.png){kind=icon}
Return
Graph Influence Network
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2022.3164474.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Due to the extraordinary abilities in extracting complex patterns, graph neural networks (GNNs) have demonstrated strong performances and received increasing attention in recent years. Despite their prominent achievements, recent GNNs do not pay enough attention to discriminate nodes when determining the information sources. Some of them select information sources from all or part of neighbors without distinction, and others merely distinguish nodes according to either graph structures or node features. To solve this problem, we propose the concept of the Influence Set and design a novel general GNN framework called the graph influence network (GINN), which discriminates neighbors by evaluating their influences on targets. In GINN, both topological structures and node features of the graph are utilized to find the most influential nodes. More specifically, given a target node, we first construct its influence set from the corresponding neighbors based on the local graph structure. To this aim, the pairwise influence comparison relations are extracted from the paths and a HodgeRank-based algorithm with analytical expression is devised to estimate the neighbors' structure influences. Then, after determining the influence set, the feature influences of nodes in the set are measured by the attention mechanism, and some task-irrelevant ones are further dislodged. Finally, only neighbor nodes that have high accessibility in structure and strong task relevance in features are chosen as the information sources. Extensive experiments on several datasets demonstrate that our model achieves state-of-the-art performances over several baselines and prove the effectiveness of discriminating neighbors in graph representation learning.
Keywords:
Task analysis
Representation learning
Graph neural networks
Feature extraction
Data mining
Convolution
Social networking (online)
Graph neural networks (GNNs)
graph representation learning
HodgeRank
influence ranking
influence set
neighbors discrimination
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
Organization
View more organizations
Cited Papers
A novel type of triterpenoid quinone methide pigment from the toadstool Russula flavida (Agaricales).png)
Tetrahedron
TetrahedronIF0
PREAI
ShareSaveShareSave
ShareSaveShareSaveDynamic patterning by the Drosophila pair-rule network reconciles long-germ and short-germ segmentationPLOS Biology
OAAI
PLOS BiologyIF0
OAAIShareSave View more