### Abstract

In order to enable scalable querying of graph databases, intelligent selection of subgraphs to index is essential. An improved index can reduce response times for graph queries significantly. For a given subgraph query, graph candidates that may contain the subgraph are retrieved using the graph index and subgraph isomorphism tests are performed to prune out unsatisfied graphs. However, since the space of all possible subgraphs of the whole set of graphs is prohibitively large, feature selection is required to identify a good subset of subgraph features for indexing. Thus, one of the key issues is: given the set of all possible subgraphs of the graph set, which subset of features is the optimal such that the algorithm retrieves the smallest set of candidate graphs and reduces the number of subgraph isomorphism tests? We introduce a graph search method for subgraph queries based on subgraph frequencies. Then, we propose several novel feature selection criteria, Max-Precision, Max-Irredundant-Information, and Max-Information-Min-Redundancy, based on mutual information. Finally we show theoretically and empirically that our proposed methods retrieve a smaller candidate set than previous methods. For example, using the same number of features, our method improve the precision for the query candidate set by 4%-13% in comparison to previous methods. As a result the response time of subgraph queries also is improved correspondingly.

Original language | English |
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Title of host publication | International Conference on Information and Knowledge Management, Proceedings |

Pages | 563-572 |

Number of pages | 10 |

DOIs | |

Publication status | Published - 2009 |

Externally published | Yes |

Event | ACM 18th International Conference on Information and Knowledge Management, CIKM 2009 - Hong Kong Duration: 2 Nov 2009 → 6 Nov 2009 |

### Other

Other | ACM 18th International Conference on Information and Knowledge Management, CIKM 2009 |
---|---|

City | Hong Kong |

Period | 2/11/09 → 6/11/09 |

### Fingerprint

### Keywords

- Feature selection
- Graph mining
- Graph search
- Index pruning

### ASJC Scopus subject areas

- Business, Management and Accounting(all)
- Decision Sciences(all)

### Cite this

*International Conference on Information and Knowledge Management, Proceedings*(pp. 563-572) https://doi.org/10.1145/1645953.1646026

**Independent informative subgraph mining for graph information retrieval.** / Sun, Bingjun; Mitra, Prasenjit; Giles, C. Lee.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*International Conference on Information and Knowledge Management, Proceedings.*pp. 563-572, ACM 18th International Conference on Information and Knowledge Management, CIKM 2009, Hong Kong, 2/11/09. https://doi.org/10.1145/1645953.1646026

}

TY - GEN

T1 - Independent informative subgraph mining for graph information retrieval

AU - Sun, Bingjun

AU - Mitra, Prasenjit

AU - Giles, C. Lee

PY - 2009

Y1 - 2009

N2 - In order to enable scalable querying of graph databases, intelligent selection of subgraphs to index is essential. An improved index can reduce response times for graph queries significantly. For a given subgraph query, graph candidates that may contain the subgraph are retrieved using the graph index and subgraph isomorphism tests are performed to prune out unsatisfied graphs. However, since the space of all possible subgraphs of the whole set of graphs is prohibitively large, feature selection is required to identify a good subset of subgraph features for indexing. Thus, one of the key issues is: given the set of all possible subgraphs of the graph set, which subset of features is the optimal such that the algorithm retrieves the smallest set of candidate graphs and reduces the number of subgraph isomorphism tests? We introduce a graph search method for subgraph queries based on subgraph frequencies. Then, we propose several novel feature selection criteria, Max-Precision, Max-Irredundant-Information, and Max-Information-Min-Redundancy, based on mutual information. Finally we show theoretically and empirically that our proposed methods retrieve a smaller candidate set than previous methods. For example, using the same number of features, our method improve the precision for the query candidate set by 4%-13% in comparison to previous methods. As a result the response time of subgraph queries also is improved correspondingly.

AB - In order to enable scalable querying of graph databases, intelligent selection of subgraphs to index is essential. An improved index can reduce response times for graph queries significantly. For a given subgraph query, graph candidates that may contain the subgraph are retrieved using the graph index and subgraph isomorphism tests are performed to prune out unsatisfied graphs. However, since the space of all possible subgraphs of the whole set of graphs is prohibitively large, feature selection is required to identify a good subset of subgraph features for indexing. Thus, one of the key issues is: given the set of all possible subgraphs of the graph set, which subset of features is the optimal such that the algorithm retrieves the smallest set of candidate graphs and reduces the number of subgraph isomorphism tests? We introduce a graph search method for subgraph queries based on subgraph frequencies. Then, we propose several novel feature selection criteria, Max-Precision, Max-Irredundant-Information, and Max-Information-Min-Redundancy, based on mutual information. Finally we show theoretically and empirically that our proposed methods retrieve a smaller candidate set than previous methods. For example, using the same number of features, our method improve the precision for the query candidate set by 4%-13% in comparison to previous methods. As a result the response time of subgraph queries also is improved correspondingly.

KW - Feature selection

KW - Graph mining

KW - Graph search

KW - Index pruning

UR - http://www.scopus.com/inward/record.url?scp=74549174134&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=74549174134&partnerID=8YFLogxK

U2 - 10.1145/1645953.1646026

DO - 10.1145/1645953.1646026

M3 - Conference contribution

AN - SCOPUS:74549174134

SN - 9781605585123

SP - 563

EP - 572

BT - International Conference on Information and Knowledge Management, Proceedings

ER -