Self-supervised Hierarchical Graph Neural Network for Graph Representation

2020 ◽  
Author(s):  
Sambaran Bandyopadhyay ◽  
Manasvi Aggarwal ◽  
M. Narasimha Murty
Keyword(s):  
Neural Network ◽  
Graph Representation ◽  
Hierarchical Graph

scholarly journals Research of Personalized Recommendation Technology Based on Knowledge Graphs

2021 ◽  
Vol 11 (15) ◽  
pp. 7104
Author(s):  
Xu Yang ◽  
Ziyi Huan ◽  
Yisong Zhai ◽  
Ting Lin
Keyword(s):  
Neural Network ◽  
Hot Spot ◽  
Experimental Results ◽  
Graph Representation ◽  
Personalized Recommendation ◽  
Knowledge Graph ◽  
Learning Technology ◽  
Data Set ◽  
Model Based ◽  
Knowledge Graphs

Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs’ construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs(KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

Megnn: Meta-path extracted graph neural network for heterogeneous graph representation learning

2021 ◽  
pp. 107611
Author(s):  
Yaomin Chang ◽  
Chuan Chen ◽  
Weibo Hu ◽  
Zibin Zheng ◽  
Xiaocong Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Representation Learning ◽  
Graph Representation ◽  
Meta Path

scholarly journals SOM-based aggregation for graph convolutional neural networks

2020 ◽  
Author(s):  
Luca Pasa ◽  
Nicolò Navarin ◽  
Alessandro Sperduti
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Predictive Performance ◽  
Aggregation Operator ◽  
Graph Representation ◽  
Approximation Properties ◽  
Neural Network Models ◽  
Self Organizing Maps ◽  
Node Level ◽  
Real World Datasets

AbstractGraph property prediction is becoming more and more popular due to the increasing availability of scientific and social data naturally represented in a graph form. Because of that, many researchers are focusing on the development of improved graph neural network models. One of the main components of a graph neural network is the aggregation operator, needed to generate a graph-level representation from a set of node-level embeddings. The aggregation operator is critical since it should, in principle, provide a representation of the graph that is isomorphism invariant, i.e. the graph representation should be a function of graph nodes treated as a set. DeepSets (in: Advances in neural information processing systems, pp 3391–3401, 2017) provides a framework to construct a set-aggregation operator with universal approximation properties. In this paper, we propose a DeepSets aggregation operator, based on Self-Organizing Maps (SOM), to transform a set of node-level representations into a single graph-level one. The adoption of SOMs allows to compute node representations that embed the information about their mutual similarity. Experimental results on several real-world datasets show that our proposed approach achieves improved predictive performance compared to the commonly adopted sum aggregation and many state-of-the-art graph neural network architectures in the literature.

Clique-Based Neural Associative Memories with Local Coding and Precoding

2016 ◽  
Vol 28 (8) ◽  
pp. 1553-1573 ◽  
Author(s):  
Asieh Abolpour Mofrad ◽  
Matthew G. Parker ◽  
Zahra Ferdosi ◽  
Mohammad H. Tadayon
Keyword(s):  
Neural Network ◽  
Associative Memory ◽  
Coding Theory ◽  
Simple Graph ◽  
Graph Representation ◽  
Associative Memories ◽  
Additive Codes ◽  
Partial Error ◽  
Pattern Retrieval ◽  
Local Coding

Techniques from coding theory are able to improve the efficiency of neuroinspired and neural associative memories by forcing some construction and constraints on the network. In this letter, the approach is to embed coding techniques into neural associative memory in order to increase their performance in the presence of partial erasures. The motivation comes from recent work by Gripon, Berrou, and coauthors, which revisited Willshaw networks and presented a neural network with interacting neurons that partitioned into clusters. The model introduced stores patterns as small-size cliques that can be retrieved in spite of partial error. We focus on improving the success of retrieval by applying two techniques: doing a local coding in each cluster and then applying a precoding step. We use a slightly different decoding scheme, which is appropriate for partial erasures and converges faster. Although the ideas of local coding and precoding are not new, the way we apply them is different. Simulations show an increase in the pattern retrieval capacity for both techniques. Moreover, we use self-dual additive codes over field [Formula: see text], which have very interesting properties and a simple-graph representation.

scholarly journals An Attention-Based Graph Neural Network for Heterogeneous Structural Learning

2020 ◽  
Vol 34 (04) ◽  
pp. 4132-4139
Author(s):  
Huiting Hong ◽  
Hantao Guo ◽  
Yucheng Lin ◽  
Xiaoqing Yang ◽  
Zang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Structural Information ◽  
Representation Learning ◽  
Graph Representation ◽  
Heterogeneous Information ◽  
Domain Experts ◽  
Proposed Model ◽  
Meta Path ◽  
Low Dimensional ◽  
Public Datasets

In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous graph embedding models via meta-paths to learn low-dimensional vector space of HIN. In this paper, we propose a novel Heterogeneous Graph Structural Attention Neural Network (HetSANN) to directly encode structural information of HIN without meta-path and achieve more informative representations. With this method, domain experts will not be needed to design meta-path schemes and the heterogeneous information can be processed automatically by our proposed model. Specifically, we implicitly represent heterogeneous information using the following two methods: 1) we model the transformation between heterogeneous vertices through a projection in low-dimensional entity spaces; 2) afterwards, we apply the graph neural network to aggregate multi-relational information of projected neighborhood by means of attention mechanism. We also present three extensions of HetSANN, i.e., voices-sharing product attention for the pairwise relationships in HIN, cycle-consistency loss to retain the transformation between heterogeneous entity spaces, and multi-task learning with full use of information. The experiments conducted on three public datasets demonstrate that our proposed models achieve significant and consistent improvements compared to state-of-the-art solutions.

scholarly journals Using the Hopfield Neural Network to Select a Behaviour Strategy for the Group of Mobile Robots

2021 ◽  
Vol 2096 (1) ◽  
pp. 012086
Author(s):  
O V Darintsev ◽  
A B Migranov
Keyword(s):  
Neural Network ◽  
Mobile Robots ◽  
Original Problem ◽  
Heuristic Algorithms ◽  
Hopfield Neural Network ◽  
Graph Representation ◽  
Distribution Problem ◽  
Problem Statement ◽  
Working Space ◽  
Task Distribution

Abstract The use of the Hopfield neural network for the task distribution problem solving in teams of mobile robots performing monosyllabic operations in a single workspace is considered. The study is a continuation of earlier works in which the same problem was solved by the authors using other heuristic algorithms – swarm and genetic. This article presents the problem statement and the model of the working space, distinguishes the goals of robotic operation. The quality indicator is the total distance traveled by each of the robots in the group. To enable the original problem to be solved using the Hopfield neural network, a graph representation of the Hopfield is made by switching from the VRP to the TSP problem. The results of computational experiments confirming the effectiveness of the chosen approach for choosing a strategy of behavior of a group of mobile robots are shown.

scholarly journals ACE: Ant Colony Based Multi-Level Network Embedding for Hierarchical Graph Representation Learning

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 73970-73982 ◽  
Author(s):  
Jianming Lv ◽  
Jiajie Zhong ◽  
Jintao Liang ◽  
Zhenguo Yang
Keyword(s):  
Representation Learning ◽  
Ant Colony ◽  
Graph Representation ◽  
Network Embedding ◽  
Hierarchical Graph ◽  
Multi Level
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