Approach Increase Student Achievement? teaching styles (teacher-centered instruction versus student-centered instruction) and examined whether teaching. I believe that student-centered learning arose in reaction to this scenario. Students who went It's a blend of both Teacher-and Student-centered approaches. Others argue that learner-centered is idealistic, unrealistic, and irresponsible. While...

We present the Topology Transformation Equivariant Representation learning, a general paradigm of self-supervised learning for node representations graph data to enable wide applicability Graph Convolutional Neural Networks (GCNNs). formalize proposed model from an information-theoretic perspective, by maximizing mutual information between topology transformations and before after transformatio...

Graph Convolutional Networks (GCNs) have been established as a fundamental approach for representation learning on graphs, based convolution operations non-Euclidean domain, defined by graph-structured data. GCNs and variants achieved state-of-the-art results classification tasks, especially in semi-supervised scenarios. A central challenge consists how to exploit the maximum of useful informat...

Self-adjusting networks (SANs) have the ability to adapt communication demand by dynamically adjusting workload (or demand) embedding, i.e., mapping of requests into network topology. SANs can reduce routing costs for frequently communicating node pairs paying a cost embedding. This is particularly beneficial when has structure, which to. Demand be represented in form graph, defined set nodes (...

Graph representation learning received increasing attentions in recent years. Most of the existing methods ignore complexity graph structures and restrict graphs a single constant-curvature space, which is only suitable to particular kinds structure indeed. Additionally, these follow supervised or semi-supervised paradigm, thereby notably limit their deployment on unlabeled real applications. T...