투빅스 1415기 GNN 스터디입니다.
태그 목록
- 전체보기(17)
- CS224W(13)
- GNN(7)
- Graph Generation(2)
- Node Embedding(1)
- Subgraph(1)
- KnowledgeGraph(1)
- Community Detection(1)
- Simplified GCN(1)
- Knowledge Graph(1)
- BigCLAM(1)
- Kronecker Graph(1)
- Collective Classification(1)
- Node-level Features(1)
- GCN(1)
- Relational Classifiers(1)
- Belief Propagation(1)
- Link-level Features(1)
- GraphEmbedding(1)
- PageRank(1)
- GCPN(1)
- Cluster-GCN(1)
- Knowledge Graph Embeddings(1)
- Reasoning(1)
- Neighbor sampling(1)
- Erdos-Renyi Random Graph(1)
- Embedding(1)
- GraphRNN(1)
- Graph-Level Features(1)
- Motif(1)
- Iterative Classification(1)
전체보기 (17)CS224W(13)GNN(7)Graph Generation(2)Node Embedding(1)Subgraph(1)KnowledgeGraph(1)Community Detection(1)Simplified GCN(1)Knowledge Graph(1)BigCLAM(1)Kronecker Graph(1)Collective Classification(1)Node-level Features(1)GCN(1)Relational Classifiers(1)Belief Propagation(1)Link-level Features(1)GraphEmbedding(1)PageRank(1)GCPN(1)Cluster-GCN(1)Knowledge Graph Embeddings(1)Reasoning(1)Neighbor sampling(1)Erdos-Renyi Random Graph(1)Embedding(1)GraphRNN(1)Graph-Level Features(1)Motif(1)Iterative Classification(1)
15. Deep Generative Models for Graphs
14기 김상현GivenGraphs sampled from $p\_{data}(G)$GoalLearn the distribution $p\_{model}(G)$sample from $P\_{model}(G)$실제 그래프 분포 샘플들을 이용해 $P{model}(G)$를 학
2021년 10월 15일
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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
2021년 10월 15일
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2. Traditional Methods for Machine Learning in Graphs
Traditional Methods for Machine Learning in Graphs [작성자: 이성범]
2021년 7월 24일
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