphs based on compact sequence representations. Inf. Syst. 51, 1–26 (2015)
Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische mathematik 1(1), 269–271 (1959)
Geisberger, R., Sanders, P., Schultes, D., Delling, D.: Contraction hierarchies: faster and simpler hierarchical routing in road networks. In: McGeoch, C.C. (ed.) WEA 2008. LNCS, vol. 5038, pp. 319–333. Springer, Heidelberg (2008)
Apache Giraph: http://giraph.apache.org/
Huo, W., Tsotras, V.J.: Efficient temporal shortest path queries on evolving social graphs. In: Conference on Scientific and Statistical Database Management, SSDBM 2014, Aalborg, Denmark, June 30–July 02, 2014, pp. 38:1–38:4 (2014)
Khurana, U., Deshpande, A.: Efficient snapshot retrieval over historical graph data. In: 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, 8–12 April 2013, pp. 997–1008 (2013)
Koloniari, G., Souravlias, D., Pitoura, E.: On graph deltas for historical queries. In: WOSS (2012)
Labouseur, A.G., Birnbaum, J., Olsen, P.W., Spillane, S.R., Vijayan, J., Hwang, J., Han, W.: The G* graph database: efficiently managing large distributed dynamic graphs. Distrib. Parallel Databases 33(4), 479–514 (2015)
Labouseur, A.G., Olsen, P.W., Hwang, J.: Scalable and robust management of dynamic graph data. In: Proceedings of the First International Workshop on Big Dynamic Distributed Data, Riva del Garda, Italy, 30 August 2013, pp. 43–48 (2013)
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection, June 2004. http://snap.stanford.edu/data
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 ACM SIGMOD International Conference on Management of Data, SIGMOD 2010, Indianapolis, Indiana, USA, 6–10 June 2010, pp. 135–146 (2010)
Neo4j: http://neo4j.org/
Nicosia, V., Tang, J., Mascolo, C., Musolesi, M., Russo, G., Latora, V.: Graph metrics for temporal networks. In: Holme, P., Saramäki, J. (eds.) Temporal Networks, pp. 15–40. Springer, Heidelberg (2013)
Ren, C., Lo, E., Kao, B., Zhu, X., Cheng, R.: On querying historical evolving graph sequences. PVLDB 4(11), 726–737 (2011)
Semertzidis, K., Pitoura, E., Lillis, K.: Timereach: historical reachability queries on evolving graphs. In: Proceedings of the 18th International Conference on Extending Database Technology, EDBT 2015, Brussels, Belgium, 23–27 March 2015, pp. 121–132 (2015)
Shao, B., Wang, H., Li, Y.: Trinity: a distributed graph engine on a memory cloud. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2013, New York, NY, USA, 22–27 June 2013, pp. 505–516 (2013)
Spillane, S.R., Birnbaum, J., Bokser, D., Kemp, D., Labouseur, A.G., Olsen, P.W., Vijayan, J., Hwang, J., Yoon, J.: A demonstration of the g\({_\ast }\) graph database system. In: 29th IEEE International Conference on Data Engineering, ICDE 2013, Brisbane, Australia, 8–12 April 2013, pp. 1356–1359 (2013)
Yang, Y., Yu, J.X., Gao, H., Pei, J., Li, J.: Mining most frequently changing component in evolving graphs. World Wide Web 17(3), 351–376 (2014)
Zhang, J., Long, X., Suel, T.: Performance of compressed inverted list caching in search engines. In: Proceedings of the 17th International Conference on World Wide Web, WWW 2008, Beijing, China, 21–25 April 2008, pp. 387–396 (2008)
Zukowski, M., Héman, S., Nes, N., Boncz, P.A.: Super-scalar RAM-CPU cache compression. In: Proceedings of the 22nd International Conference on Data Engineering, ICDE 2006, Atlanta, GA, USA, 3–8 April 2006, p. 59 (2006)
Acknowledgments
This research has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning of the National Strategic Reference Framework (NSRF) - Research Funding Program: Thales. Investing in knowledge society through the European Social Fund.”
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kosmatopoulos, A., Giannakopoulou, K., Papadopoulos, A.N., Tsichlas, K. (2016). An Overview of Methods for Handling Evolving Graph Sequences. In: Karydis, I., Sioutas, S., Triantafillou, P., Tsoumakos, D. (eds) Algorithmic Aspects of Cloud Computing. ALGOCLOUD 2015. Lecture Notes in Computer Science(), vol 9511. Springer, Cham. https://doi.org/10.1007/978-3-319-29919-8_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-29919-8_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-29918-1
Online ISBN: 978-3-319-29919-8
eBook Packages: Computer ScienceComputer Science (R0)