Accelerating Minimum Temporal Paths Query Based on Dynamic Programming

Temporal path is a fundamental problem in the research of temporal graphs. The solutions [19] in existing studies are not efficient enough since they spend more time to scan temporal edges which reflects connections between two vertices in every time instants. Therefore, in this paper, we first propose efficient algorithms including FDP and SDP, using dynamic programming to calculate the shortest path and fastest path respectively. Then we define a restricted minimum temporal path for some special requirements, including the restricted earliest-arrival path and restricted latest-departure path, and present REDP and RLDP algorithms to solve them. Finally, extensive experiments have demonstrated that our proposed algorithms are effective and efficient over massive real-world temporal graphs.

[1]  Jianxin Li,et al.  The Flexible Socio Spatial Group Queries , 2018, Proc. VLDB Endow..

[2]  Afonso Ferreira,et al.  Computing Shortest, Fastest, and Foremost Journeys in Dynamic Networks , 2003, Int. J. Found. Comput. Sci..

[3]  Panos Kalnis,et al.  Incremental Frequent Subgraph Mining on Large Evolving Graphs , 2017, IEEE Transactions on Knowledge and Data Engineering.

[4]  Cecilia Mascolo,et al.  Characterising temporal distance and reachability in mobile and online social networks , 2010, CCRV.

[5]  Christos D. Zaroliagis,et al.  Timetable Information: Models and Algorithms , 2004, ATMOS.

[6]  Yi Yang,et al.  Diversified Temporal Subgraph Pattern Mining , 2016, KDD.

[7]  Yangjun Chen,et al.  An Efficient Algorithm for Answering Graph Reachability Queries , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[8]  Keshav Pingali,et al.  A compiler for throughput optimization of graph algorithms on GPUs , 2016, OOPSLA.

[9]  Jari Saramäki,et al.  Temporal Networks , 2011, Encyclopedia of Social Network Analysis and Mining.

[10]  Ambuj K. Singh,et al.  Mining Heavy Subgraphs in Time-Evolving Networks , 2011, 2011 IEEE 11th International Conference on Data Mining.

[11]  Jianxin Li,et al.  Maximum Co-located Community Search in Large Scale Social Networks , 2018, Proc. VLDB Endow..

[12]  Jianxin Li,et al.  Influence Propagation Model for Clique-Based Community Detection in Social Networks , 2018, IEEE Transactions on Computational Social Systems.

[13]  Cecilia Mascolo,et al.  Temporal distance metrics for social network analysis , 2009, WOSN '09.

[14]  David A. Padua,et al.  DSMR: a shared and distributed memory algorithm for single-source shortest path problem , 2016, PPoPP.

[15]  Weidong Xiao,et al.  Temporal Social Network: Storage, Indexing and Query Processing , 2016, EDBT/ICDT Workshops.

[16]  Kostas Tsichlas,et al.  An Overview of Methods for Handling Evolving Graph Sequences , 2015, ALGOCLOUD.

[17]  Yi Lu,et al.  Path Problems in Temporal Graphs , 2014, Proc. VLDB Endow..

[18]  Evaggelia Pitoura,et al.  TimeReach: Historical Reachability Queries on Evolving Graphs , 2015, EDBT.

[19]  Charu C. Aggarwal,et al.  Evolutionary Network Analysis , 2014, ACM Comput. Surv..

[20]  Jon M. Kleinberg,et al.  The structure of information pathways in a social communication network , 2008, KDD.

[21]  Feng Xia,et al.  CoPFun: an urban co-occurrence pattern mining scheme based on regional function discovery , 2018, World Wide Web.

[22]  Bo Zong,et al.  Behavior Query Discovery in System-Generated Temporal Graphs , 2015, Proc. VLDB Endow..

[23]  Jinfeng Li,et al.  Reachability and time-based path queries in temporal graphs , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).