Semantic Graph Compression with Hypergraphs

Can we model complex networks as hyper graphs and compress them for faster storage, transmission, and mining of data? In this paper, we propose a modeling and compression technique that consists of two phases: (i) mapping networks to hyper graphs by exploiting inherent or structural semantic features, and (ii) partitioning the resulting hyper graph such that similar nodes are grouped into a number of possibly disconnected parts. The partitioned hyper graph is then processed in order to yield more structural redundancy to increase compression. We provide empirical results that compare the proposed method to random and natural orderings of select real networks using an information-theoretic measure. When modeling networks using hyper graphs as proposed here, the potential for compactness and compression increases, as observed in our experimental evaluation. This benefits a variety of domains in a variety of ways, such as social networks, biological systems, and the need to represent these as compactly as possible for faster execution of queries. We also address questions for eventual investigation.

[1]  Cécile Bothorel,et al.  An Algorithm for Detecting Communities in Folksonomy Hypergraphs , 2011, IICS.

[2]  Anna C. Gilbert,et al.  Compressing Network Graphs , 2004 .

[3]  Gonzalo Navarro,et al.  Fast and Compact Web Graph Representations , 2010, TWEB.

[4]  Claude Berge,et al.  Graphs and Hypergraphs , 2021, Clustering.

[5]  Andrew B. Kahng,et al.  Recent directions in netlist partitioning: a survey , 1995, Integr..

[6]  Jian Pei,et al.  Neighbor query friendly compression of social networks , 2010, KDD.

[7]  Sebastiano Vigna,et al.  The webgraph framework I: compression techniques , 2004, WWW '04.

[8]  Christos Faloutsos,et al.  Fully automatic cross-associations , 2004, KDD.

[9]  Emad Ramadan,et al.  A hypergraph model for the yeast protein complex network , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[10]  Jure Leskovec,et al.  Defining and Evaluating Network Communities Based on Ground-Truth , 2012, ICDM.

[11]  George Karypis,et al.  Multi-threaded Graph Partitioning , 2013, 2013 IEEE 27th International Symposium on Parallel and Distributed Processing.

[12]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[13]  Giorgio Gallo,et al.  Directed Hypergraphs and Applications , 1993, Discret. Appl. Math..

[14]  Vipin,et al.  Multilevel kway Hypergraph Partitioning * , 1999 .

[15]  I K Fodor,et al.  A Collection of Features for Semantic Graphs , 2007 .

[16]  William J. Knottenbelt,et al.  Towards a parallel disk-based algorithm for multilevel k-way hypergraph partitioning , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[17]  Vipin Kumar,et al.  Parallel Multilevel Algorithms for Multi-constraint Graph Partitioning (Distinguished Paper) , 2000, Euro-Par.

[18]  Steffen Klamt,et al.  Hypergraphs and Cellular Networks , 2009, PLoS Comput. Biol..

[19]  Igor L. Markov,et al.  Hypergraph Partitioning and Clustering , 2007, Handbook of Approximation Algorithms and Metaheuristics.

[20]  Silvio Lattanzi,et al.  On compressing social networks , 2009, KDD.

[21]  R. Solé,et al.  Information Theory of Complex Networks: On Evolution and Architectural Constraints , 2004 .

[22]  Xin Wang,et al.  Query preserving graph compression , 2012, SIGMOD Conference.

[23]  G. Karypis,et al.  Multilevel k-way hypergraph partitioning , 1999, Proceedings 1999 Design Automation Conference (Cat. No. 99CH36361).

[24]  Christos Faloutsos,et al.  Beyond 'Caveman Communities': Hubs and Spokes for Graph Compression and Mining , 2011, 2011 IEEE 11th International Conference on Data Mining.