To fill this gap, we present Automated Graph Learning (AutoGL), the first library for automated machine learning on graphs.
In this paper, we propose a novel Heterogeneous graph neural network framework for diversified recommendation (GraphDR) in matching to improve both recommendation accuracy and diversity.
Specifically, we augment the existing GNNs with stochastic node representations learned to preserve node proximities.
In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity.
Compared with the existing practice of feature concatenation, we find that uncovering the correlation among the three factors is a superior way of leveraging the pivotal contextual cues provided by edges and poses.
Spectral clustering and Singular Value Decomposition (SVD) are both widely used technique for analyzing graph data.
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.
In this paper, we propose a novel tracklet processing method to cleave and re-connect tracklets on crowd or long-term occlusion by Siamese Bi-Gated Recurrent Unit (GRU).
Ranked #14 on Multi-Object Tracking on MOT16
By setting a maximum tolerated error as a threshold, we can trigger SVD restart automatically when the margin exceeds this threshold. We prove that the time complexity of our method is linear with respect to the number of local dynamic changes, and our method is general across different types of dynamic networks.
Social and Information Networks