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Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification
MTG -- Source codes and Data used for Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Philip Yu. Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification. TKDE, 2015. Description: This package includes two variants of MTG, i.e., MTG-l1 and MTG-l21. In general, MTG iteratively solves two subproblems: (1) Multi-task learning (MTL) for vector data with logistic loss function (2) Most discriminative subgraph selection For the first subproblem, we employ MALSAR solver [1] to solve the multi-task problem. For the second subproblem, MTG mploys a Top K subgraph miner in Java with upper bounds to prune the unpromising subgraph space. Folders and Files: src/ : core scripts for MTG algorithm; MALSAR/ : a solver for solving the MTL problem; GMiner : Top-K discriminative subgrpah mining written in JAVA, it also provides source code for subgraph base graph classification, i.e., first mine a set of frequent subgraphs, and then employ SVMs for graph classification; data/ : NCI data used in the report mtg_result/ : results obtained from the demo Demo: run demo_MTG.m for result Other Reference 1. J. Zhou, J. Chen and J. Ye. MALSAR: Multi-tAsk Learning via StructurAl Regularization. Arizona State University, 2012. http://www.public.asu.edu/~jye02/Software/MALSAR. Tips: If come across Out of Memory error, increase the Java Heap Space in Matlab: Preferences -> General -> Java Heap Space restart matlab
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