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Social bookmark data mining using extended graph kernel
| Content Provider | ACM Digital Library |
|---|---|
| Author | Niimi, Ayahiko Konishi, Osamu |
| Abstract | In this study, we present a social bookmark data community and propose a method to extract the rules of community changes. The transaction data collected from a data stream is considered a graph representing change in structure and sequence data for each relevant time period, while analyzing changes in the sequence graph of the community. The algorithm proposed in this paper is the hierarchical clustering method combined with convolution graph kernels weighted in time. We use this algorithm on the entire community to analyze the relationship among graph sequences and show that it occasionally appears (disappearing in the middle of the sequences) and changes the extracted community rules. Experimental results obtained using synthetic data sets and real social bookmark data show that changes in the community captured the occasional occurrence of the proposed algorithm. The proposed method can detect and analyze social bookmark data including large-scale time-series graph. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450308410 |
| DOI | 10.1145/2237827.2237830 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2011-08-21 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Community transition rules Social bookmark Graph kernels Clustering Graph sequence |
| Content Type | Text |
| Resource Type | Article |
