# GNN
[ICML '18] Graph Networks as Learnable Physics Engines for Inference and Control
Graph Networks as Learnable Physics Engines for Inference and Control 논문 리뷰
[ICLR '18] NerveNet: Learning Structured Policy with Graph Neural Networks
NerveNet
[WWW '22] Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
[WWW '22] Improving Graph Collaborative Filtering with Neighborhood-enriched Contrastive Learning
[SIGIR '22] Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
[SIGIR '22] Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation
[SIGIR '21] Self-supervised Graph Learning for Recommendation
[SIGIR '21] Self-supervised Graph Learning for Recommendation
Neural Graph Collaborative Filtering
강의자 : 투빅스 16기 이승주ContentsUnit 01 ㅣ IntroductionUnit 02 ㅣ MethodologyUnit 03 ㅣ ExperimentsUnit 04 ㅣ Conclusion and Future WorkTwo key components in
Graph, CNN, GNN, GCN, ST-GCN, Skeleton Dataset
graph imageGraph = Node + Edge비선형(non-linear) 자료구조.node: data를 의미edge: data간의 관계를 의미, 연결하는 선(line)으로 표현표현 방법adjancy list (인접 리스트)adjacency matrix (인접행
[Boostcamp AI Tech] Recommender System with Deep Learning (2)
[Boostcamp AI Tech] Recommender System with Deep Learning (2)
Learning Contextualized Knowledge Structures for Commonsense Reasoning: HGN
GNN is a Counter?: Revisiting GNN for Questions Answering 논문리뷰 (paper review)
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GreaseLM 논문 리뷰 (paper review)
GreaseLM: Graph Reasoning Enhanced Language Models for Question Answering 논문 리뷰 Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy L
The Graph Neural Network Model[Contents_4]
According to our experiments, the application of a trained GNN on a graph (test phase) is relatively fast even for large graphs. Formally, the complex
The Graph Neural Network Model[Contents_3]
In this section, an accurate analysis of the computational cost will be derived. The analysis will focus on three different GNN models: positional GNN
The Graph Neural Network Model[Contents_B,C]
Banach's fixed point theorem53 does not only ensure the existence and the uniqueness of the solution of (1) but it also suggests the following classic
15. Deep Generative Models for Graphs
14기 김상현 Graph Generation Given Graphs sampled from $p_{data}(G)$ Goal Learn the distribution $p_{model}(G)$ sample from $P_{model}(G)$ 실제 그래프 분포
14. Traditional Generative Models for Graphs
작성자: 14기 김상현그래프 생성 모델(Graph Generative Model)을 통해 실제 그래프와 유사한 그래프를 생성한다.그래프 생성을 공부하는 이유는 다음과 같다.InsightsPredictionsSimulationsAnomaly detection임의로 선택된