@article {113,
	title = {Clustering frequent graph patterns},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {335 - 346},
	abstract = {In recent years, graph mining has attracted much attention in the data mining community. Several efficient frequent subgraph mining algorithms have been recently proposed. However, the number of frequent graph patterns generated by these graph mining algorithms may be too large to be effectively explored by users, especially when the support threshold is low. In this paper, we propose to summarize frequent graph patterns by a much smaller number of representative graph patterns. Several novel concepts such as δ-cover graph, jump value and δ-jump pattern are proposed for efficiently summarizing frequent graph patterns. Based on the property of all δ-jump patterns being representative graph patterns, we propose two efficient algorithms for summarizing frequent graph patterns, RP-FP and RP-GD. The RP-FP algorithm computes representative graph patterns from a set of closed frequent graph patterns, whereas the RP-GD algorithm directly mines representative graph patterns from graph databases. The RP-FP has tight approximation (summarization) bound but higher computational complexity. The RP-GD has no approximation bound guarantee but is far more efficient. We conducted extensive experimental studies using various real and synthetic datasels. Experimental results show that RP-FP and RP-GD are able to obtain compact summarization in both real and synthetic graph databases. When the number of closed graph patterns is very large, RP-GD is much more efficient than RP-FP, while achieving comparable summarization quality.},
	keywords = {Closed graph pattern, Frequent graph pattern, Graph mining, Representative graph pattern, δ-jump pattern},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-49149118250\&partnerID=40\&md5=cae15cbd3bb5b1a9e3f57a95dbe295b5},
	author = {Liu, Y. and Li, J. and Zhu, J. and Gao, H.}
}
@article {100,
	title = {Social search with missing data: Which ranking algorithm?},
	journal = {Journal of Digital Information Management},
	volume = {5},
	year = {2007},
	note = {Export Date: 10 July 2014},
	month = {2007},
	pages = {249 - 261},
	abstract = {Online social networking tools are extremely popular, but can miss potential discoveries latent in the social {\textquoteright}fabric{\textquoteright}. Matchmaking services which can do naive profile matching with old database technology are too brittle in the absence of key data, and even modern ontological markup, though powerful, can be onerous at data-input time. In this paper, we present a system called BuddyFinder which can automatically identify buddies who can best match a user{\textquoteright}s search requirements specified in a term-based query, even in the absence of stored user-profiles. We deploy and compare five statistical measures, namely, our own CORDER, mutual information (MI), phi-squared, improved MI and Z score, and two TF/IDF based baseline methods to find online users who best match the search requirements based on {\textquoteright}inferred profiles{\textquoteright} of these users in the form of scavenged web pages. These measures identify statistically significant relationships between online users and a term-based query. Our user evaluation on two groups of users shows that BuddyFinder can find users highly relevant to search queries, and that CORDER achieved the best average ranking correlations among all seven algorithms and improved the performance of both baseline methods.},
	keywords = {Instant messaging, Ranking algorithms, Relation discovery, Social software},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-70350629888\&partnerID=40\&md5=a263e560496728c004285a55b37a4e27},
	author = {Zhu, J. and Eisenstadt, M. and Gon{\c c}alves, A. and Denham, C. and Uren, V. and Song, D.}
}
