Many underlying relationships among data in several areas of science and engineering, e.g., computer vision, molecular chemistry, molecular biology, pattern recognition, and data mining, can be represented in terms of graphs. extends prior recurrent models to handle general types of graphs, e.g., acyclic, cyclic, directed, and undirected graphs
Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings … %0 Conference Paper %T Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance %A Dasaem Jeong %A Taegyun Kwon %A Yoojin Kim %A Juhan Nam %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97 … In this paper, we propose a new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains. It involves capturing global dependencies among sentences regardless of their input order. In this paper, we propose a novel and flexible graph-based neural sentence ordering model, which adopts graph recurrent network \citep{Zhang:acl18} to accurately learn semantic representations of the sentences.
Abstract Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments.
Unlike traditional neural nets, the proposed network is guaranteed to converge to valid solutions with no parameter tuning needed. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains an unsolved challenge. #2 best model for Graph Classification on IPC-grounded (Accuracy metric) CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): This paper proposes a new algorithm using a maximum neural network model to k-color vertices of a simple undirected graph. This GNN model, which can directly process most of the practically useful types of …
Graph Neural Network (GNN* 2) proposed by Scarselli et al.
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