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Data Mining: Graph mining and social network analysis

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Data Mining: Graph mining and social network analysis

Data Mining: Graph mining and social network analysis

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  • 1. Graph Mining, Social Network Analysis, and Multi relational Data Mining
  • 2. Why and What is Graph Mining?
    Graphs become increasingly important in modeling complicated structures, such as circuits, images, biological networks, social networks, the Web, and XML documents.
    Many graph search algorithms have been developed in chemical informatics, computer vision, video indexing, and text retrieval.
    With the increasing demand on the analysis of large amounts of structured data, graph mining has become an active and important theme in data mining.
  • 3. Methods for Mining Frequent Sub graphs
    Apriori-based Approach
    Apriori-based algorithms for frequent substructure mining include AGM, FSG, and a path-join method.
     AGM shares similar characteristics with Apriori-based item-set mining.
    FSG and the path-join method explore edges and connections in an Apriori-based fashion.
  • 4. Other Approach for Mining Frequent Sub graphs
    Pattern Growth Graph Approach : Simplistic pattern growth-based frequent substructure mining.
    gSpan: A pattern-growth algorithm for frequent substructure mining.
    (for detailed algorithm refer wiki)
  • 5. Characteristics of Social Networks
    Densification power law
    Shrinking diameter
    Heavy-tailed out-degree and in-degree distributions
  • 6. Link Mining
    Traditional methods of machine learning and data mining, taking, as input, a random sample of homogenous objects from a single relation, may not be appropriate in social networks.
    The data comprising social networks tend to be heterogeneous, multi relational, and semi-structured.
    As a result, a new field of research has emerged called link mining.
  • 7. Tasks involved in link mining
    Link-based object classification.
    Object type prediction.
    Link type prediction.
    Predicting link existence
    Link cardinality estimation.
    Object reconciliation.
    Group detection
    Sub graph detection
    Metadata mining
  • 8. Challenges faced by Link Mining
    Logical versus statistical dependencies
    Feature construction
    Instances versus classes.
    Collective classification and collective consolidation.
    Effective use of labeled and unlabeled data
    Link prediction
    Closed versus open world assumption
    Community mining from multi relational networks.
  • 9. What is Multi relational Data Mining?
    Multi relational data mining (MRDM) methods search for patterns that involve multiple tables (relations) from a relational database
  • 10. Multi relational Clustering with User Guidance
    Multi relational clustering is the process of partitioning data objects into a set of clusters based on their similarity, utilizing information in multiple relations.
  • 11. Visit more self help tutorials
    Pick a tutorial of your choice and browse through it at your own pace.
    The tutorials section is free, self-guiding and will not involve any additional support.
    Visit us at www.dataminingtools.net

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