Mining Graph Evolution Rules View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2009

AUTHORS

Michele Berlingerio , Francesco Bonchi , Björn Bringmann , Aristides Gionis

ABSTRACT

In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern that describe the evolution of large networks over time, at a local level. Given a sequence of snapshots of an evolving graph, we aim at discovering rules describing the local changes occurring in it. Adopting a definition of support based on minimum image we study the problem of extracting patterns whose frequency is larger than a minimum support threshold. Then, similar to the classical association rules framework, we derive graph-evolution rules from frequent patterns that satisfy a given minimum confidence constraint. We discuss merits and limits of alternative definitions of support and confidence, justifying the chosen framework. To evaluate our approach we devise GERM (Graph Evolution Rule Miner), an algorithm to mine all graph-evolution rules whose support and confidence are greater than given thresholds. The algorithm is applied to analyze four large real-world networks (i.e., two social networks, and two co-authorship networks from bibliographic data), using different time granularities. Our extensive experimentation confirms the feasibility and utility of the presented approach. It further shows that different kinds of networks exhibit different evolution rules, suggesting the usage of these local patterns to globally discriminate different kind of networks. More... »

PAGES

115-130

References to SciGraph publications

  • 2006. Mining Temporally Changing Web Usage Graphs in ADVANCES IN WEB MINING AND WEB USAGE ANALYSIS
  • 2007. gPrune: A Constraint Pushing Framework for Graph Pattern Mining in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • 2006-09. Support measures for graph data* in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2005-11. Finding Frequent Patterns in a Large Sparse Graph* in DATA MINING AND KNOWLEDGE DISCOVERY
  • 2008. What Is Frequent in a Single Graph? in ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING
  • Book

    TITLE

    Machine Learning and Knowledge Discovery in Databases

    ISBN

    978-3-642-04179-2
    978-3-642-04180-8

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-04180-8_25

    DOI

    http://dx.doi.org/10.1007/978-3-642-04180-8_25

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1031732441


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