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Found 8 papers, 1 papers with code
AMCAD: Adaptive Mixed-Curvature Representation based Advertisement Retrieval System
Moreover, to deploy AMCAD in Taobao, one of the largest ecommerce platforms with hundreds of million users, we design an efficient two-layer online retrieval framework for the task of graph based advertisement retrieval.
TrafficStream: A Streaming Traffic Flow Forecasting Framework Based on Graph Neural Networks and Continual Learning
With the rapid growth of traffic sensors deployed, a massive amount of traffic flow data are collected, revealing the long-term evolution of traffic flows and the gradual expansion of traffic networks.
TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
To simultaneously extract spatial and relational information from tables, we propose a novel neural network architecture, TabularNet.
Learning Node Representations from Noisy Graph Structures
Since noises are often unknown on real graphs, we design two generators, namely a graph generator and a noise generator, to identify normal structures and noises in an unsupervised setting.
EPNE: Evolutionary Pattern Preserving Network Embedding
In this paper, we propose EPNE, a temporal network embedding model preserving evolutionary patterns of the local structure of nodes.
Streaming Graph Neural Networks via Continual Learning
In this paper, we propose a streaming GNN model based on continual learning so that the model is trained incrementally and up-to-date node representations can be obtained at each time step.
Tag2Vec: Learning Tag Representations in Tag Networks
Network embedding is a method to learn low-dimensional representation vectors for nodes in complex networks.
Using Social Network Information in Bayesian Truth Discovery
We incorporate knowledge of the agents' social network in our truth discovery framework and develop Laplace variational inference methods to estimate agents' reliabilities, communities, and the event states.
