Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning

Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semisupervised learning, a recent important development is graph convolutional networks (GCNs), which nicely integrate local vertex features and graph topology in the convolutional layers. Although the GCN model compares favorably with other state-of-the-art methods, its mechanisms are not clear and it still requires a considerable amount of labeled data for validation and model selection. In this paper, we develop deeper insights into the GCN model and address its fundamental limits. First, we show that the graph convolution of the GCN model is actually a special form of Laplacian smoothing, which is the key reason why GCNs work, but it also brings potential concerns of over-smoothing with many convolutional layers. Second, to overcome the limits of the GCN model with shallow architectures, we propose both co-training and self-training approaches to train GCNs. Our approaches significantly improve GCNs in learning with very few labels, and exempt them from requiring additional labels for validation. Extensive experiments on benchmarks have verified our theory and proposals.Comment: AAAI-2018 Oral Presentatio

A celebration of tradition or of self? An ethnographic study of teachers\u27 comments on student writing in America and in China

The study builds a dialogue between teachers of writing in China and America on what good writing is, for the purpose of revealing the fact that good writing resides not just with student texts, but with the teachers who read and judge student papers. Writing comments on student papers is a time-honored and widely accepted practice in writing classrooms in most countries. Teachers offer text-specific advice to each student and communicate to the student writer, among other things, the criteria of good writing. A close look at the teacher\u27s comments, therefore, reveals the criteria with which teachers measure student papers. The study consists of a case study of four writing teachers, two from China and two from the United States, and a survey of sixty writing teachers in both countries. Through extensive interviews, the reader is introduced to the lives of the four unique individuals and their reading of and comments on six pieces of personal narrative selected and recommended by themselves as samples of good writing . Four of the pieces then were sent to sixty writing teachers (forty-five responded) in both countries, who were asked to rank order and comment on them. The result shows surprising similarities among teachers regardless of their nationalities, but also substantial national differences between the teachers of the two nations. The study then examines some commonly shared criteria of good writing among the American teachers and the underlying ideology of these maxims to place the notion of good writing in a historical and cultural context. Teachers are not innocent readers. When they read student papers, they bring to their judgments not only their personal history, but a whole set of culturally defined and prescribed criteria. In a word, the notion of good writing is a cultivated sensitivity, an acquired taste. Too often writing teachers are portrayed as and believed to be common readers , whose reading of student texts is no more than dramatizing the presence of a reader , while in actuality writing teachers have the power to pass final judgements on student papers. To recognize the power is to accept the responsibility that power entails. It is important to recognize the culture bias in our judgement of student papers especially when our writing classrooms are accepting in large numbers students whose culture backgrounds have instilled in them different notions of good writing