A Space Efficient Scheme for Persistent Graph Representation

Graph mining is currently the focus of intense research. Major driving factors include social media, opinion mining, and the schemaless noSQL databases. Time evolving or dynamic graphs are the primary data structures in these fields. Often dynamic graphs must support persistency, meaning that from any given graph state past states can be accessed. Within the graph database context, persistency enables rollback capability, whereas in social media several phenomena such as friend deletion can be modeled. A novel, efficient, and persistent data structure based on tries is proposed. Its potential is displayed by added persistency to the deterministic Kronecker graph model.

[1]  Vasileios Megalooikonomou,et al.  Expansion Properties of Large Social Graphs , 2011, DASFAA Workshops.

[2]  Christos Faloutsos,et al.  Realistic, Mathematically Tractable Graph Generation and Evolution, Using Kronecker Multiplication , 2005, PKDD.

[3]  Edward Fredkin,et al.  Trie memory , 1960, Commun. ACM.

[4]  Aart J. C. Bik,et al.  Pregel: a system for large-scale graph processing , 2010, SIGMOD Conference.

[5]  Prosenjit Bose,et al.  Dynamic optimality for skip lists and B-trees , 2008, SODA '08.

[6]  Hanan Samet,et al.  Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling) , 2005 .

[7]  Hanan Samet,et al.  Foundations of multidimensional and metric data structures , 2006, Morgan Kaufmann series in data management systems.

[8]  Christos Faloutsos,et al.  Scalable modeling of real graphs using Kronecker multiplication , 2007, ICML '07.

[9]  P. Erdos,et al.  On the evolution of random graphs , 1984 .

[10]  Martin Erwig,et al.  Fully Persistent Graphs - Which One To Choose? , 1997, Implementation of Functional Languages.

[11]  A. Rbnyi.  ON THE EVOLUTION OF RANDOM GRAPHS , 2001 .

[12]  Martin Erwig,et al.  Inductive graphs and functional graph algorithms , 2001, J. Funct. Program..

[13]  Jure Leskovec,et al.  Social media analytics: tracking, modeling and predicting the flow of information through networks , 2011, WWW.

[14]  B. Bollobás.  The evolution of random graphs , 1984 .

[15]  Joseph E. Gonzalez,et al.  GraphLab: A New Parallel Framework for Machine Learning , 2010 .

[16]  Christos Faloutsos,et al.  Kronecker Graphs: An Approach to Modeling Networks , 2008, J. Mach. Learn. Res..

[17]  Christos Faloutsos,et al.  Graphs over time: densification laws, shrinking diameters and possible explanations , 2005, KDD '05.

[18]  Robert E. Tarjan,et al.  Making Data Structures Persistent , 1989, J. Comput. Syst. Sci..