IEEE Trans Cybern. 2017 Apr;47(4):818-829. doi: 10.1109/TCYB.2016.2527239. Epub 2016 Mar 23.
In this paper, we advance graph classification to handle multi-graph learning for complicated objects, where each object is represented as a bag of graphs and the label is only available to each bag but not individual graphs. In addition, when training classifiers, users are only given a handful of positive bags and many unlabeled bags, and the learning objective is to train models to classify previously unseen graph bags with maximum accuracy. To achieve the goal, we propose a positive and unlabeled multi-graph learning (puMGL) framework to first select informative subgraphs to convert graphs into a feature space. To utilize unlabeled bags for learning, puMGL assigns a confidence weight to each bag and dynamically adjusts its weight value to select "reliable negative bags." A number of representative graphs, selected from positive bags and identified reliable negative graph bags, form a "margin graph pool" which serves as the base for deriving subgraph patterns, training graph classifiers, and further updating the bag weight values. A closed-loop iterative process helps discover optimal subgraphs from positive and unlabeled graph bags for learning. Experimental comparisons demonstrate the performance of puMGL for classifying real-world complicated objects.
在本文中，我们将图分类推进到处理多图学习，以处理复杂对象，其中每个对象表示为图的集合，标签仅可用于每个集合，但不能用于单个图。此外，在训练分类器时，用户仅提供少量正例集合和许多未标记的集合，学习目标是训练模型以最大精度对以前未见的图集合进行分类。为了实现这一目标，我们提出了一个正例和未标记的多图学习（puMGL）框架，首先选择信息丰富的子图将图转换为特征空间。为了利用未标记的集合进行学习，puMGL 为每个集合分配置信权重，并动态调整其权重值以选择“可靠的负例集合”。从正例集合中选择的一些有代表性的图和识别出的可靠负例图集合组成了一个“边界图池”，作为提取子图模式、训练图分类器以及进一步更新集合权重值的基础。一个闭环迭代过程有助于从正例和未标记的图集合中发现用于学习的最优子图。实验比较证明了 puMGL 对分类真实复杂对象的性能。
