GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction

5 Nov 2019Fenyu HuYanqiao ZhuShu WuWeiran HuangLiang WangTieniu Tan

Graph representation learning is of paramount importance for a variety of graph analytical tasks, ranging from node classification to community detection. Recently, graph convolutional networks (GCNs) have been successfully applied for graph representation learning... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Node Classification CiteSeer with Public Split: fixed 20 nodes per class AIR-GCN Accuracy 72.9% # 8
Node Classification Cora with Public Split: fixed 20 nodes per class AIR-GCN Accuracy 84.7% # 2
Node Classification PubMed with Public Split: fixed 20 nodes per class AIR-GCN Accuracy 80% # 6

Methods used in the Paper


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