Algorithms for mining the evolution of conserved relational states in dynamic networks

Dynamic networks have recently being recognized as a powerful abstraction to model and represent the temporal changes and dynamic aspects of the data underlying many complex systems. Significant insights regarding the stable relational patterns among the entities can be gained by analyzing temporal evolution of the complex entity relations. This can help identify the transitions from one conserved state to the next and may provide evidence to the existence of external factors that are responsible for changing the stable relational patterns in these networks. This paper presents a new data mining method that analyzes the time-persistent relations or states between the entities of the dynamic networks and captures all maximal non-redundant evolution paths of the stable relational states. Experimental results based on multiple datasets from real-world applications show that the method is efficient and scalable.

[1]  Tanya Y. Berger-Wolf,et al.  Mining Periodic Behavior in Dynamic Social Networks , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[2]  Hans-Peter Kriegel,et al.  Pattern Mining in Frequent Dynamic Subgraphs , 2006, Sixth International Conference on Data Mining (ICDM'06).

[3]  George Karypis,et al.  Algorithms for Mining the Evolution of Conserved Relational States in Dynamic Networks , 2011, ICDM.

[4]  George Karypis,et al.  Frequent Substructure-Based Approaches for Classifying Chemical Compounds , 2005, IEEE Trans. Knowl. Data Eng..

[5]  Jian Pei,et al.  On mining cross-graph quasi-cliques , 2005, KDD '05.

[6]  Gerard Salton,et al.  Term-Weighting Approaches in Automatic Text Retrieval , 1988, Inf. Process. Manag..

[7]  Takashi Washio,et al.  An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data , 2000, PKDD.

[8]  D. West.  Introduction to Graph Theory , 1995 .

[9]  Huan Liu,et al.  Community evolution in dynamic multi-mode networks , 2008, KDD.

[10]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[11]  Danah Boyd,et al.  Social Network Sites: Definition, History, and Scholarship , 2007, J. Comput. Mediat. Commun..

[12]  Zhengding Lu,et al.  Community mining on dynamic weighted directed graphs , 2009, CIKM-CNIKM.

[13]  Jiawei Han,et al.  Mining coherent dense subgraphs across massive biological networks for functional discovery , 2005, ISMB.

[14]  Jiawei Han,et al.  Mining closed relational graphs with connectivity constraints , 2005, 21st International Conference on Data Engineering (ICDE'05).

[15]  K. Gleditsch,et al.  Expanded Trade and GDP Data , 2002 .

[16]  Céline Robardet,et al.  Constraint-Based Pattern Mining in Dynamic Graphs , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[17]  Jeffrey Xu Yu,et al.  Spotting Significant Changing Subgraphs in Evolving Graphs , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[18]  Takashi Washio,et al.  Mining Frequent Graph Sequence Patterns Induced by Vertices , 2010, SDM.

[19]  Christian Böhm,et al.  Frequent subgraph discovery in dynamic networks , 2010, MLG '10.

[20]  Takashi Washio,et al.  A Fast Method to Mine Frequent Subsequences from Graph Sequence Data , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[21]  Anthony K. H. Tung,et al.  Spatial clustering methods in data mining : A survey , 2001 .

[22]  George Karypis,et al.  Finding Frequent Patterns in a Large Sparse Graph* , 2005, Data Mining and Knowledge Discovery.

[23]  Xiaohua Hu,et al.  Mining and analysing scale-free proteinprotein interaction network , 2005, Int. J. Bioinform. Res. Appl..

[24]  Luc De Raedt,et al.  Molecular feature mining in HIV data , 2001, KDD '01.

[25]  Aristides Gionis,et al.  Mining Graph Evolution Rules , 2009, ECML/PKDD.

[26]  Philip S. Yu,et al.  GraphScope: parameter-free mining of large time-evolving graphs , 2007, KDD '07.

[27]  J. Srivastava,et al.  Mining Temporally Evolving Graphs , 2004 .

[28]  George Karypis,et al.  An efficient algorithm for discovering frequent subgraphs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[29]  Ruoming Jin,et al.  Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[30]  Yehuda Koren,et al.  Measuring and extracting proximity graphs in networks , 2007, TKDD.

[31]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[32]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[33]  Hiroki Arimura,et al.  Efficient Substructure Discovery from Large Semi-Structured Data , 2001, IEICE Trans. Inf. Syst..

[34]  George Karypis,et al.  Finding Frequent Patterns in a Large Sparse Graph* , 2004, IEEE International Parallel and Distributed Processing Symposium.

[35]  Lawrence B. Holder,et al.  Learning patterns in the dynamics of biological networks , 2009, KDD.

[36]  Deepayan Chakrabarti,et al.  Evolutionary clustering , 2006, KDD '06.

[37]  Wei Wang,et al.  Efficient mining of frequent subgraphs in the presence of isomorphism , 2003, Third IEEE International Conference on Data Mining.

[38]  George Karypis,et al.  Comparison of descriptor spaces for chemical compound retrieval and classification , 2006, Sixth International Conference on Data Mining (ICDM'06).

[39]  Mohammed J. Zaki.  Efficiently mining frequent trees in a forest , 2002, KDD.

[40]  Bülent Yener,et al.  Graph Theoretic and Spectral Analysis of Enron Email Data , 2005, Comput. Math. Organ. Theory.

[41]  Johan Bollen,et al.  Co-authorship networks in the digital library research community , 2005, Inf. Process. Manag..

[42]  Jean-François Boulicaut,et al.  Discovering Relevant Cross-Graph Cliques in Dynamic Networks , 2009, ISMIS.

[43]  Pourang Irani,et al.  2008 Eighth IEEE International Conference on Data Mining , 2008 .

[44]  Jaideep Srivastava,et al.  Mining Temporally Changing Web Usage Graphs , 2004, WebKDD.

[45]  Wojciech Szpankowski,et al.  Detecting Conserved Interaction Patterns in Biological Networks , 2006, J. Comput. Biol..

[46]  Tanya Y. Berger-Wolf,et al.  A framework for analysis of dynamic social networks , 2006, KDD '06.