OverviewThis tool aims to match any given query metagraph (i.e., to compute the instances of the metagraph) over a large input graph. For the definition and examples of metagraphs, refer to the citation below. Currently, the tool only works on undirected graphs, where nodes are typed, and edges are untyped (or rather, edge type is a function of the two node types).CitationY. Fang, W. Lin, V. W. Zheng, M. Wu, K. C.-C. Chang and X. Li. Semantic Proximity Search on Graphs with Metagraph-based Learning. In ICDE 2016, pp. 277--288. [PDF] [BibTex] Code Download
UsageSubMatch.exe mode=2 data=<String> query=<String> maxfreq=<Integer> subgraph=<String> stats=<String>data=<String> The input graph filename. The file is in the Labeled Graph Format. The graph is treated as undirected, and edge types are not considered at the moment. query=<String> The input filename for a list of query metagraphs, in the Metagraph Query Format. These query metagraphs can be mined from the input graph using a modified version of GRAMI. maxfreq=<Integer> The maximum number of instances to match, for each query metagraph. The program immediately moves on to the next query after the specified maximum number of instances are found. subgraph=<String> The filename to output the metagraph database, which contains a list of processed metagraphs. The file is in the Metagraph Database Format. stats=<String> The directory name to output matched instances of each metagraph.
Sample DataSample data of two input graphs and their correponding metagraph queries are included, which are also used in our citation above. They are derived from SNAP's Facebook data and Forward's LinkedIn data.DisclaimerWe provide any code and/or data on an as-is basis. Use at your own risk. |
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