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Found 12 papers, 7 papers with code
AutoGL: A Library for Automated Graph Learning
To fill this gap, we present Automated Graph Learning (AutoGL), the first library for automated machine learning on graphs.
Automated Machine Learning on Graphs: A Survey
Machine learning on graphs has been extensively studied in both academic and industry.
Improving Accuracy and Diversity in Matching of Recommendation with Diversified Preference Network
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.
A Simple and General Graph Neural Network with Stochastic Message Passing
Specifically, we augment the existing GNNs with stochastic node representations learned to preserve node proximities.
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs
Graph Neural Networks (GNNs) are emerging machine learning models on graphs.
Asymmetric Transitivity Preserving Graph Embedding
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.
Correlating Edge, Pose with Parsing
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.
Deep Learning on Graphs: A Survey
Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques.
A Note on Spectral Clustering and SVD of Graph Data
Spectral clustering and Singular Value Decomposition (SVD) are both widely used technique for analyzing graph data.
Billion-scale Network Embedding with Iterative Random Projection
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.
Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking
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
TIMERS: Error-Bounded SVD Restart on Dynamic Networks
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