The intuitions behind Tree-structured Parzen estimator

TPE：一种基于贝叶斯推断的超参数调优方法。

HOW POWERFUL ARE GRAPH NEURAL NETWORKS?

This paper:

1. characterize how expressive different GNN variants are in learning to represent and distinguish between different graph structures
2. show that GNNs are at most as powerful as the WL test in distinguishing graph structures.
3. identify graph structures that cannot be distinguished by popular GNN variants, such as GCN (Kipf & Welling, 2017) and GraphSAGE (Hamilton et al., 2017a)
4. develop a simple neural architecture, Graph Isomorphism Network (GIN)

本文就提出了一种无监督的方法。核心思想：通过特定的游走方式进行采样，对于每个点都会生成 对应的序列。再将这些序列视为文本导入skip-gram模型，即可得 到每个节点的向量

Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs

本文讨论了graph的heterophily

A Three-Way Model for Collective Learning on Multi-Relational Data

2011

we propose the relational learning approach RESCAL which is based on a tensor factorization that is related to DEDICOM but does not exhibit the same constraints.

Convolutional 2D Knowledge Graph Embeddings

2018-7-4

1 Introduction

第一个利用CNN学习KGE的方法。

A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network

2018-3-13 ConvKB

Adaptive Convolution for Multi-Relational Learning

2019-6

1 Introduction

Learning with multi-relational data plays a pivotal role in many application domains, ranging from social networks or recommender systems to large-scale knowledge bases (KBs)

ConvR的思想是从relation中构造filter，然后卷积于subject embedding，最后投影，与object embedding做点积。

这样的做法就导致了ConvR的另一个优势，减少了参数的数量。

Interaction Embeddings for Prediction and Explanation in Knowledge Graphs

2019-3-12日发表

设计了一种有效的，浅层的KGE方法CrossE，能够让entity embedding和relation embedding进行更多的交互。

A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalization

2019-6-2s

1 Introduction

常用的KE模型，比如TransE，Complex，DISTMULT等模型，它们只捕获了三元实体之间的线性联系，没有捕获非线性的联系。

本论文的基础是在capsule networks（CapsNet）Dynamic routing between capsules的基础上，直接应用到knowledge graph triplet上。CPasNet原来是作用于图片上。

论文的理论是处在相同维度下的triplet，同一纬度下的embedding可以通过capsule（each capsule is a group of neurons） network捕获不同的变体。